// ==UserScript==
// @name         WPlace Image Processor
// @namespace    http://tampermonkey.net/
// @version      2025-09-16.1
// @description  Image processing and color management for WPlace AutoBot
// @author       Wbot
// @match        https://wplace.live/*
// @grant        none
// ==/UserScript==

/**
 * ImageProcessor - Handles image loading, processing, color conversion, and dithering for WPlace AutoBot
 * Extracted from Auto-Image.js for better modularity and reusabilit
 */
class ImageProcessor {
  constructor(imageSrc = null) {
    this.imageSrc = imageSrc;
    this.img = null;
    this.canvas = null;
    this.ctx = null;
    
    // Dithering buffers
    this._ditherWorkBuf = null;
    this._ditherEligibleBuf = null;
    
    // Color cache for performance optimization
    this._colorCache = new Map();
    this._labCache = new Map();
    this._hsvCache = new Map();
    this._hslCache = new Map();
    this._xyzCache = new Map();
    this._luvCache = new Map();
    this._yuvCache = new Map();
    this._oklabCache = new Map();
    this._lchCache = new Map();
    
    // Configuration constants
    this.TRANSPARENCY_THRESHOLD = 128;
    this.WHITE_THRESHOLD = 230;
    this.COLOR_CACHE_LIMIT = 15000;
  }

  /**
   * Load image from source
   * @returns {Promise<void>}
   */
  async load() {
    if (!this.imageSrc) {
      throw new Error('No image source provided');
    }

    return new Promise((resolve, reject) => {
      this.img = new Image();
      this.img.crossOrigin = 'anonymous';
      this.img.onload = () => {
        this.canvas = document.createElement('canvas');
        this.ctx = this.canvas.getContext('2d');
        this.canvas.width = this.img.width;
        this.canvas.height = this.img.height;
        this.ctx.drawImage(this.img, 0, 0);
        resolve();
      };
      this.img.onerror = reject;
      this.img.src = this.imageSrc;
    });
  }

  /**
   * Get image dimensions
   * @returns {{width: number, height: number}}
   */
  getDimensions() {
    if (!this.canvas) {
      throw new Error('Image not loaded. Call load() first.');
    }
    return {
      width: this.canvas.width,
      height: this.canvas.height,
    };
  }

  /**
   * Get pixel data from the image
   * @returns {Uint8ClampedArray} RGBA pixel data
   */
  getPixelData() {
    if (!this.ctx) {
      throw new Error('Image not loaded. Call load() first.');
    }
    return this.ctx.getImageData(0, 0, this.canvas.width, this.canvas.height).data;
  }

  /**
   * Resize image to new dimensions with specified resampling method
   * @param {number} newWidth - Target width
   * @param {number} newHeight - Target height
   * @param {string} method - Resampling method: 'nearest', 'bilinear', 'box', 'median', 'dominant'
   * @returns {Uint8ClampedArray} Resized image data
   */
  resize(newWidth, newHeight, method = 'nearest') {
    if (!this.canvas || !this.ctx) {
      throw new Error('Image not loaded. Call load() first.');
    }

    const tempCanvas = document.createElement('canvas');
    const tempCtx = tempCanvas.getContext('2d');

    tempCanvas.width = newWidth;
    tempCanvas.height = newHeight;

    // Use the specified resampling method
    const resampledCanvas = this.resampleImage(this.canvas, newWidth, newHeight, method);
    
    this.canvas.width = newWidth;
    this.canvas.height = newHeight;
    this.ctx.clearRect(0, 0, newWidth, newHeight);
    this.ctx.drawImage(resampledCanvas, 0, 0);

    return this.ctx.getImageData(0, 0, newWidth, newHeight).data;
  }

  /**
   * Resample image using specified method (adapted from wplace_helper)
   * @param {HTMLCanvasElement|HTMLImageElement} source - Source image/canvas
   * @param {number} dstW - Destination width
   * @param {number} dstH - Destination height
   * @param {string} method - Resampling method
   * @returns {HTMLCanvasElement} Resampled canvas
   */
  resampleImage(source, dstW, dstH, method = 'nearest') {
    const srcW = source.width;
    const srcH = source.height;
    const factor = srcW / dstW;
    const isInteger = Math.abs(factor - Math.round(factor)) < 1e-6;

    // For non-integer factors, only nearest and bilinear are available
    if (!isInteger) {
      if (method === 'bilinear') {
        return this.resampleBilinear(source, dstW, dstH);
      }
      return this.resampleNearest(source, dstW, dstH);
    }

    const intFactor = Math.max(1, Math.round(factor));
    
    switch (method) {
      case 'bilinear':
        return this.resampleBilinear(source, dstW, dstH);
      case 'box':
        return this.resampleBox(source, dstW, dstH, intFactor);
      case 'median':
        return this.resampleMedian(source, dstW, dstH, intFactor);
      case 'dominant':
        return this.resampleDominant(source, dstW, dstH, intFactor);
      case 'nearest':
      default:
        return this.resampleNearest(source, dstW, dstH);
    }
  }

  /**
   * Nearest neighbor resampling
   */
  resampleNearest(source, dstW, dstH) {
    const canvas = document.createElement('canvas');
    const ctx = canvas.getContext('2d', { willReadFrequently: true });
    canvas.width = dstW;
    canvas.height = dstH;
    ctx.imageSmoothingEnabled = false;
    ctx.clearRect(0, 0, dstW, dstH);
    ctx.drawImage(source, 0, 0, dstW, dstH);
    return canvas;
  }

  /**
   * Bilinear resampling
   */
  resampleBilinear(source, dstW, dstH) {
    const canvas = document.createElement('canvas');
    const ctx = canvas.getContext('2d', { willReadFrequently: true });
    canvas.width = dstW;
    canvas.height = dstH;
    ctx.imageSmoothingEnabled = true;
    ctx.clearRect(0, 0, dstW, dstH);
    ctx.drawImage(source, 0, 0, dstW, dstH);
    return canvas;
  }

  /**
   * Box filter resampling (average of pixels in block)
   */
  resampleBox(source, dstW, dstH, factor) {
    const srcCanvas = document.createElement('canvas');
    const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
    srcCanvas.width = source.width;
    srcCanvas.height = source.height;
    srcCtx.drawImage(source, 0, 0);
    const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
    
    const dstCanvas = document.createElement('canvas');
    const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
    dstCanvas.width = dstW;
    dstCanvas.height = dstH;
    const dstImageData = dstCtx.createImageData(dstW, dstH);
    const dstData = dstImageData.data;

    const sw = source.width;
    const sh = source.height;

    for (let y = 0; y < dstH; y++) {
      const sy0 = y * factor;
      const sy1 = Math.min(sh, sy0 + factor);
      for (let x = 0; x < dstW; x++) {
        const sx0 = x * factor;
        const sx1 = Math.min(sw, sx0 + factor);
        let r = 0, g = 0, b = 0, a = 0, cnt = 0;
        
        for (let yy = sy0; yy < sy1; yy++) {
          let p = (yy * sw + sx0) * 4;
          for (let xx = sx0; xx < sx1; xx++) {
            r += srcData[p];
            g += srcData[p + 1];
            b += srcData[p + 2];
            a += srcData[p + 3];
            cnt++;
            p += 4;
          }
        }
        
        const q = (y * dstW + x) * 4;
        dstData[q] = Math.min(255, Math.max(0, Math.round(r / cnt)));
        dstData[q + 1] = Math.min(255, Math.max(0, Math.round(g / cnt)));
        dstData[q + 2] = Math.min(255, Math.max(0, Math.round(b / cnt)));
        dstData[q + 3] = Math.min(255, Math.max(0, Math.round(a / cnt)));
      }
    }
    
    dstCtx.putImageData(dstImageData, 0, 0);
    return dstCanvas;
  }

  /**
   * Median filter resampling (median color in block)
   */
  resampleMedian(source, dstW, dstH, factor) {
    const srcCanvas = document.createElement('canvas');
    const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
    srcCanvas.width = source.width;
    srcCanvas.height = source.height;
    srcCtx.drawImage(source, 0, 0);
    const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
    
    const dstCanvas = document.createElement('canvas');
    const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
    dstCanvas.width = dstW;
    dstCanvas.height = dstH;
    const dstImageData = dstCtx.createImageData(dstW, dstH);
    const dstData = dstImageData.data;

    const sw = source.width;
    const sh = source.height;
    const rHist = new Uint32Array(16);
    const gHist = new Uint32Array(16);
    const bHist = new Uint32Array(16);
    const aHist = new Uint32Array(16);
    const binToByte = (bin) => (bin * 17) | 0;

    for (let y = 0; y < dstH; y++) {
      const sy0 = y * factor;
      const sy1 = Math.min(sh, sy0 + factor);
      for (let x = 0; x < dstW; x++) {
        rHist.fill(0); gHist.fill(0); bHist.fill(0); aHist.fill(0);
        const sx0 = x * factor;
        const sx1 = Math.min(sw, sx0 + factor);
        
        for (let yy = sy0; yy < sy1; yy++) {
          let p = (yy * sw + sx0) * 4;
          for (let xx = sx0; xx < sx1; xx++) {
            rHist[srcData[p] >> 4]++;
            gHist[srcData[p + 1] >> 4]++;
            bHist[srcData[p + 2] >> 4]++;
            aHist[srcData[p + 3] >> 4]++;
            p += 4;
          }
        }
        
        const half = ((sx1 - sx0) * (sy1 - sy0)) >> 1;
        const medianFrom = (hist) => {
          let acc = 0;
          for (let i = 0; i < 16; i++) {
            acc += hist[i];
            if (acc > half) return binToByte(i);
          }
          return binToByte(15);
        };
        
        const q = (y * dstW + x) * 4;
        dstData[q] = medianFrom(rHist);
        dstData[q + 1] = medianFrom(gHist);
        dstData[q + 2] = medianFrom(bHist);
        dstData[q + 3] = medianFrom(aHist);
      }
    }
    
    dstCtx.putImageData(dstImageData, 0, 0);
    return dstCanvas;
  }

  /**
   * Dominant color resampling (most frequent color in block)
   */
  resampleDominant(source, dstW, dstH, factor) {
    const srcCanvas = document.createElement('canvas');
    const srcCtx = srcCanvas.getContext('2d', { willReadFrequently: true });
    srcCanvas.width = source.width;
    srcCanvas.height = source.height;
    srcCtx.drawImage(source, 0, 0);
    const srcData = srcCtx.getImageData(0, 0, source.width, source.height).data;
    
    const dstCanvas = document.createElement('canvas');
    const dstCtx = dstCanvas.getContext('2d', { willReadFrequently: true });
    dstCanvas.width = dstW;
    dstCanvas.height = dstH;
    const dstImageData = dstCtx.createImageData(dstW, dstH);
    const dstData = dstImageData.data;

    const sw = source.width;
    const sh = source.height;
    const counts = new Uint32Array(4096);

    for (let y = 0; y < dstH; y++) {
      const sy0 = y * factor;
      const sy1 = Math.min(sh, sy0 + factor);
      for (let x = 0; x < dstW; x++) {
        counts.fill(0);
        const sx0 = x * factor;
        const sx1 = Math.min(sw, sx0 + factor);
        let bestIdx = 0, bestCount = -1;
        
        for (let yy = sy0; yy < sy1; yy++) {
          let p = (yy * sw + sx0) * 4;
          for (let xx = sx0; xx < sx1; xx++) {
            const r = srcData[p] >> 4;
            const g = srcData[p + 1] >> 4;
            const b = srcData[p + 2] >> 4;
            const idx = (r << 8) | (g << 4) | b;
            const c = (counts[idx] = (counts[idx] + 1) >>> 0);
            if (c > bestCount) {
              bestCount = c;
              bestIdx = idx;
            }
            p += 4;
          }
        }
        
        const r = ((bestIdx >> 8) & 0xF) * 17;
        const g = ((bestIdx >> 4) & 0xF) * 17;
        const b = (bestIdx & 0xF) * 17;
        const q = (y * dstW + x) * 4;
        dstData[q] = r;
        dstData[q + 1] = g;
        dstData[q + 2] = b;
        dstData[q + 3] = 255;
      }
    }
    
    dstCtx.putImageData(dstImageData, 0, 0);
    return dstCanvas;
  }

  /**
   * Generate preview with resizing
   * @param {number} width - Preview width
   * @param {number} height - Preview height
   * @returns {string} Base64 data URL of the preview
   */
  generatePreview(width, height) {
    if (!this.canvas) {
      throw new Error('Image not loaded. Call load() first.');
    }

    const previewCanvas = document.createElement('canvas');
    const previewCtx = previewCanvas.getContext('2d');
    previewCanvas.width = width;
    previewCanvas.height = height;

    previewCtx.imageSmoothingEnabled = false;
    previewCtx.drawImage(this.canvas, 0, 0, width, height);

    return previewCanvas.toDataURL();
  }

  // =============================================
  // COLOR PROCESSING METHODS
  // =============================================

