📊 Image Similarity Comparison

Interactive demonstration of SSIM.js for comparing image similarity

1. Canvas Setup for Image Processing

Creating a canvas element to convert images to ImageData

Code 
// Canvas needed to convert image to imageData
// To save overhead, define it outside and use this one
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d', {
    willReadFrequently: true
});
✨ Result

Canvas Element Created

Size: 200 × 150

Context: 2D with willReadFrequently optimization

💡 Tip: The willReadFrequently: true option optimizes the canvas for frequent getImageData() calls, improving performance for image comparison tasks.

2. Converting Images to ImageData

The image2data method that converts image elements to ImageData objects

Code 
// Method to convert image to imagedata
const image2data = (img) => {
    const { width, height } = canvas;
    ctx.clearRect(0, 0, width, height);
    ctx.drawImage(img, 0, 0, width, height);
    return ctx.getImageData(0, 0, width, height);
};
✨ Result

Original Image

ImageData Object

Width: -

Height: -

Data Length: - pixels

Color Space: sRGB

3. Image Similarity Comparison

Compare two images and get a similarity score (MSSIM)

Code 
// Simulate SSIM comparison (simplified version)
const compareImages = (imgData1, imgData2) => {
    // Calculate pixel-by-pixel difference
    let totalDiff = 0;
    for (let i = 0; i < imgData1.data.length; i += 4) {
        const diff = Math.abs(imgData1.data[i] - imgData2.data[i]);
        totalDiff += diff;
    }
    const maxDiff = imgData1.data.length / 4 * 255;
    const similarity = 1 - (totalDiff / maxDiff);
    return similarity.toFixed(4);
};
✨ Result

📁 Click to upload Image 1

📁 Click to upload Image 2

📊 Interpretation Guide:
• Below 0.4: Clear difference between images
• 0.4 - 0.7: Moderate similarity
• Above 0.7: High similarity
• Close to 1.0: Nearly identical images

4. Performance Optimization with Canvas Sizing

Smaller canvas sizes improve performance without sacrificing accuracy

Code 
// Resize canvas for optimal performance
const optimizeCanvasSize = (targetSize = 128) => {
    canvas.width = targetSize;
    canvas.height = targetSize;
    // Now image2data will use this optimized size
    // Performance improves significantly!
};
✨ Result

Canvas Size: 128 × 128

Pixel Count: 16,384

Performance Impact: Optimal

⚡ Performance Tip: A 128×128 canvas is often sufficient for similarity comparison. Larger sizes increase processing time without significantly improving accuracy. For batch operations, use smaller canvas sizes!

5. Real-world Use Cases

Practical applications of image similarity comparison

Code 
// Example: Remove duplicate frames from GIF
const removeDuplicateFrames = (frames, threshold = 0.95) => {
    const uniqueFrames = [frames[0]];

    for (let i = 1; i < frames.length; i++) {
        const lastFrame = uniqueFrames[uniqueFrames.length - 1];
        const similarity = compareImages(lastFrame, frames[i]);

        if (similarity < threshold) {
            uniqueFrames.push(frames[i]);
        }
    }

    return uniqueFrames;
};
✨ Result

🎬 GIF Frame Deduplication Simulator

Original Frames: 100

Similarity Threshold: 0.95

Unique Frames: -

Compression Ratio: -

🎯 Use Cases:
• GIF Compression: Remove similar consecutive frames
• Video Scene Detection: Identify transition points
• Duplicate Detection: Find similar images in large collections
• Quality Assurance: Compare rendered output with expected results