javascript browser - HTML5 Canvas Resize(Downscale)Image High Quality?

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I use html5 canvas elements to resize images im my browser. It turns out that the quality is very low. I found this: Disable Interpolation when Scaling a <canvas> but it does not help to increase the quality.

Below is my css and js code as well as the image scalled with Photoshop and scaled in the canvas API.

What do I have to do to get optimal quality when scaling an image in the browser?

Note: I want to scale down a large image to a small one, modify color in a canvas and send the result from the canvas to the server.


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


var $img = $('<img>');
var $originalCanvas = $('<canvas>');
$img.load(function() {

   var originalContext = $originalCanvas[0].getContext('2d');   
   originalContext.imageSmoothingEnabled = false;
   originalContext.webkitImageSmoothingEnabled = false;
   originalContext.mozImageSmoothingEnabled = false;
   originalContext.drawImage(this, 0, 0, 379, 500);

The image resized with photoshop:

The image resized on canvas:


I tried to make downscaling in more than one steps as proposed in:

Resizing an image in an HTML5 canvas and Html5 canvas drawImage: how to apply antialiasing

This is the function I have used:

function resizeCanvasImage(img, canvas, maxWidth, maxHeight) {
    var imgWidth = img.width, 
        imgHeight = img.height;

    var ratio = 1, ratio1 = 1, ratio2 = 1;
    ratio1 = maxWidth / imgWidth;
    ratio2 = maxHeight / imgHeight;

    // Use the smallest ratio that the image best fit into the maxWidth x maxHeight box.
    if (ratio1 < ratio2) {
        ratio = ratio1;
    else {
        ratio = ratio2;

    var canvasContext = canvas.getContext("2d");
    var canvasCopy = document.createElement("canvas");
    var copyContext = canvasCopy.getContext("2d");
    var canvasCopy2 = document.createElement("canvas");
    var copyContext2 = canvasCopy2.getContext("2d");
    canvasCopy.width = imgWidth;
    canvasCopy.height = imgHeight;  
    copyContext.drawImage(img, 0, 0);

    // init
    canvasCopy2.width = imgWidth;
    canvasCopy2.height = imgHeight;        
    copyContext2.drawImage(canvasCopy, 0, 0, canvasCopy.width, canvasCopy.height, 0, 0, canvasCopy2.width, canvasCopy2.height);

    var rounds = 2;
    var roundRatio = ratio * rounds;
    for (var i = 1; i <= rounds; i++) {
        console.log("Step: "+i);

        // tmp
        canvasCopy.width = imgWidth * roundRatio / i;
        canvasCopy.height = imgHeight * roundRatio / i;

        copyContext.drawImage(canvasCopy2, 0, 0, canvasCopy2.width, canvasCopy2.height, 0, 0, canvasCopy.width, canvasCopy.height);

        // copy back
        canvasCopy2.width = imgWidth * roundRatio / i;
        canvasCopy2.height = imgHeight * roundRatio / i;
        copyContext2.drawImage(canvasCopy, 0, 0, canvasCopy.width, canvasCopy.height, 0, 0, canvasCopy2.width, canvasCopy2.height);

    } // end for

    // copy back to canvas
    canvas.width = imgWidth * roundRatio / rounds;
    canvas.height = imgHeight * roundRatio / rounds;
    canvasContext.drawImage(canvasCopy2, 0, 0, canvasCopy2.width, canvasCopy2.height, 0, 0, canvas.width, canvas.height);


Here is the result if I use a 2 step down sizing:

Here is the result if I use a 3 step down sizing:

Here is the result if I use a 4 step down sizing:

Here is the result if I use a 20 step down sizing:

Note: It turns out that from 1 step to 2 steps there is a large improvement in image quality but the more steps you add to the process the more fuzzy the image becomes.

Is there a way to solve the problem that the image gets more fuzzy the more steps you add?

Edit 2013-10-04: I tried the algorithm of GameAlchemist. Here is the result compared to Photoshop.

PhotoShop Image:

GameAlchemist's Algorithm:


This is the improved Hermite resize filter that utilises 1 worker so that the window doesn't freeze.

Not the right answer for people who really need to resize the image itself, but just to shrink the file size.

I had a problem with "directly from the camera" pictures, that my customers often uploaded in "uncompressed" JPEG.

Not so well known is, that the canvas supports (in most browsers 2017) to change the quality of JPEG

data=canvas.toDataURL('image/jpeg', .85) # [1..0] default 0.92

With this trick I could reduce 4k x 3k pics with >10Mb to 1 or 2Mb, sure it depends on your needs.

look here

I found a solution that doesn't need to access directly the pixel data and loop through it to perform the downsampling. Depending on the size of the image this can be very resource intensive, and it would be better to use the browser's internal algorithms.

The drawImage() function is using a linear-interpolation, nearest-neighbor resampling method. That works well when you are not resizing down more than half the original size.

If you loop to only resize max one half at a time, the results would be quite good, and much faster than accessing pixel data.