  /**
   * Convert RGB to LAB color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} LAB values [L, a, b]
   * @private
   */
  _rgbToLab(r, g, b) {
    // Normalize RGB values
    let x = r / 255.0;
    let y = g / 255.0;
    let z = b / 255.0;

    // Apply gamma correction
    x = x > 0.04045 ? Math.pow((x + 0.055) / 1.055, 2.4) : x / 12.92;
    y = y > 0.04045 ? Math.pow((y + 0.055) / 1.055, 2.4) : y / 12.92;
    z = z > 0.04045 ? Math.pow((z + 0.055) / 1.055, 2.4) : z / 12.92;

    // Convert to XYZ using sRGB matrix
    let X = x * 0.4124564 + y * 0.3575761 + z * 0.1804375;
    let Y = x * 0.2126729 + y * 0.7151522 + z * 0.0721750;
    let Z = x * 0.0193339 + y * 0.1191920 + z * 0.9503041;

    // Normalize for D65 illuminant
    X /= 0.95047;
    Y /= 1.00000;
    Z /= 1.08883;

    // Convert to LAB
    X = X > 0.008856 ? Math.pow(X, 1/3) : (7.787 * X + 16/116);
    Y = Y > 0.008856 ? Math.pow(Y, 1/3) : (7.787 * Y + 16/116);
    Z = Z > 0.008856 ? Math.pow(Z, 1/3) : (7.787 * Z + 16/116);

    const L = 116 * Y - 16;
    const a = 500 * (X - Y);
    const b2 = 200 * (Y - Z);

    return [L, a, b2];
  }

  /**
   * Get LAB values with caching
   * @param {number} r - Red value
   * @param {number} g - Green value
   * @param {number} b - Blue value
   * @returns {number[]} Cached LAB values
   * @private
   */
  _getLab(r, g, b) {
    const key = (r << 16) | (g << 8) | b;
    let v = this._labCache.get(key);
    if (!v) {
      v = this._rgbToLab(r, g, b);
      this._labCache.set(key, v);
    }
    return v;
  }

  /**
   * Convert RGB to HSV color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} HSV values [H (0-360), S (0-1), V (0-1)]
   * @private
   */
  _rgbToHsv(r, g, b) {
    r /= 255;
    g /= 255;
    b /= 255;

    const max = Math.max(r, g, b);
    const min = Math.min(r, g, b);
    const delta = max - min;

    let h = 0;
    const s = max === 0 ? 0 : delta / max;
    const v = max;

    if (delta !== 0) {
      if (max === r) {
        h = ((g - b) / delta) % 6;
      } else if (max === g) {
        h = (b - r) / delta + 2;
      } else {
        h = (r - g) / delta + 4;
      }
      h *= 60;
      if (h < 0) h += 360;
    }

    return [h, s, v];
  }

  /**
   * Convert HSV to RGB color space
   * @param {number} h - Hue value (0-360)
   * @param {number} s - Saturation value (0-1)
   * @param {number} v - Value/Brightness (0-1)
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _hsvToRgb(h, s, v) {
    const c = v * s;
    const x = c * (1 - Math.abs(((h / 60) % 2) - 1));
    const m = v - c;

    let r = 0, g = 0, b = 0;

    if (h >= 0 && h < 60) {
      r = c; g = x; b = 0;
    } else if (h >= 60 && h < 120) {
      r = x; g = c; b = 0;
    } else if (h >= 120 && h < 180) {
      r = 0; g = c; b = x;
    } else if (h >= 180 && h < 240) {
      r = 0; g = x; b = c;
    } else if (h >= 240 && h < 300) {
      r = x; g = 0; b = c;
    } else if (h >= 300 && h < 360) {
      r = c; g = 0; b = x;
    }

    return [
      Math.round((r + m) * 255),
      Math.round((g + m) * 255),
      Math.round((b + m) * 255)
    ];
  }

  /**
   * Convert RGB to HSL color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} HSL values [H (0-360), S (0-1), L (0-1)]
   * @private
   */
  _rgbToHsl(r, g, b) {
    r /= 255;
    g /= 255;
    b /= 255;

    const max = Math.max(r, g, b);
    const min = Math.min(r, g, b);
    const delta = max - min;

    let h = 0;
    let s = 0;
    const l = (max + min) / 2;

    if (delta !== 0) {
      s = l > 0.5 ? delta / (2 - max - min) : delta / (max + min);

      if (max === r) {
        h = ((g - b) / delta) % 6;
      } else if (max === g) {
        h = (b - r) / delta + 2;
      } else {
        h = (r - g) / delta + 4;
      }
      h *= 60;
      if (h < 0) h += 360;
    }

    return [h, s, l];
  }

  /**
   * Convert HSL to RGB color space
   * @param {number} h - Hue value (0-360)
   * @param {number} s - Saturation value (0-1)
   * @param {number} l - Lightness value (0-1)
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _hslToRgb(h, s, l) {
    const c = (1 - Math.abs(2 * l - 1)) * s;
    const x = c * (1 - Math.abs(((h / 60) % 2) - 1));
    const m = l - c / 2;

    let r = 0, g = 0, b = 0;

    if (h >= 0 && h < 60) {
      r = c; g = x; b = 0;
    } else if (h >= 60 && h < 120) {
      r = x; g = c; b = 0;
    } else if (h >= 120 && h < 180) {
      r = 0; g = c; b = x;
    } else if (h >= 180 && h < 240) {
      r = 0; g = x; b = c;
    } else if (h >= 240 && h < 300) {
      r = x; g = 0; b = c;
    } else if (h >= 300 && h < 360) {
      r = c; g = 0; b = x;
    }

    return [
      Math.round((r + m) * 255),
      Math.round((g + m) * 255),
      Math.round((b + m) * 255)
    ];
  }

  /**
   * Convert RGB to XYZ color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} XYZ values [X, Y, Z]
   * @private
   */
  _rgbToXyz(r, g, b) {
    // Normalize RGB values
    let x = r / 255.0;
    let y = g / 255.0;
    let z = b / 255.0;

    // Apply gamma correction
    x = x > 0.04045 ? Math.pow((x + 0.055) / 1.055, 2.4) : x / 12.92;
    y = y > 0.04045 ? Math.pow((y + 0.055) / 1.055, 2.4) : y / 12.92;
    z = z > 0.04045 ? Math.pow((z + 0.055) / 1.055, 2.4) : z / 12.92;

    // Convert to XYZ using sRGB matrix
    const X = x * 0.4124564 + y * 0.3575761 + z * 0.1804375;
    const Y = x * 0.2126729 + y * 0.7151522 + z * 0.0721750;
    const Z = x * 0.0193339 + y * 0.1191920 + z * 0.9503041;

    return [X, Y, Z];
  }

  /**
   * Convert XYZ to RGB color space
   * @param {number} x - X value
   * @param {number} y - Y value
   * @param {number} z - Z value
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _xyzToRgb(x, y, z) {
    // Convert XYZ to linear RGB
    let r = x * 3.2404542 + y * -1.5371385 + z * -0.4985314;
    let g = x * -0.9692660 + y * 1.8760108 + z * 0.0415560;
    let b = x * 0.0556434 + y * -0.2040259 + z * 1.0572252;

    // Apply gamma correction
    r = r > 0.0031308 ? 1.055 * Math.pow(r, 1.0 / 2.4) - 0.055 : 12.92 * r;
    g = g > 0.0031308 ? 1.055 * Math.pow(g, 1.0 / 2.4) - 0.055 : 12.92 * g;
    b = b > 0.0031308 ? 1.055 * Math.pow(b, 1.0 / 2.4) - 0.055 : 12.92 * b;

    // Clamp and convert to 0-255 range
    return [
      Math.round(Math.max(0, Math.min(1, r)) * 255),
      Math.round(Math.max(0, Math.min(1, g)) * 255),
      Math.round(Math.max(0, Math.min(1, b)) * 255)
    ];
  }

  /**
   * Convert RGB to LUV color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} LUV values [L, u, v]
   * @private
   */
  _rgbToLuv(r, g, b) {
    const [X, Y, Z] = this._rgbToXyz(r, g, b);

    // Reference white D65
    const Xn = 0.95047;
    const Yn = 1.00000;
    const Zn = 1.08883;

    const yr = Y / Yn;
    const L = yr > 0.008856 ? 116 * Math.pow(yr, 1/3) - 16 : 903.3 * yr;

    const denom = X + 15 * Y + 3 * Z;
    const denomN = Xn + 15 * Yn + 3 * Zn;
    
    const up = denom === 0 ? 0 : (4 * X) / denom;
    const vp = denom === 0 ? 0 : (9 * Y) / denom;
    const upN = (4 * Xn) / denomN;
    const vpN = (9 * Yn) / denomN;

    const u = 13 * L * (up - upN);
    const v = 13 * L * (vp - vpN);

    return [L, u, v];
  }

  /**
   * Convert LUV to RGB color space
   * @param {number} l - L value
   * @param {number} u - u value
   * @param {number} v - v value
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _luvToRgb(l, u, v) {
    // Reference white D65
    const Xn = 0.95047;
    const Yn = 1.00000;
    const Zn = 1.08883;

    const upN = (4 * Xn) / (Xn + 15 * Yn + 3 * Zn);
    const vpN = (9 * Yn) / (Xn + 15 * Yn + 3 * Zn);

    const Y = l > 8 ? Yn * Math.pow((l + 16) / 116, 3) : Yn * l / 903.3;

    const up = u / (13 * l) + upN;
    const vp = v / (13 * l) + vpN;

    const X = Y * 9 * up / (4 * vp);
    const Z = Y * (12 - 3 * up - 20 * vp) / (4 * vp);

    return this._xyzToRgb(X, Y, Z);
  }

  /**
   * Convert RGB to YUV color space
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} YUV values [Y, U, V]
   * @private
   */
  _rgbToYuv(r, g, b) {
    const Y = 0.299 * r + 0.587 * g + 0.114 * b;
    const U = -0.14713 * r - 0.28886 * g + 0.436 * b;
    const V = 0.615 * r - 0.51499 * g - 0.10001 * b;

    return [Y, U, V];
  }

  /**
   * Convert YUV to RGB color space
   * @param {number} y - Y (luminance) value
   * @param {number} u - U (chrominance) value
   * @param {number} v - V (chrominance) value
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _yuvToRgb(y, u, v) {
    const r = y + 1.13983 * v;
    const g = y - 0.39465 * u - 0.58060 * v;
    const b = y + 2.03211 * u;

    return [
      Math.round(Math.max(0, Math.min(255, r))),
      Math.round(Math.max(0, Math.min(255, g))),
      Math.round(Math.max(0, Math.min(255, b)))
    ];
  }

  /**
   * Convert RGB to Oklab color space (improved LAB)
   * @param {number} r - Red value (0-255)
   * @param {number} g - Green value (0-255)
   * @param {number} b - Blue value (0-255)
   * @returns {number[]} Oklab values [L, a, b]
   * @private
   */
  _rgbToOklab(r, g, b) {
    // Normalize RGB
    r /= 255;
    g /= 255;
    b /= 255;

