HTML5 Canvas Image Resizing (Downscale) with High Quality

Resizing images in the browser using HTML5 canvas can result in poor quality, especially when downscaling. This article investigates the issue and provides a solution for achieving optimal quality during downscaling.

Disabling Interpolation and Image Smoothing

The initial CSS and JS code provided in the question included properties for disabling interpolation and image smoothing:

image-rendering: optimizeQuality;
image-rendering: -moz-crisp-edges;
image-rendering: -webkit-optimize-contrast;
image-rendering: optimize-contrast;
-ms-interpolation-mode: nearest-neighbor;

However, these properties do not directly impact downscaling quality. Interpolation and smoothing are concerned with creating new pixels, which is not relevant when reducing image size.

Downsampling vs. Interpolation

The issue with downscaling images in browsers is related to downsampling rather than interpolation.

In downsampling, browsers typically use a simple method where they select a single pixel from the source image for each pixel in the destination image. This can result in loss of detail and noise.

Pixel-Perfect Downsampling Algorithm

To solve this issue, we need a pixel-perfect downsampling algorithm that takes all source pixels into account. The provided code snippet is an example of such an algorithm:

function downScaleCanvas(cv, scale) {
    // Process all pixels in the source image
    for (sy = 0; sy < sh; sy  ) {
        for (sx = 0; sx < sw; sx  ) {
            // Calculate target pixel position and weights
            ...

            // Add weighted contributions to target buffer
            ...
        }
    }

    // Create result canvas and populate it
    ...

    return resCV;
}

This algorithm computes the contribution of each source pixel to one, two, or four destination pixels, ensuring that all details are preserved during downscaling.

Significance of Multiple Downscaling Steps

Downscaling in multiple steps can lead to increased fuzziness in the image. This is because the cumulative rounding errors from successive downscaling operations result in greater noise.

Comparison with Other Approaches

The provided algorithm outperforms other downsampling techniques, as demonstrated in the example images. It achieves a balance between retaining sharpness and minimizing noise, even with multiple downscaling steps.