    // Apply gamma correction
    r = r > 0.04045 ? Math.pow((r + 0.055) / 1.055, 2.4) : r / 12.92;
    g = g > 0.04045 ? Math.pow((g + 0.055) / 1.055, 2.4) : g / 12.92;
    b = b > 0.04045 ? Math.pow((b + 0.055) / 1.055, 2.4) : b / 12.92;

    // Linear RGB to Oklab
    const l = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b;
    const m = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b;
    const s = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b;

    const l_ = Math.cbrt(l);
    const m_ = Math.cbrt(m);
    const s_ = Math.cbrt(s);

    return [
      0.2104542553 * l_ + 0.7936177850 * m_ - 0.0040720468 * s_,
      1.9779984951 * l_ - 2.4285922050 * m_ + 0.4505937099 * s_,
      0.0259040371 * l_ + 0.7827717662 * m_ - 0.8086757660 * s_
    ];
  }

  /**
   * Convert Oklab to RGB color space
   * @param {number} l - L value
   * @param {number} a - a value
   * @param {number} b - b value
   * @returns {number[]} RGB values [R, G, B] (0-255)
   * @private
   */
  _oklabToRgb(l, a, b) {
    const l_ = l + 0.3963377774 * a + 0.2158037573 * b;
    const m_ = l - 0.1055613458 * a - 0.0638541728 * b;
    const s_ = l - 0.0894841775 * a - 1.2914855480 * b;

    const l3 = l_ * l_ * l_;
    const m3 = m_ * m_ * m_;
    const s3 = s_ * s_ * s_;

    let r = 4.0767416621 * l3 - 3.3077115913 * m3 + 0.2309699292 * s3;
    let g = -1.2684380046 * l3 + 2.6097574011 * m3 - 0.3413193965 * s3;
    let b2 = -0.0041960863 * l3 - 0.7034186147 * m3 + 1.7076147010 * s3;

    // Apply gamma correction
    r = r > 0.0031308 ? 1.055 * Math.pow(r, 1.0 / 2.4) - 0.055 : 12.92 * r;
    g = g > 0.0031308 ? 1.055 * Math.pow(g, 1.0 / 2.4) - 0.055 : 12.92 * g;
    b2 = b2 > 0.0031308 ? 1.055 * Math.pow(b2, 1.0 / 2.4) - 0.055 : 12.92 * b2;

    return [
      Math.round(Math.max(0, Math.min(1, r)) * 255),
      Math.round(Math.max(0, Math.min(1, g)) * 255),
      Math.round(Math.max(0, Math.min(1, b2)) * 255)
    ];
  }

  /**
   * Convert LAB to LCH color space (cylindrical representation)
   * @param {number} l - L value
   * @param {number} a - a value
   * @param {number} b - b value
   * @returns {number[]} LCH values [L, C, H]
   * @private
   */
  _labToLch(l, a, b) {
    const c = Math.sqrt(a * a + b * b);
    let h = Math.atan2(b, a) * 180 / Math.PI;
    if (h < 0) h += 360;

    return [l, c, h];
  }

  /**
   * Convert LCH to LAB color space
   * @param {number} l - L value
   * @param {number} c - C (chroma) value
   * @param {number} h - H (hue) value (0-360)
   * @returns {number[]} LAB values [L, a, b]
   * @private
   */
  _lchToLab(l, c, h) {
    const hRad = h * Math.PI / 180;
    const a = c * Math.cos(hRad);
    const b = c * Math.sin(hRad);

    return [l, a, b];
  }

  /**
   * Find closest color in palette using specified algorithm
   * @param {number} r - Red value
   * @param {number} g - Green value
   * @param {number} b - Blue value
   * @param {Array} palette - Array of RGB arrays [[r,g,b], ...]
   * @param {string} algorithm - 'legacy' or 'lab'
   * @param {Object} options - Additional options for color matching
   * @returns {number[]} Closest RGB color [r, g, b]
   */
  findClosestPaletteColor(r, g, b, palette, algorithm = 'lab', options = {}) {
    if (!palette || palette.length === 0) {
      return [0, 0, 0];
    }

    const { enableChromaPenalty = false, chromaPenaltyWeight = 0.15 } = options;

    if (algorithm === 'legacy') {
      let menorDist = Infinity;
      let cor = [0, 0, 0];
      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const rmean = (pr + r) / 2;
        const rdiff = pr - r;
        const gdiff = pg - g;
        const bdiff = pb - b;
        const dist = Math.sqrt(
          (((512 + rmean) * rdiff * rdiff) >> 8) +
          4 * gdiff * gdiff +
          (((767 - rmean) * bdiff * bdiff) >> 8)
        );
        if (dist < menorDist) {
          menorDist = dist;
          cor = [pr, pg, pb];
        }
      }
      return cor;
    }

    // HSV algorithm
    if (algorithm === 'hsv') {
      const [ht, st, vt] = this._rgbToHsv(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [hp, sp, vp] = this._rgbToHsv(pr, pg, pb);
        const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)) / 360; // Normalize hue distance
        const ds = st - sp;
        const dv = vt - vp;
        const dist = dh * dh + ds * ds + dv * dv;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // HSL algorithm
    if (algorithm === 'hsl') {
      const [ht, st, lt] = this._rgbToHsl(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [hp, sp, lp] = this._rgbToHsl(pr, pg, pb);
        const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)) / 360; // Normalize hue distance
        const ds = st - sp;
        const dl = lt - lp;
        const dist = dh * dh + ds * ds + dl * dl;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // XYZ algorithm
    if (algorithm === 'xyz') {
      const [xt, yt, zt] = this._rgbToXyz(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [xp, yp, zp] = this._rgbToXyz(pr, pg, pb);
        const dx = xt - xp;
        const dy = yt - yp;
        const dz = zt - zp;
        const dist = dx * dx + dy * dy + dz * dz;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // LUV algorithm
    if (algorithm === 'luv') {
      const [lt, ut, vt] = this._rgbToLuv(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [lp, up, vp] = this._rgbToLuv(pr, pg, pb);
        const dl = lt - lp;
        const du = ut - up;
        const dv = vt - vp;
        const dist = dl * dl + du * du + dv * dv;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // YUV algorithm
    if (algorithm === 'yuv') {
      const [yt, ut, vt] = this._rgbToYuv(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [yp, up, vp] = this._rgbToYuv(pr, pg, pb);
        const dy = yt - yp;
        const du = ut - up;
        const dv = vt - vp;
        const dist = dy * dy + du * du + dv * dv;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // Oklab algorithm
    if (algorithm === 'oklab') {
      const [lt, at, bt] = this._rgbToOklab(r, g, b);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [lp, ap, bp] = this._rgbToOklab(pr, pg, pb);
        const dl = lt - lp;
        const da = at - ap;
        const db = bt - bp;
        const dist = dl * dl + da * da + db * db;

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // LCH algorithm
    if (algorithm === 'lch') {
      const [Lt, at, bt] = this._getLab(r, g, b);
      const [lt, ct, ht] = this._labToLch(Lt, at, bt);
      let best = null;
      let bestDist = Infinity;

      for (let i = 0; i < palette.length; i++) {
        const [pr, pg, pb] = palette[i];
        const [Lp, ap, bp] = this._getLab(pr, pg, pb);
        const [lp, cp, hp] = this._labToLch(Lp, ap, bp);
        const dl = lt - lp;
        const dc = ct - cp;
        const dh = Math.min(Math.abs(ht - hp), 360 - Math.abs(ht - hp)); // Circular hue distance
        const dist = dl * dl + dc * dc + (dh * dh) / 360; // Normalize hue

        if (dist < bestDist) {
          bestDist = dist;
          best = palette[i];
          if (bestDist === 0) break;
        }
      }
      return best || [0, 0, 0];
    }

    // LAB algorithm (default)
    const [Lt, at, bt] = this._getLab(r, g, b);
    const targetChroma = Math.sqrt(at * at + bt * bt);
    let best = null;
    let bestDist = Infinity;

    for (let i = 0; i < palette.length; i++) {
      const [pr, pg, pb] = palette[i];
      const [Lp, ap, bp] = this._getLab(pr, pg, pb);
      const dL = Lt - Lp;
      const da = at - ap;
      const db = bt - bp;
      let dist = dL * dL + da * da + db * db;

      if (enableChromaPenalty && targetChroma > 20) {
        const candChroma = Math.sqrt(ap * ap + bp * bp);
        if (candChroma < targetChroma) {
          const chromaDiff = targetChroma - candChroma;
          dist += chromaDiff * chromaDiff * chromaPenaltyWeight;
        }
      }

      if (dist < bestDist) {
        bestDist = dist;
        best = palette[i];
        if (bestDist === 0) break;
      }
    }

    return best || [0, 0, 0];
  }

  /**
   * Check if a pixel is considered white based on threshold
   * @param {number} r - Red value
   * @param {number} g - Green value
   * @param {number} b - Blue value
   * @param {number} threshold - White threshold (default: 230)
   * @returns {boolean} True if pixel is white
   */
  isWhitePixel(r, g, b, threshold = this.WHITE_THRESHOLD) {
    return r >= threshold && g >= threshold && b >= threshold;
  }

  /**
   * Resolve target RGB to closest available color with caching
   * @param {number[]} targetRgb - Target RGB array [r, g, b]
   * @param {Array} availableColors - Available colors with id and rgb properties
   * @param {Object} options - Matching options
   * @returns {Object} {id: number|null, rgb: number[]}
   */
  resolveColor(targetRgb, availableColors, options = {}) {
    const {
      exactMatch = false,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15,
      whiteThreshold = this.WHITE_THRESHOLD
    } = options;

    if (!availableColors || availableColors.length === 0) {
      return { id: null, rgb: targetRgb };
    }

    const cacheKey = `${targetRgb[0]},${targetRgb[1]},${targetRgb[2]}|${algorithm}|${enableChromaPenalty ? 'c' : 'nc'}|${chromaPenaltyWeight}|${exactMatch ? 'exact' : 'closest'}`;

    if (this._colorCache.has(cacheKey)) {
      return this._colorCache.get(cacheKey);
    }

    // Check for exact match
    if (exactMatch) {
      const match = availableColors.find(
        (c) => c.rgb[0] === targetRgb[0] && c.rgb[1] === targetRgb[1] && c.rgb[2] === targetRgb[2]
      );
      const result = match ? { id: match.id, rgb: [...match.rgb] } : { id: null, rgb: targetRgb };
      this._colorCache.set(cacheKey, result);
      return result;
    }

    // Check for white pixel matching
    if (
      targetRgb[0] >= whiteThreshold &&
      targetRgb[1] >= whiteThreshold &&
      targetRgb[2] >= whiteThreshold
    ) {
      const whiteEntry = availableColors.find(
        (c) => c.rgb[0] >= whiteThreshold && c.rgb[1] >= whiteThreshold && c.rgb[2] >= whiteThreshold
      );
      if (whiteEntry) {
        const result = { id: whiteEntry.id, rgb: [...whiteEntry.rgb] };
        this._colorCache.set(cacheKey, result);
        return result;
      }
    }

    // Find nearest color
    let bestId = availableColors[0].id;
    let bestRgb = [...availableColors[0].rgb];
    let bestScore = Infinity;

    if (algorithm === 'legacy') {
      for (let i = 0; i < availableColors.length; i++) {
        const c = availableColors[i];
        const [r, g, b] = c.rgb;
        const rmean = (r + targetRgb[0]) / 2;
        const rdiff = r - targetRgb[0];
        const gdiff = g - targetRgb[1];
        const bdiff = b - targetRgb[2];
        const dist = Math.sqrt(
          (((512 + rmean) * rdiff * rdiff) >> 8) +
          4 * gdiff * gdiff +
          (((767 - rmean) * bdiff * bdiff) >> 8)
        );
        if (dist < bestScore) {
          bestScore = dist;
          bestId = c.id;
          bestRgb = [...c.rgb];
          if (dist === 0) break;
        }
      }
    } else {
      const [Lt, at, bt] = this._getLab(targetRgb[0], targetRgb[1], targetRgb[2]);
      const targetChroma = Math.sqrt(at * at + bt * bt);
      const penaltyWeight = enableChromaPenalty ? chromaPenaltyWeight : 0;

      for (let i = 0; i < availableColors.length; i++) {
        const c = availableColors[i];
        const [r, g, b] = c.rgb;
        const [L2, a2, b2] = this._getLab(r, g, b);
        const dL = Lt - L2;
        const da = at - a2;
        const db = bt - b2;
        let dist = dL * dL + da * da + db * db;

        if (penaltyWeight > 0 && targetChroma > 20) {
          const candChroma = Math.sqrt(a2 * a2 + b2 * b2);
          if (candChroma < targetChroma) {
            const cd = targetChroma - candChroma;
            dist += cd * cd * penaltyWeight;
          }
        }

        if (dist < bestScore) {
          bestScore = dist;
          bestId = c.id;
          bestRgb = [...c.rgb];
          if (dist === 0) break;
        }
      }
    }

    const result = { id: bestId, rgb: bestRgb };
    this._colorCache.set(cacheKey, result);

    // Limit cache size
    if (this._colorCache.size > this.COLOR_CACHE_LIMIT) {
      const firstKey = this._colorCache.keys().next().value;
      this._colorCache.delete(firstKey);
    }

    return result;
  }

  // =============================================
  // DITHERING METHODS (Floyd-Steinberg)
  // =============================================

  /**
   * Ensure dithering buffers are properly sized
   * @param {number} n - Number of pixels
   * @returns {Object} {work: Float32Array, eligible: Uint8Array}
   * @private
   */
  _ensureDitherBuffers(n) {
    if (!this._ditherWorkBuf || this._ditherWorkBuf.length !== n * 3) {
      this._ditherWorkBuf = new Float32Array(n * 3);
    }
    if (!this._ditherEligibleBuf || this._ditherEligibleBuf.length !== n) {
      this._ditherEligibleBuf = new Uint8Array(n);
    }
    return { work: this._ditherWorkBuf, eligible: this._ditherEligibleBuf };
  }

  /**
   * Apply Floyd-Steinberg dithering to image data
   * @param {Uint8ClampedArray} imageData - RGBA image data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {Array} palette - Color palette for dithering
   * @param {Object} options - Dithering options
   * @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
   */
  applyFloydSteinbergDithering(imageData, width, height, palette, options = {}) {
    const {
      paintTransparentPixels = false,
      paintWhitePixels = true,
      transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
      whiteThreshold = this.WHITE_THRESHOLD,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15,
      mask = null
    } = options;

    const data = new Uint8ClampedArray(imageData);
    const n = width * height;
    const { work, eligible } = this._ensureDitherBuffers(n);
    let totalValidPixels = 0;

    // Initialize working buffers
    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const idx = y * width + x;
        const i4 = idx * 4;
        const r = data[i4];
        const g = data[i4 + 1];
        const b = data[i4 + 2];
        const a = data[i4 + 3];
        const masked = mask && mask[idx];

        const isEligible =
          !masked &&
          (paintTransparentPixels || a >= transparencyThreshold) &&
          (paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));

        eligible[idx] = isEligible ? 1 : 0;
        work[idx * 3] = r;
        work[idx * 3 + 1] = g;
        work[idx * 3 + 2] = b;

        if (!isEligible) {
          data[i4 + 3] = 0; // Make ineligible pixels transparent
        }
      }
    }

    // Error diffusion function
    const diffuse = (nx, ny, er, eg, eb, factor) => {
      if (nx < 0 || nx >= width || ny < 0 || ny >= height) return;
      const nidx = ny * width + nx;
      if (!eligible[nidx]) return;
      const base = nidx * 3;
      work[base] = Math.min(255, Math.max(0, work[base] + er * factor));
      work[base + 1] = Math.min(255, Math.max(0, work[base + 1] + eg * factor));
      work[base + 2] = Math.min(255, Math.max(0, work[base + 2] + eb * factor));
    };

    // Apply Floyd-Steinberg dithering
      for (let y = 0; y < height; y++) {
        for (let x = 0; x < width; x++) {
          const idx = y * width + x;
          if (!eligible[idx]) continue;

          const base = idx * 3;
          const r0 = work[base];
          const g0 = work[base + 1];
          const b0 = work[base + 2];
          const i4 = idx * 4;
          const a = data[i4 + 3];

          // If painting transparent pixels AND this pixel is transparent, keep it transparent
          if (paintTransparentPixels && a < transparencyThreshold) {
            data[i4] = 0;
            data[i4 + 1] = 0;
            data[i4 + 2] = 0;
            data[i4 + 3] = 0;
            totalValidPixels++;
            continue;
          }

          const [nr, ng, nb] = this.findClosestPaletteColor(
            r0, g0, b0, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
          );

          data[i4] = nr;
          data[i4 + 1] = ng;
          data[i4 + 2] = nb;
          data[i4 + 3] = 255;
          totalValidPixels++;

          // Calculate and diffuse error
          const er = r0 - nr;
          const eg = g0 - ng;
          const eb = b0 - nb;

          diffuse(x + 1, y, er, eg, eb, 7 / 16);
          diffuse(x - 1, y + 1, er, eg, eb, 3 / 16);
          diffuse(x, y + 1, er, eg, eb, 5 / 16);
          diffuse(x + 1, y + 1, er, eg, eb, 1 / 16);
        }
      }    return { data, totalValidPixels };
  }

  /**
   * Apply simple color quantization without dithering
   * @param {Uint8ClampedArray} imageData - RGBA image data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {Array} palette - Color palette for quantization
   * @param {Object} options - Quantization options
   * @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
   */
  applySimpleQuantization(imageData, width, height, palette, options = {}) {
    const {
      paintTransparentPixels = false,
      paintWhitePixels = true,
      transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
      whiteThreshold = this.WHITE_THRESHOLD,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15
    } = options;

    const data = new Uint8ClampedArray(imageData);
    let totalValidPixels = 0;

    for (let i = 0; i < data.length; i += 4) {
      const r = data[i];
      const g = data[i + 1];
      const b = data[i + 2];
      const a = data[i + 3];

      const isTransparent = a < transparencyThreshold;

      const isEligible =
        (paintTransparentPixels || !isTransparent) &&
        (paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));

      if (!isEligible) {
        data[i + 3] = 0; // Make ineligible pixels transparent
        continue;
      }

      // If painting transparent pixels AND this pixel is transparent, keep it transparent
      if (paintTransparentPixels && isTransparent) {
        data[i] = 0;
        data[i + 1] = 0;
        data[i + 2] = 0;
        data[i + 3] = 0;
        totalValidPixels++;
        continue;
      }

      const [nr, ng, nb] = this.findClosestPaletteColor(
        r, g, b, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
      );

      data[i] = nr;
      data[i + 1] = ng;
      data[i + 2] = nb;
      data[i + 3] = 255;
      totalValidPixels++;
    }

    return { data, totalValidPixels };
  }

  // =============================================
  // UTILITY METHODS
  // =============================================

  /**
   * Extract available colors from DOM elements
   * @returns {Array|null} Array of color objects with id and rgb properties
   */
  extractAvailableColors() {
    const colorElements = document.querySelectorAll('.color-option, [data-color-id]');
    if (colorElements.length === 0) return null;

    const colors = [];
    colorElements.forEach((element) => {
      const colorId = element.dataset.colorId || element.getAttribute('data-color-id');
      const computedStyle = window.getComputedStyle(element);
      const backgroundColor = computedStyle.backgroundColor;

      if (backgroundColor && colorId) {
        const rgbMatch = backgroundColor.match(/rgb\((\d+),\s*(\d+),\s*(\d+)\)/);
        if (rgbMatch) {
          colors.push({
            id: parseInt(colorId),
            rgb: [parseInt(rgbMatch[1]), parseInt(rgbMatch[2]), parseInt(rgbMatch[3])]
          });
        }
      }
    });

    return colors.length > 0 ? colors : null;
  }

  /**
   * Create file uploader for images
   * @returns {Promise<string>} Promise resolving to image data URL
   */
  createImageUploader() {
    return new Promise((resolve) => {
      const input = document.createElement('input');
      input.type = 'file';
      input.accept = 'image/png,image/jpeg';
      input.onchange = () => {
        if (input.files && input.files[0]) {
          const fr = new FileReader();
          fr.onload = () => resolve(fr.result);
          fr.readAsDataURL(input.files[0]);
        } else {
          resolve(null);
        }
      };
      input.click();
    });
  }

  /**
   * Extract available colors from DOM palette
   * @param {Object} colorMap - The color map object (optional, will use window.CONFIG if not provided)
   * @returns {Array|null} Array of available colors or null if none found
   */
  extractAvailableColors(colorMap = null) {
    const colorElements = document.querySelectorAll('.tooltip button[id^="color-"]');
    if (colorElements.length === 0) {
      console.log('❌ No color elements found on page');
      return null;
    }
    
    // Use provided colorMap or fallback to window.CONFIG
    const effectiveColorMap = colorMap || (window.CONFIG?.COLOR_MAP);
    
    // Separate available and unavailable colors
    const availableColors = [];
    const unavailableColors = [];

    Array.from(colorElements).forEach((el) => {
      const id = Number.parseInt(el.id.replace('color-', ''));
      if (id === 0) return; // Skip transparent color

      const rgbStr = el.style.backgroundColor.match(/\d+/g);
      if (!rgbStr || rgbStr.length < 3) {
        console.warn(`Skipping color element ${el.id} — cannot parse RGB`);
        return;
      }
      const rgb = rgbStr.map(Number);

      // Find color name from COLOR_MAP
      let name = `Unknown Color ${id}`;
      if (effectiveColorMap) {
        const colorInfo = Object.values(effectiveColorMap).find((color) => color.id === id);
        name = colorInfo ? colorInfo.name : name;
      }

      const colorData = { id, name, rgb };

      // Check if color is available (no SVG overlay means available)
      if (!el.querySelector('svg')) {
        availableColors.push(colorData);
      } else {
        unavailableColors.push(colorData);
      }
    });

    // Console log detailed color information
    console.log('=== CAPTURED COLORS STATUS ===');
    console.log(`Total available colors: ${availableColors.length}`);
    console.log(`Total unavailable colors: ${unavailableColors.length}`);
    console.log(`Total colors scanned: ${availableColors.length + unavailableColors.length}`);

    if (availableColors.length > 0) {
      console.log('\n--- AVAILABLE COLORS ---');
      availableColors.forEach((color, index) => {
        console.log(
          `${index + 1
          }. ID: ${color.id}, Name: "${color.name}", RGB: (${color.rgb[0]}, ${color.rgb[1]}, ${color.rgb[2]})`
        );
      });
    }

    if (unavailableColors.length > 0) {
      console.log('\n--- UNAVAILABLE COLORS ---');
      unavailableColors.forEach((color, index) => {
        console.log(
          `${index + 1
          }. ID: ${color.id}, Name: "${color.name}", RGB: (${color.rgb[0]}, ${color.rgb[1]}, ${color.rgb[2]}) [LOCKED]`
        );
      });
    }

    console.log('=== END COLOR STATUS ===');

    return availableColors;
  }

  /**
   * Clear all caches to free memory
   */
  clearCaches() {
    this._colorCache.clear();
    this._labCache.clear();
    this._hsvCache?.clear();
    this._hslCache?.clear();
    this._xyzCache?.clear();
    this._luvCache?.clear();
    this._yuvCache?.clear();
    this._oklabCache?.clear();
    this._lchCache?.clear();
    this._ditherWorkBuf = null;
    this._ditherEligibleBuf = null;
  }

  /**
   * Clean up resources
   */
  cleanup() {
    this.clearCaches();
    if (this.canvas) {
      this.canvas.width = 0;
      this.canvas.height = 0;
    }
    this.img = null;
    this.canvas = null;
    this.ctx = null;
  }
  
  /**
   * Apply pre-blur to canvas (before downscaling)
   * @param {HTMLCanvasElement} canvas - Source canvas
   * @param {string} mode - Blur mode: 'none', 'box', 'gaussian', 'kuwahara'
   * @param {number} radius - Blur radius
   * @returns {HTMLCanvasElement} Blurred canvas
   */
  applyPreBlur(canvas, mode, radius) {
    const r = Math.max(0, Math.floor(radius));
    if (!mode || mode === 'none' || r <= 0) return canvas;
    
    if (mode === 'box') return this.boxBlur(canvas, r);
    if (mode === 'gaussian') return this.gaussianBlur(canvas, r);
    if (mode === 'kuwahara') return this.kuwaharaFilter(canvas, r);
    
    return canvas;
  }
  
  /**
   * Box blur filter
   * @param {HTMLCanvasElement} canvas - Source canvas
   * @param {number} r - Blur radius
   * @returns {HTMLCanvasElement} Blurred canvas
   */
  boxBlur(canvas, r) {
    const w = canvas.width, h = canvas.height;
    if (r <= 0 || w === 0 || h === 0) return canvas;
    
    const ctx = canvas.getContext('2d', { willReadFrequently: true });
    const imgData = ctx.getImageData(0, 0, w, h);
    const src = imgData.data;
    const tmp = new Uint8ClampedArray(src.length);
    const win = 2 * r + 1;
    
    // Horizontal pass
    for (let y = 0; y < h; y++) {
      let rs = 0, gs = 0, bs = 0, as = 0;
      const yoff = y * w * 4;
      
      // Initialize window
      for (let k = -r; k <= r; k++) {
        const xx = Math.max(0, k);
        const p = yoff + xx * 4;
        rs += src[p];
        gs += src[p + 1];
        bs += src[p + 2];
        as += src[p + 3];
      }
      
      for (let x = 0; x < w; x++) {
        const i = yoff + x * 4;
        tmp[i] = (rs / win) | 0;
        tmp[i + 1] = (gs / win) | 0;
        tmp[i + 2] = (bs / win) | 0;
        tmp[i + 3] = (as / win) | 0;
        
        const x0 = Math.max(0, x - r);
        const x1 = Math.min(w - 1, x + r + 1);
        const p0 = yoff + x0 * 4;
        const p1 = yoff + x1 * 4;
        rs += src[p1] - src[p0];
        gs += src[p1 + 1] - src[p0 + 1];
        bs += src[p1 + 2] - src[p0 + 2];
        as += src[p1 + 3] - src[p0 + 3];
      }
    }
    
    // Vertical pass
    const out = new Uint8ClampedArray(src.length);
    for (let x = 0; x < w; x++) {
      let rs = 0, gs = 0, bs = 0, as = 0;
      const xoff = x * 4;
      
      // Initialize window
      for (let k = -r; k <= r; k++) {
        const yy = Math.max(0, k);
        const p = yy * w * 4 + xoff;
        rs += tmp[p];
        gs += tmp[p + 1];
        bs += tmp[p + 2];
        as += tmp[p + 3];
      }
      
      for (let y = 0; y < h; y++) {
        const i = y * w * 4 + xoff;
        out[i] = (rs / win) | 0;
        out[i + 1] = (gs / win) | 0;
        out[i + 2] = (bs / win) | 0;
        out[i + 3] = (as / win) | 0;
        
        const y0 = Math.max(0, y - r);
        const y1 = Math.min(h - 1, y + r + 1);
        const p0 = y0 * w * 4 + xoff;
        const p1 = y1 * w * 4 + xoff;
        rs += tmp[p1] - tmp[p0];
        gs += tmp[p1 + 1] - tmp[p0 + 1];
        bs += tmp[p1 + 2] - tmp[p0 + 2];
        as += tmp[p1 + 3] - tmp[p0 + 3];
      }
    }
    
    imgData.data.set(out);
    ctx.putImageData(imgData, 0, 0);
    return canvas;
  }
  
  /**
   * Gaussian blur (approximated with multiple box blurs)
   * @param {HTMLCanvasElement} canvas - Source canvas
   * @param {number} r - Blur radius
   * @returns {HTMLCanvasElement} Blurred canvas
   */
  gaussianBlur(canvas, r) {
    // Approximate Gaussian with 3 box blur passes
    let result = canvas;
    for (let i = 0; i < 3; i++) {
      result = this.boxBlur(result, Math.max(1, r));
    }
    return result;
  }
  
  /**
   * Kuwahara filter - edge-preserving smoothing filter
   * @param {HTMLCanvasElement} canvas - Source canvas
   * @param {number} r - Radius (size of regions)
   * @returns {HTMLCanvasElement} Filtered canvas
   */
  kuwaharaFilter(canvas, r) {
    const w = canvas.width | 0;
    const h = canvas.height | 0;
    if (r <= 0 || w === 0 || h === 0) return canvas;
    
    const ctx = canvas.getContext('2d', { willReadFrequently: true });
    const img = ctx.getImageData(0, 0, w, h);
    const s = img.data;
    const out = new Uint8ClampedArray(s.length);
    
    // Build integral images for fast region sum calculation
    const sw = w + 1;
    const sh = h + 1;
    const N = sw * sh;
    const iR = new Float64Array(N);
    const iG = new Float64Array(N);
    const iB = new Float64Array(N);
    const iC = new Float64Array(N);
    const iL = new Float64Array(N);
    const iL2 = new Float64Array(N);
    
    const idx = (x, y) => y * sw + x;
    
    // Build integral images
    for (let y = 0; y < h; y++) {
      for (let x = 0; x < w; x++) {
        const p = (y * w + x) * 4;
        const a = s[p + 3] | 0;
        const c = (a >= 128) ? 1 : 0;
        const r0 = s[p] | 0;
        const g0 = s[p + 1] | 0;
        const b0 = s[p + 2] | 0;
        const lum = 0.299 * r0 + 0.587 * g0 + 0.114 * b0;
        
        const ii = idx(x + 1, y + 1);
        const left = idx(x, y + 1);
        const up = idx(x + 1, y);
        const upleft = idx(x, y);
        
        iR[ii] = iR[left] + iR[up] - iR[upleft] + (c ? r0 : 0);
        iG[ii] = iG[left] + iG[up] - iG[upleft] + (c ? g0 : 0);
        iB[ii] = iB[left] + iB[up] - iB[upleft] + (c ? b0 : 0);
        iC[ii] = iC[left] + iC[up] - iC[upleft] + c;
        iL[ii] = iL[left] + iL[up] - iL[upleft] + (c ? lum : 0);
        iL2[ii] = iL2[left] + iL2[up] - iL2[upleft] + (c ? lum * lum : 0);
      }
    }
    
    const rectSum = (ii, x0, y0, x1, y1) => {
      const a = idx(x0, y0);
      const b = idx(x1, y0);
      const c = idx(x0, y1);
      const d = idx(x1, y1);
      return ii[d] - ii[b] - ii[c] + ii[a];
    };
    
    const clampByte = (v) => (v < 0 ? 0 : v > 255 ? 255 : v | 0);
    
    // Process each pixel
    for (let y = 0; y < h; y++) {
      for (let x = 0; x < w; x++) {
        const p = (y * w + x) * 4;
        const a = s[p + 3] | 0;
        
        // Skip transparent pixels
        if (a < 128) {
          out[p] = s[p];
          out[p + 1] = s[p + 1];
          out[p + 2] = s[p + 2];
          out[p + 3] = a;
          continue;
        }
        
        // Define 4 regions (quadrants)
        const x0 = Math.max(0, x - r);
        const x1 = x;
        const x2 = Math.min(w - 1, x + r);
        const y0 = Math.max(0, y - r);
        const y1 = y;
        const y2 = Math.min(h - 1, y + r);
        
        const regions = [
          { x0: x0, y0: y0, x1: x1, y1: y1 }, // Top-left
          { x0: x1, y0: y0, x1: x2, y1: y1 }, // Top-right
          { x0: x0, y0: y1, x1: x1, y1: y2 }, // Bottom-left
          { x0: x1, y0: y1, x1: x2, y1: y2 }  // Bottom-right
        ];
        
        let bestVar = 1e20;
        let mR = s[p];
        let mG = s[p + 1];
        let mB = s[p + 2];
        
        // Find region with minimum variance
        for (let k = 0; k < 4; k++) {
          const rx0 = regions[k].x0;
          const ry0 = regions[k].y0;
          const rx1 = regions[k].x1;
          const ry1 = regions[k].y1;
          
          const ex0 = rx0;
          const ey0 = ry0;
          const ex1 = rx1 + 1;
          const ey1 = ry1 + 1;
          
          const cnt = rectSum(iC, ex0, ey0, ex1, ey1);
          if (cnt <= 0) continue;
          
          const sumL = rectSum(iL, ex0, ey0, ex1, ey1);
          const sumL2 = rectSum(iL2, ex0, ey0, ex1, ey1);
          const meanL = sumL / cnt;
          const variance = (sumL2 / cnt) - meanL * meanL;
          
          if (variance < bestVar) {
            bestVar = variance;
            const sr = rectSum(iR, ex0, ey0, ex1, ey1);
            const sg = rectSum(iG, ex0, ey0, ex1, ey1);
            const sb = rectSum(iB, ex0, ey0, ex1, ey1);
            mR = clampByte((sr / cnt) | 0);
            mG = clampByte((sg / cnt) | 0);
            mB = clampByte((sb / cnt) | 0);
          }
        }
        
        out[p] = mR;
        out[p + 1] = mG;
        out[p + 2] = mB;
        out[p + 3] = 255;
      }
    }
    
    img.data.set(out);
    ctx.putImageData(img, 0, 0);
    return canvas;
  }
  
  /**
   * Apply unsharp mask (sharpening)
   * @param {HTMLCanvasElement} canvas - Source canvas
   * @param {number} amount - Sharpen amount (0-300, percentage)
   * @param {number} radius - Blur radius for mask
   * @param {number} threshold - Threshold for sharpening (0-64)
   * @returns {HTMLCanvasElement} Sharpened canvas
   */
  applyUnsharpMask(canvas, amount, radius, threshold) {
    const w = canvas.width, h = canvas.height;
    if (w === 0 || h === 0 || amount <= 0 || radius <= 0) return canvas;
    
    const ctx = canvas.getContext('2d', { willReadFrequently: true });
    const imgData = ctx.getImageData(0, 0, w, h);
    const orig = imgData.data;
    
    // Create blurred version
    const blurCanvas = document.createElement('canvas');
    blurCanvas.width = w;
    blurCanvas.height = h;
    const blurCtx = blurCanvas.getContext('2d', { willReadFrequently: true });
    blurCtx.drawImage(canvas, 0, 0);
    
    const blurred = this.gaussianBlur(blurCanvas, radius);
    const blurImgData = blurred.getContext('2d').getImageData(0, 0, w, h);
    const blurData = blurImgData.data;
    
    const k = amount / 100;
    const clampByte = (v) => Math.min(255, Math.max(0, v));
    
    for (let i = 0; i < orig.length; i += 4) {
      const dr = orig[i] - blurData[i];
      const dg = orig[i + 1] - blurData[i + 1];
      const db = orig[i + 2] - blurData[i + 2];
      
      if (Math.abs(dr) >= threshold) orig[i] = clampByte(orig[i] + k * dr);
      if (Math.abs(dg) >= threshold) orig[i + 1] = clampByte(orig[i + 1] + k * dg);
      if (Math.abs(db) >= threshold) orig[i + 2] = clampByte(orig[i + 2] + k * db);
    }
    
    ctx.putImageData(imgData, 0, 0);
    return canvas;
  }

  // =============================================
  // MULTI-ALGORITHM DITHERING SYSTEM
  // =============================================

  /**
   * Apply dithering with multiple algorithm support
   * @param {Uint8ClampedArray} imageData - RGBA image data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {Array} palette - Color palette for dithering
   * @param {Object} options - Dithering options
   * @returns {Object} {data: Uint8ClampedArray, totalValidPixels: number}
   */
  applyDithering(imageData, width, height, palette, options = {}) {
    const {
      method = 'floyd',
      strength = 0.5,
      posterizeLevels = 0,
      paintTransparentPixels = false,
      paintWhitePixels = true,
      transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
      whiteThreshold = this.WHITE_THRESHOLD,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15,
      mask = null
    } = options;

    // Apply posterization if requested
    let workingData = new Uint8ClampedArray(imageData);
    if (posterizeLevels >= 2) {
      workingData = this._applyPosterize(workingData, width, height, posterizeLevels);
    }

    // Error diffusion methods
    const errorDiffusionMethods = {
      'floyd': this._getFloydSteinbergKernel(),
      'falsefloydsteinberg': this._getFalseFloydSteinbergKernel(),
      'atkinson': this._getAtkinsonKernel(),
      'jarvis': this._getJarvisKernel(),
      'stucki': this._getStuckiKernel(),
      'burkes': this._getBurkesKernel(),
      'sierra': this._getSierraKernel(),
      'twosierra': this._getTwoSierraKernel(),
      'sierralite': this._getSierraLiteKernel()
    };

    // Ordered dithering methods
    const orderedMethods = ['bayer2', 'bayer4', 'bayer8', 'random'];

    if (errorDiffusionMethods[method]) {
      return this._applyErrorDiffusion(workingData, width, height, palette, errorDiffusionMethods[method], options);
    } else if (orderedMethods.includes(method)) {
      return this._applyOrderedDither(workingData, width, height, palette, method, strength, options);
    } else {
      // Fallback to Floyd-Steinberg
      console.warn(`Unknown dithering method: ${method}, falling back to Floyd-Steinberg`);
      return this._applyErrorDiffusion(workingData, width, height, palette, this._getFloydSteinbergKernel(), options);
    }
  }

  /**
   * Apply posterization (color quantization by levels)
   * @private
   */
  _applyPosterize(data, width, height, levels) {
    const result = new Uint8ClampedArray(data);
    const step = 256 / levels;
    
    for (let i = 0; i < result.length; i += 4) {
      for (let c = 0; c < 3; c++) {
        const value = result[i + c];
        result[i + c] = Math.floor(value / step) * step;
      }
    }
    
    return result;
  }

  /**
   * Generic error diffusion dithering
   * @private
   */
  _applyErrorDiffusion(imageData, width, height, palette, kernel, options) {
    const data = new Uint8ClampedArray(imageData);
    const n = width * height;
    const { work, eligible } = this._ensureDitherBuffers(n);
    let totalValidPixels = 0;

    const {
      paintTransparentPixels = false,
      paintWhitePixels = true,
      transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
      whiteThreshold = this.WHITE_THRESHOLD,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15,
      mask = null
    } = options;

    // Initialize working buffers
    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const idx = y * width + x;
        const i4 = idx * 4;
        const r = data[i4];
        const g = data[i4 + 1];
        const b = data[i4 + 2];
        const a = data[i4 + 3];
        const masked = mask && mask[idx];

        const isEligible =
          !masked &&
          (paintTransparentPixels || a >= transparencyThreshold) &&
          (paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));

        eligible[idx] = isEligible ? 1 : 0;
        work[idx * 3] = r;
        work[idx * 3 + 1] = g;
        work[idx * 3 + 2] = b;

        if (!isEligible) {
          data[i4 + 3] = 0;
        }
      }
    }

    // Error diffusion function
    const diffuse = (nx, ny, er, eg, eb, factor) => {
      if (nx < 0 || nx >= width || ny < 0 || ny >= height) return;
      const nidx = ny * width + nx;
      if (!eligible[nidx]) return;
      const base = nidx * 3;
      work[base] = Math.min(255, Math.max(0, work[base] + er * factor));
      work[base + 1] = Math.min(255, Math.max(0, work[base + 1] + eg * factor));
      work[base + 2] = Math.min(255, Math.max(0, work[base + 2] + eb * factor));
    };

    // Apply error diffusion
    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const idx = y * width + x;
        if (!eligible[idx]) continue;

        const base = idx * 3;
        const r0 = work[base];
        const g0 = work[base + 1];
        const b0 = work[base + 2];
        const i4 = idx * 4;
        const a = data[i4 + 3];

        if (paintTransparentPixels && a < transparencyThreshold) {
          data[i4] = 0;
          data[i4 + 1] = 0;
          data[i4 + 2] = 0;
          data[i4 + 3] = 0;
          totalValidPixels++;
          continue;
        }

        const [nr, ng, nb] = this.findClosestPaletteColor(
          r0, g0, b0, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
        );

        data[i4] = nr;
        data[i4 + 1] = ng;
        data[i4 + 2] = nb;
        data[i4 + 3] = 255;
        totalValidPixels++;

        const er = r0 - nr;
        const eg = g0 - ng;
        const eb = b0 - nb;

        // Apply error diffusion kernel (kernel is an object with numbered properties)
        for (let k = 0; kernel[k]; k++) {
          const { dx, dy, weight } = kernel[k];
          diffuse(x + dx, y + dy, er, eg, eb, weight / kernel.divisor);
        }
      }
    }

    return { data, totalValidPixels };
  }

  /**
   * Apply ordered (threshold) dithering
   * @private
   */
  _applyOrderedDither(imageData, width, height, palette, method, strength, options) {
    const data = new Uint8ClampedArray(imageData);
    let totalValidPixels = 0;

    const {
      paintTransparentPixels = false,
      paintWhitePixels = true,
      transparencyThreshold = this.TRANSPARENCY_THRESHOLD,
      whiteThreshold = this.WHITE_THRESHOLD,
      algorithm = 'lab',
      enableChromaPenalty = false,
      chromaPenaltyWeight = 0.15
    } = options;

    // Get threshold matrix
    let matrix;
    if (method === 'bayer2') matrix = this._getBayer2x2();
    else if (method === 'bayer4') matrix = this._getBayer4x4();
    else if (method === 'bayer8') matrix = this._getBayer8x8();
    else if (method === 'random') matrix = null; // Random doesn't use matrix

    const matrixSize = matrix ? matrix.length : 0;

    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const i = (y * width + x) * 4;
        const r = data[i];
        const g = data[i + 1];
        const b = data[i + 2];
        const a = data[i + 3];

        const isTransparent = a < transparencyThreshold;
        const isEligible =
          (paintTransparentPixels || !isTransparent) &&
          (paintWhitePixels || !this.isWhitePixel(r, g, b, whiteThreshold));

        if (!isEligible) {
          data[i + 3] = 0;
          continue;
        }

        if (paintTransparentPixels && isTransparent) {
          data[i] = 0;
          data[i + 1] = 0;
          data[i + 2] = 0;
          data[i + 3] = 0;
          totalValidPixels++;
          continue;
        }

        // Get threshold value
        let threshold;
        if (method === 'random') {
          threshold = Math.random();
        } else {
          const mx = x % matrixSize;
          const my = y % matrixSize;
          threshold = matrix[my][mx];
        }

        // Apply strength modulation
        threshold = threshold * strength;

        // Add threshold noise to colors
        const noise = (threshold - 0.5) * 64 * strength;
        const nr = Math.max(0, Math.min(255, r + noise));
        const ng = Math.max(0, Math.min(255, g + noise));
        const nb = Math.max(0, Math.min(255, b + noise));

        const [pr, pg, pb] = this.findClosestPaletteColor(
          nr, ng, nb, palette, algorithm, { enableChromaPenalty, chromaPenaltyWeight }
        );

        data[i] = pr;
        data[i + 1] = pg;
        data[i + 2] = pb;
        data[i + 3] = 255;
        totalValidPixels++;
      }
    }

    return { data, totalValidPixels };
  }

  // Dithering kernel definitions
  _getFloydSteinbergKernel() {
    return {
      divisor: 16,
      0: { dx: 1, dy: 0, weight: 7 },
      1: { dx: -1, dy: 1, weight: 3 },
      2: { dx: 0, dy: 1, weight: 5 },
      3: { dx: 1, dy: 1, weight: 1 }
    };
  }

  _getFalseFloydSteinbergKernel() {
    return {
      divisor: 8,
      0: { dx: 1, dy: 0, weight: 3 },
      1: { dx: 0, dy: 1, weight: 3 },
      2: { dx: 1, dy: 1, weight: 2 }
    };
  }

  _getAtkinsonKernel() {
    return {
      divisor: 8,
      0: { dx: 1, dy: 0, weight: 1 },
      1: { dx: 2, dy: 0, weight: 1 },
      2: { dx: -1, dy: 1, weight: 1 },
      3: { dx: 0, dy: 1, weight: 1 },
      4: { dx: 1, dy: 1, weight: 1 },
      5: { dx: 0, dy: 2, weight: 1 }
    };
  }

  _getJarvisKernel() {
    return {
      divisor: 48,
      0: { dx: 1, dy: 0, weight: 7 },
      1: { dx: 2, dy: 0, weight: 5 },
      2: { dx: -2, dy: 1, weight: 3 },
      3: { dx: -1, dy: 1, weight: 5 },
      4: { dx: 0, dy: 1, weight: 7 },
      5: { dx: 1, dy: 1, weight: 5 },
      6: { dx: 2, dy: 1, weight: 3 },
      7: { dx: -2, dy: 2, weight: 1 },
      8: { dx: -1, dy: 2, weight: 3 },
      9: { dx: 0, dy: 2, weight: 5 },
      10: { dx: 1, dy: 2, weight: 3 },
      11: { dx: 2, dy: 2, weight: 1 }
    };
  }

  _getStuckiKernel() {
    return {
      divisor: 42,
      0: { dx: 1, dy: 0, weight: 8 },
      1: { dx: 2, dy: 0, weight: 4 },
      2: { dx: -2, dy: 1, weight: 2 },
      3: { dx: -1, dy: 1, weight: 4 },
      4: { dx: 0, dy: 1, weight: 8 },
      5: { dx: 1, dy: 1, weight: 4 },
      6: { dx: 2, dy: 1, weight: 2 },
      7: { dx: -2, dy: 2, weight: 1 },
      8: { dx: -1, dy: 2, weight: 2 },
      9: { dx: 0, dy: 2, weight: 4 },
      10: { dx: 1, dy: 2, weight: 2 },
      11: { dx: 2, dy: 2, weight: 1 }
    };
  }

  _getBurkesKernel() {
    return {
      divisor: 32,
      0: { dx: 1, dy: 0, weight: 8 },
      1: { dx: 2, dy: 0, weight: 4 },
      2: { dx: -2, dy: 1, weight: 2 },
      3: { dx: -1, dy: 1, weight: 4 },
      4: { dx: 0, dy: 1, weight: 8 },
      5: { dx: 1, dy: 1, weight: 4 },
      6: { dx: 2, dy: 1, weight: 2 }
    };
  }

  _getSierraKernel() {
    return {
      divisor: 32,
      0: { dx: 1, dy: 0, weight: 5 },
      1: { dx: 2, dy: 0, weight: 3 },
      2: { dx: -2, dy: 1, weight: 2 },
      3: { dx: -1, dy: 1, weight: 4 },
      4: { dx: 0, dy: 1, weight: 5 },
      5: { dx: 1, dy: 1, weight: 4 },
      6: { dx: 2, dy: 1, weight: 2 },
      7: { dx: -1, dy: 2, weight: 2 },
      8: { dx: 0, dy: 2, weight: 3 },
      9: { dx: 1, dy: 2, weight: 2 }
    };
  }

  _getTwoSierraKernel() {
    return {
      divisor: 16,
      0: { dx: 1, dy: 0, weight: 4 },
      1: { dx: 2, dy: 0, weight: 3 },
      2: { dx: -2, dy: 1, weight: 1 },
      3: { dx: -1, dy: 1, weight: 2 },
      4: { dx: 0, dy: 1, weight: 3 },
      5: { dx: 1, dy: 1, weight: 2 },
      6: { dx: 2, dy: 1, weight: 1 }
    };
  }

  _getSierraLiteKernel() {
    return {
      divisor: 4,
      0: { dx: 1, dy: 0, weight: 2 },
      1: { dx: 0, dy: 1, weight: 1 },
      2: { dx: 1, dy: 1, weight: 1 }
    };
  }

  // Bayer matrices for ordered dithering
  _getBayer2x2() {
    return [
      [0/4, 2/4],
      [3/4, 1/4]
    ];
  }

  _getBayer4x4() {
    return [
      [0/16, 8/16, 2/16, 10/16],
      [12/16, 4/16, 14/16, 6/16],
      [3/16, 11/16, 1/16, 9/16],
      [15/16, 7/16, 13/16, 5/16]
    ];
  }

  _getBayer8x8() {
    return [
      [0/64, 32/64, 8/64, 40/64, 2/64, 34/64, 10/64, 42/64],
      [48/64, 16/64, 56/64, 24/64, 50/64, 18/64, 58/64, 26/64],
      [12/64, 44/64, 4/64, 36/64, 14/64, 46/64, 6/64, 38/64],
      [60/64, 28/64, 52/64, 20/64, 62/64, 30/64, 54/64, 22/64],
      [3/64, 35/64, 11/64, 43/64, 1/64, 33/64, 9/64, 41/64],
      [51/64, 19/64, 59/64, 27/64, 49/64, 17/64, 57/64, 25/64],
      [15/64, 47/64, 7/64, 39/64, 13/64, 45/64, 5/64, 37/64],
      [63/64, 31/64, 55/64, 23/64, 61/64, 29/64, 53/64, 21/64]
    ];
  }

  // ====== POST-PROCESSING METHODS ======

  /**
   * Apply edge overlay to the image
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {string} algorithm - Edge detection algorithm: 'sobel', 'prewitt', 'roberts'
   * @param {number} threshold - Edge detection threshold (0-255, default 60)
   * @param {number} thickness - Edge line thickness (1-6, default 1)
   * @param {boolean} thinEdges - Apply non-maximum suppression (default false)
   * @returns {Uint8ClampedArray} Image data with edge overlay applied
   */
  applyEdgeOverlay(imageData, width, height, algorithm = 'sobel', threshold = 60, thickness = 1, thinEdges = false) {
    // Detect edges with optional NMS thinning
    const edges = this._detectEdges(imageData, width, height, algorithm, threshold, thinEdges);
    const result = new Uint8ClampedArray(imageData);

    // Apply thickness by dilating edges (if thickness > 1)
    const processed = this._dilateEdges(edges, width, height, thickness);

    for (let i = 0; i < processed.length; i++) {
      if (processed[i] > 128) {
        const idx = i * 4;
        result[idx] = 0;     // R
        result[idx + 1] = 0; // G
        result[idx + 2] = 0; // B
        result[idx + 3] = 255; // A (keep alpha)
      }
    }

    return result;
  }

  /**
   * Detect edges using specified algorithm
   * @private
   */
  _detectEdges(imageData, width, height, algorithm, threshold = 60, applyNMS = false) {
    const gray = new Uint8ClampedArray(width * height);
    const magnitude = new Float32Array(width * height);
    const direction = applyNMS ? new Int8Array(width * height) : null;

    // Convert to grayscale
    for (let i = 0; i < imageData.length; i += 4) {
      const idx = i / 4;
      gray[idx] = Math.round(0.299 * imageData[i] + 0.587 * imageData[i + 1] + 0.114 * imageData[i + 2]);
    }

    // Apply edge detection and store magnitude + direction
    switch (algorithm) {
      case 'sobel':
        this._sobelEdgeDetection(gray, magnitude, direction, width, height);
        break;
      case 'prewitt':
        this._prewittEdgeDetection(gray, magnitude, direction, width, height);
        break;
      case 'roberts':
        this._robertsEdgeDetection(gray, magnitude, direction, width, height);
        break;
      case 'laplacian':
        this._laplacianEdgeDetection(gray, magnitude, width, height);
        break;
      default:
        this._sobelEdgeDetection(gray, magnitude, direction, width, height);
    }

    const edges = new Uint8ClampedArray(width * height);

    // Apply non-maximum suppression if requested
    if (applyNMS && direction) {
      for (let y = 1; y < height - 1; y++) {
        for (let x = 1; x < width - 1; x++) {
          const i = y * width + x;
          const mag = magnitude[i];
          if (mag < threshold) continue;

          const dir = direction[i];
          let m1 = 0, m2 = 0;
          
          // Compare with neighbors along gradient direction
          if (dir === 0) {  // Horizontal
            m1 = magnitude[i - 1];
            m2 = magnitude[i + 1];
          } else {  // Vertical
            m1 = magnitude[i - width];
            m2 = magnitude[i + width];
          }

          // Keep only if local maximum
          if (mag >= m1 && mag >= m2) {
            edges[i] = 255;
          }
        }
      }
    } else {
      // Simple thresholding
      for (let i = 0; i < edges.length; i++) {
        edges[i] = magnitude[i] > threshold ? 255 : 0;
      }
    }

    return edges;
  }

  /**
   * Sobel edge detection
   * @private
   */
  _sobelEdgeDetection(gray, magnitude, direction, width, height) {
    const sobelX = [-1, 0, 1, -2, 0, 2, -1, 0, 1];
    const sobelY = [-1, -2, -1, 0, 0, 0, 1, 2, 1];

    for (let y = 1; y < height - 1; y++) {
      for (let x = 1; x < width - 1; x++) {
        let gx = 0, gy = 0;
        let idx = 0;
        for (let ky = -1; ky <= 1; ky++) {
          for (let kx = -1; kx <= 1; kx++) {
            const pixelIdx = (y + ky) * width + (x + kx);
            gx += sobelX[idx] * gray[pixelIdx];
            gy += sobelY[idx] * gray[pixelIdx];
            idx++;
          }
        }
        const i = y * width + x;
        magnitude[i] = Math.abs(gx) + Math.abs(gy);
        
        // Store gradient direction for NMS
        if (direction) {
          const ax = Math.abs(gx);
          const ay = Math.abs(gy);
          direction[i] = ax >= ay ? 0 : 1;  // 0=horizontal, 1=vertical
        }
      }
    }
  }

  /**
   * Prewitt edge detection
   * @private
   */
  _prewittEdgeDetection(gray, magnitude, direction, width, height) {
    const prewittX = [-1, 0, 1, -1, 0, 1, -1, 0, 1];
    const prewittY = [1, 1, 1, 0, 0, 0, -1, -1, -1];

    for (let y = 1; y < height - 1; y++) {
      for (let x = 1; x < width - 1; x++) {
        let gx = 0, gy = 0;
        let idx = 0;
        for (let ky = -1; ky <= 1; ky++) {
          for (let kx = -1; kx <= 1; kx++) {
            const pixelIdx = (y + ky) * width + (x + kx);
            gx += prewittX[idx] * gray[pixelIdx];
            gy += prewittY[idx] * gray[pixelIdx];
            idx++;
          }
        }
        const i = y * width + x;
        magnitude[i] = Math.abs(gx) + Math.abs(gy);
        
        if (direction) {
          const ax = Math.abs(gx);
          const ay = Math.abs(gy);
          direction[i] = ax >= ay ? 0 : 1;
        }
      }
    }
  }

  /**
   * Roberts edge detection
   * @private
   */
  _robertsEdgeDetection(gray, magnitude, direction, width, height) {
    for (let y = 0; y < height - 1; y++) {
      for (let x = 0; x < width - 1; x++) {
        const idx = y * width + x;
        const gx = gray[idx] - gray[idx + width + 1];
        const gy = gray[idx + 1] - gray[idx + width];
        magnitude[idx] = Math.abs(gx) + Math.abs(gy);
        
        if (direction) {
          const ax = Math.abs(gx);
          const ay = Math.abs(gy);
          direction[idx] = ax >= ay ? 0 : 1;
        }
      }
    }
  }

  /**
   * Laplacian edge detection
   * @private
   */
  _laplacianEdgeDetection(gray, magnitude, width, height) {
    // Laplacian kernel for edge detection
    const laplacianKernel = [0, -1, 0, -1, 4, -1, 0, -1, 0];

    for (let y = 1; y < height - 1; y++) {
      for (let x = 1; x < width - 1; x++) {
        let sum = 0;
        let idx = 0;
        for (let ky = -1; ky <= 1; ky++) {
          for (let kx = -1; kx <= 1; kx++) {
            const pixelIdx = (y + ky) * width + (x + kx);
            sum += laplacianKernel[idx] * gray[pixelIdx];
            idx++;
          }
        }
        magnitude[y * width + x] = Math.abs(sum);
      }
    }
  }

  /**
   * Dilate edges to make them thicker
   * @private
   */
  _dilateEdges(edges, width, height, thickness = 1) {
    const t = Math.max(1, Math.min(6, thickness || 1));
    // WPlace uses thickness - 1 for dilation count
    // thickness=1 means no dilation (keep original edges)
    // thickness=2 means dilate 1 time, etc.
    const dilationCount = t - 1;
    
    if (dilationCount <= 0) return new Uint8ClampedArray(edges);
    
    const dilated = new Uint8ClampedArray(edges);
    const tmp = new Uint8ClampedArray(edges.length);

    for (let k = 0; k < dilationCount; k++) {
      tmp.fill(0);
      for (let y = 0; y < height; y++) {
        for (let x = 0; x < width; x++) {
          const i = y * width + x;
          if (dilated[i] > 128) {
            tmp[i] = 255;
            continue;
          }
          // Check 4-connected neighbors
          if ((x > 0 && dilated[i - 1] > 128) ||
              (x + 1 < width && dilated[i + 1] > 128) ||
              (y > 0 && dilated[i - width] > 128) ||
              (y + 1 < height && dilated[i + width] > 128)) {
            tmp[i] = 255;
          }
        }
      }
      dilated.set(tmp);
    }

    return dilated;
  }

  /**
   * Apply outline to the image
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {number} thickness - Outline thickness in pixels (0-10)
   * @returns {Uint8ClampedArray} Image data with outline applied
   */
  applyOutline(imageData, width, height, thickness) {
    // Morphological dilation approach from wplace_helper
    const t = Math.max(0, Math.floor(thickness || 0));
    if (t <= 0) return new Uint8ClampedArray(imageData);

    const w = width;
    const h = height;
    const data = new Uint8ClampedArray(imageData);
    
    // Create binary mask: 1 = opaque (alpha >= 128), 0 = transparent
    const mask = new Uint8Array(w * h);
    for (let i = 0, p = 0; i < mask.length; i++, p += 4) {
      mask[i] = data[p + 3] >= 128 ? 1 : 0;
    }

    // Dilate the mask t times
    const dil = new Uint8Array(mask);
    const tmp = new Uint8Array(mask.length);
    
    function dilateOnce() {
      tmp.fill(0);
      for (let y = 0; y < h; y++) {
        for (let x = 0; x < w; x++) {
          const i = y * w + x;
          if (dil[i]) {
            tmp[i] = 1;
            continue;
          }
          // Check 4-connected neighbors
          if ((x > 0     && dil[i - 1]) ||
              (x + 1 < w && dil[i + 1]) ||
              (y > 0     && dil[i - w]) ||
              (y + 1 < h && dil[i + w])) {
            tmp[i] = 1;
          }
        }
      }
      dil.set(tmp);
    }
    
    // Apply dilation t times
    for (let k = 0; k < t; k++) {
      dilateOnce();
    }

    // Draw outline where dilated but not original
    for (let y = 0; y < h; y++) {
      for (let x = 0; x < w; x++) {
        const i = y * w + x;
        if (dil[i] && !mask[i]) {
          const p = i * 4;
          data[p] = 0;          // R
          data[p + 1] = 0;      // G
          data[p + 2] = 0;      // B
          data[p + 3] = 255;    // A
        }
      }
    }

    return data;
  }

  /**
   * Simplify regions by merging small color areas
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {number} minArea - Minimum area in pixels (0-1000)
   * @returns {Uint8ClampedArray} Simplified image data
   */
  simplifyRegions(imageData, width, height, minArea) {
    if (minArea <= 0) return new Uint8ClampedArray(imageData);

    const thr = Math.max(1, Math.floor(minArea || 0));
    if (width === 0 || height === 0) return new Uint8ClampedArray(imageData);

    const result = new Uint8ClampedArray(imageData);
    const visited = new Uint8Array(width * height);
    const qx = new Int32Array(width * height);
    const qy = new Int32Array(width * height);
    let qs = 0, qe = 0;

    function idx(x, y) { return (y * width + x) << 2; }

    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const li = y * width + x;
        if (visited[li]) continue;

        const p = li << 2;
        const a = result[p + 3] | 0;
        if (a < 128) {
          visited[li] = 1;
          continue;
        }

        const r0 = result[p] | 0;
        const g0 = result[p + 1] | 0;
        const b0 = result[p + 2] | 0;
        const key = ((r0 << 16) | (g0 << 8) | b0) >>> 0;

        qs = 0;
        qe = 0;
        qx[qe] = x;
        qy[qe] = y;
        qe++;

        const comp = [];
        const borderColors = {};
        visited[li] = 1;

        while (qs < qe) {
          const cx0 = qx[qs];
          const cy0 = qy[qs];
          qs++;
          const pi = idx(cx0, cy0);
          comp.push(pi);

          const neighbors = [[1, 0], [-1, 0], [0, 1], [0, -1]];
          for (let k = 0; k < 4; k++) {
            const nx = cx0 + neighbors[k][0];
            const ny = cy0 + neighbors[k][1];
            if (nx < 0 || ny < 0 || nx >= width || ny >= height) continue;

            const l2 = ny * width + nx;
            if (visited[l2]) continue;

            const p2 = idx(nx, ny);
            const a2 = result[p2 + 3] | 0;
            if (a2 < 128) {
              visited[l2] = 1;
              continue;
            }

            const r = result[p2] | 0;
            const g = result[p2 + 1] | 0;
            const b = result[p2 + 2] | 0;
            const k2 = ((r << 16) | (g << 8) | b) >>> 0;

            if (k2 === key) {
              visited[l2] = 1;
              qx[qe] = nx;
              qy[qe] = ny;
              qe++;
            } else {
              borderColors[k2] = (borderColors[k2] || 0) + 1;
            }
          }
        }

        if (comp.length > 0 && comp.length < thr) {
          let bestKey = -1;
          let bestCnt = -1;
          for (const ks in borderColors) {
            const c = borderColors[ks] | 0;
            if (c > bestCnt) {
              bestCnt = c;
              bestKey = Number(ks) | 0;
            }
          }

          if (bestKey >= 0) {
            const nr = (bestKey >>> 16) & 255;
            const ng = (bestKey >>> 8) & 255;
            const nb = bestKey & 255;
            for (let i = 0; i < comp.length; i++) {
              const p3 = comp[i];
              result[p3] = nr;
              result[p3 + 1] = ng;
              result[p3 + 2] = nb;
              result[p3 + 3] = 255;
            }
          }
        }
      }
    }

    return result;
  }

  /**
   * Flood fill to find connected regions
   * @private
   */

  /**
   * Erode edges by shrinking them inward
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {number} amount - Erosion amount in pixels (0-20)
   * @returns {Uint8ClampedArray} Eroded image data
   */
  erodeEdges(imageData, width, height, amount) {
    if (amount <= 0) return new Uint8ClampedArray(imageData);

    const result = new Uint8ClampedArray(imageData);

    for (let iteration = 0; iteration < amount; iteration++) {
      const temp = new Uint8ClampedArray(result);
      for (let y = 1; y < height - 1; y++) {
        for (let x = 1; x < width - 1; x++) {
          const idx = (y * width + x) * 4;
          const alpha = temp[idx + 3];

          // Check if any neighbor is transparent
          let hasTransparentNeighbor = false;
          for (let dy = -1; dy <= 1 && !hasTransparentNeighbor; dy++) {
            for (let dx = -1; dx <= 1; dx++) {
              const nidx = ((y + dy) * width + (x + dx)) * 4;
              if (temp[nidx + 3] < 128) {
                hasTransparentNeighbor = true;
                break;
              }
            }
          }

          if (hasTransparentNeighbor && alpha > 128) {
            result[idx + 3] = 0; // Make transparent
          }
        }
      }
    }

    return result;
  }

  /**
   * Apply mode filter (median-like filtering) - optimized from wplace_helper
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} width - Image width
   * @param {number} height - Image height
   * @param {number} radius - Filter radius (1-10)
   * @returns {Uint8ClampedArray} Filtered image data
   */
  applyModeFilter(imageData, width, height, radius) {
    if (radius <= 0 || width === 0 || height === 0) return new Uint8ClampedArray(imageData);

    const n = radius | 0;
    if (n <= 1) return new Uint8ClampedArray(imageData);

    const left = Math.floor((n - 1) / 2);
    const right = n - left - 1;
    const result = new Uint8ClampedArray(imageData);
    const out = new Uint8ClampedArray(imageData);

    for (let y = 0; y < height; y++) {
      for (let x = 0; x < width; x++) {
        const i = (y * width + x) * 4;
        const a = imageData[i + 3] | 0;

        // Skip transparent pixels
        if (a < 128) {
          out[i] = imageData[i];
          out[i + 1] = imageData[i + 1];
          out[i + 2] = imageData[i + 2];
          out[i + 3] = a;
          continue;
        }

        const counts = {};
        let best = 0;
        let bR = imageData[i];
        let bG = imageData[i + 1];
        let bB = imageData[i + 2];

        for (let dy = -left; dy <= right; dy++) {
          const yy = y + dy;
          if (yy < 0 || yy >= height) continue;

          for (let dx = -left; dx <= right; dx++) {
            const xx = x + dx;
            if (xx < 0 || xx >= width) continue;

            const j = (yy * width + xx) * 4;
            if ((imageData[j + 3] | 0) < 128) continue;

            const key = ((imageData[j] | 0) << 16) | ((imageData[j + 1] | 0) << 8) | (imageData[j + 2] | 0);
            const c = (counts[key] || 0) + 1;
            counts[key] = c;

            if (c > best) {
              best = c;
              bR = imageData[j];
              bG = imageData[j + 1];
              bB = imageData[j + 2];
            }
          }
        }

        out[i] = bR;
        out[i + 1] = bG;
        out[i + 2] = bB;
        out[i + 3] = 255;
      }
    }

    // Copy result back
    for (let i = 0; i < out.length; i += 4) {
      result[i] = out[i];
      result[i + 1] = out[i + 1];
      result[i + 2] = out[i + 2];
    }

    return result;
  }

  // ====== COLOR CORRECTION METHODS ======

  /**
   * Apply color correction to the image
   * @param {Uint8ClampedArray} imageData - RGBA pixel data
   * @param {number} brightness - Brightness adjustment (-100 to 100)
   * @param {number} contrast - Contrast adjustment (-100 to 100)
   * @param {number} saturation - Saturation adjustment (-100 to 100)
   * @param {number} hue - Hue rotation (-180 to 180 degrees)
   * @param {number} gamma - Gamma correction (0.5 to 3.0)
   * @returns {Uint8ClampedArray} Color-corrected image data
   */
  applyColorCorrection(imageData, brightness = 0, contrast = 0, saturation = 0, hue = 0, gamma = 1.0) {
    const result = new Uint8ClampedArray(imageData);

    for (let i = 0; i < result.length; i += 4) {
      let r = result[i];
      let g = result[i + 1];
      let b = result[i + 2];

      // Brightness
      if (brightness !== 0) {
        r = Math.max(0, Math.min(255, r + (brightness * 2.55)));
        g = Math.max(0, Math.min(255, g + (brightness * 2.55)));
        b = Math.max(0, Math.min(255, b + (brightness * 2.55)));
      }

      // Contrast
      if (contrast !== 0) {
        const factor = (contrast + 100) / 100;
        r = Math.max(0, Math.min(255, (r - 128) * factor + 128));
        g = Math.max(0, Math.min(255, (g - 128) * factor + 128));
        b = Math.max(0, Math.min(255, (b - 128) * factor + 128));
      }

      // Saturation
      if (saturation !== 0) {
        const [h, s, l] = this._rgbToHsl(r, g, b);
        const newS = Math.max(0, Math.min(100, s + saturation));
        [r, g, b] = this._hslToRgb(h, newS, l);
      }

      // Hue rotation
      if (hue !== 0) {
        const [h, s, l] = this._rgbToHsl(r, g, b);
        const newH = (h + hue) % 360;
        [r, g, b] = this._hslToRgb(newH, s, l);
      }

      // Gamma correction
      if (gamma !== 1.0) {
        const invGamma = 1.0 / gamma;
        r = Math.pow(r / 255, invGamma) * 255;
        g = Math.pow(g / 255, invGamma) * 255;
        b = Math.pow(b / 255, invGamma) * 255;
      }

      result[i] = Math.round(r);
      result[i + 1] = Math.round(g);
      result[i + 2] = Math.round(b);
    }

    return result;
  }

  /**
   * Convert RGB to HSL
   * @private
   */
  _rgbToHsl(r, g, b) {
    r /= 255;
    g /= 255;
    b /= 255;
    const max = Math.max(r, g, b);
    const min = Math.min(r, g, b);
    let h = 0, s = 0;
    const l = (max + min) / 2;

    if (max !== min) {
      const d = max - min;
      s = l > 0.5 ? d / (2 - max - min) : d / (max + min);
      switch (max) {
        case r: h = ((g - b) / d + (g < b ? 6 : 0)) / 6; break;
        case g: h = ((b - r) / d + 2) / 6; break;
        case b: h = ((r - g) / d + 4) / 6; break;
      }
    }

    return [h * 360, s * 100, l * 100];
  }

  /**
   * Convert HSL to RGB
   * @private
   */
  _hslToRgb(h, s, l) {
    h /= 360;
    s /= 100;
    l /= 100;

    let r, g, b;
    if (s === 0) {
      r = g = b = l;
    } else {
      const hue2rgb = (p, q, t) => {
        if (t < 0) t += 1;
        if (t > 1) t -= 1;
        if (t < 1/6) return p + (q - p) * 6 * t;
        if (t < 1/2) return q;
        if (t < 2/3) return p + (q - p) * (2/3 - t) * 6;
        return p;
      };
      const q = l < 0.5 ? l * (1 + s) : l + s - l * s;
      const p = 2 * l - q;
      r = hue2rgb(p, q, h + 1/3);
      g = hue2rgb(p, q, h);
      b = hue2rgb(p, q, h - 1/3);
    }

    return [Math.round(r * 255), Math.round(g * 255), Math.round(b * 255)];
  }

  /**
   * Color matching helper
   * @private
   */
  _colorMatch(color1, color2, tolerance = 10) {
    return Math.abs(color1[0] - color2[0]) <= tolerance &&
           Math.abs(color1[1] - color2[1]) <= tolerance &&
           Math.abs(color1[2] - color2[2]) <= tolerance;
  }
}


// Create global instance
window.WPlaceImageProcessor = ImageProcessor;

// Create global instance for Auto-Image.js compatibility
window.globalImageProcessor = new ImageProcessor();

// Legacy compatibility - expose key methods globally for backward compatibility
window.ImageProcessor = ImageProcessor;

console.log('✅ WPlace Image Processor loaded and ready');