This function downsample to half at a time until reaching the desired size:

  function resize_image( src, dst, type, quality ) {
     var tmp = new Image(),
         canvas, context, cW, cH;

     type = type || 'image/jpeg';
     quality = quality || 0.92;

     cW = src.naturalWidth;
     cH = src.naturalHeight;

     tmp.src = src.src;
     tmp.onload = function() {

        canvas = document.createElement( 'canvas' );

        cW /= 2;
        cH /= 2;

        if ( cW < src.width ) cW = src.width;
        if ( cH < src.height ) cH = src.height;

        canvas.width = cW;
        canvas.height = cH;
        context = canvas.getContext( '2d' );
        context.drawImage( tmp, 0, 0, cW, cH );

        dst.src = canvas.toDataURL( type, quality );

        if ( cW <= src.width || cH <= src.height )

        tmp.src = dst.src;

  // The images sent as parameters can be in the DOM or be image objects
  resize_image( $( '#original' )[0], $( '#smaller' )[0] );

Credits to this post

context.scale(xScale, yScale)

<canvas id="c"></canvas>
<img id="i" />

var i = document.getElementById('i');

i.onload = function(){
    var width = this.naturalWidth,
        height = this.naturalHeight,
        canvas = document.getElementById('c'),
        ctx = canvas.getContext('2d');

    canvas.width = Math.floor(width / 2);
    canvas.height = Math.floor(height / 2);

    ctx.scale(0.5, 0.5);
    ctx.drawImage(this, 0, 0);

    // restore original 1x1 scale
    ctx.scale(2, 2);

i.src = '';

Why use the canvas to resize images? Modern browsers all use bicubic interpolation — the same process used by Photoshop (if you're doing it right) — and they do it faster than the canvas process. Just specify the image size you want (use only one dimension, height or width, to resize proportionally).

This is supported by most browsers, including later versions of IE. Earlier versions may require browser-specific CSS.

A simple function (using jQuery) to resize an image would be like this:

function resizeImage(img, percentage) {
    var coeff = percentage/100,
        width = $(img).width(),
        height = $(img).height();

    return {"width": width*coeff, "height": height*coeff}           

EDIT Changed image to img to match function args. ^)^

Then just use the returned value to resize the image in one or both dimensions.

Obviously there are different refinements you could make, but this gets the job done.


Paste the following code into the console of this page and watch what happens to the gravatars:

function resizeImage(img, percentage) {
    var coeff = percentage/100,
        width = $(img).width(),
        height = $(img).height();

    return {"width": width*coeff, "height": height*coeff}           

$('.user-gravatar32 img').each(function(){
  var newDimensions = resizeImage( this, 150); = newDimensions.width + "px"; = newDimensions.height + "px";

Fast canvas resample with good quality:

Update: version 2.0 (faster, web workers + transferable objects) -

 * Hermite resize - fast image resize/resample using Hermite filter. 1 cpu version!
 * @param {HtmlElement} canvas
 * @param {int} width
 * @param {int} height
 * @param {boolean} resize_canvas if true, canvas will be resized. Optional.
function resample_single(canvas, width, height, resize_canvas) {
    var width_source = canvas.width;
    var height_source = canvas.height;
    width = Math.round(width);
    height = Math.round(height);

    var ratio_w = width_source / width;
    var ratio_h = height_source / height;
    var ratio_w_half = Math.ceil(ratio_w / 2);
    var ratio_h_half = Math.ceil(ratio_h / 2);

    var ctx = canvas.getContext("2d");
    var img = ctx.getImageData(0, 0, width_source, height_source);
    var img2 = ctx.createImageData(width, height);
    var data =;
    var data2 =;

    for (var j = 0; j < height; j++) {
        for (var i = 0; i < width; i++) {
            var x2 = (i + j * width) * 4;
            var weight = 0;
            var weights = 0;
            var weights_alpha = 0;
            var gx_r = 0;
            var gx_g = 0;
            var gx_b = 0;
            var gx_a = 0;
            var center_y = (j + 0.5) * ratio_h;
            var yy_start = Math.floor(j * ratio_h);
            var yy_stop = Math.ceil((j + 1) * ratio_h);
            for (var yy = yy_start; yy < yy_stop; yy++) {
                var dy = Math.abs(center_y - (yy + 0.5)) / ratio_h_half;
                var center_x = (i + 0.5) * ratio_w;
                var w0 = dy * dy; //pre-calc part of w
                var xx_start = Math.floor(i * ratio_w);
                var xx_stop = Math.ceil((i + 1) * ratio_w);
                for (var xx = xx_start; xx < xx_stop; xx++) {
                    var dx = Math.abs(center_x - (xx + 0.5)) / ratio_w_half;
                    var w = Math.sqrt(w0 + dx * dx);
                    if (w >= 1) {
                        //pixel too far
                    //hermite filter
                    weight = 2 * w * w * w - 3 * w * w + 1;
                    var pos_x = 4 * (xx + yy * width_source);
                    gx_a += weight * data[pos_x + 3];
                    weights_alpha += weight;
                    if (data[pos_x + 3] < 255)
                        weight = weight * data[pos_x + 3] / 250;
                    gx_r += weight * data[pos_x];
                    gx_g += weight * data[pos_x + 1];
                    gx_b += weight * data[pos_x + 2];
                    weights += weight;
            data2[x2] = gx_r / weights;
            data2[x2 + 1] = gx_g / weights;
            data2[x2 + 2] = gx_b / weights;
            data2[x2 + 3] = gx_a / weights_alpha;
    //clear and resize canvas
    if (resize_canvas === true) {
        canvas.width = width;
        canvas.height = height;
    } else {
        ctx.clearRect(0, 0, width_source, height_source);

    ctx.putImageData(img2, 0, 0);

If you wish to use canvas only, the best result will be with multiple downsteps. But that's not good enougth yet. For better quality you need pure js implementation. We just released pica - high speed downscaler with variable quality/speed. In short, it resizes 1280*1024px in ~0.1s, and 5000*3000px image in 1s, with highest quality (lanczos filter with 3 lobes). Pica has demo, where you can play with your images, quality levels, and even try it on mobile devices.

Pica does not have unsharp mask yet, but that will be added very soon. That's much more easy than implement high speed convolution filter for resize.

Suggestion 1 - extend the process pipe-line

You can use step-down as I describe in the links you refer to but you appear to use them in a wrong way.

Step down is not needed to scale images to ratios above 1:2 (typically, but not limited to). It is where you need to do a drastic down-scaling you need to split it up in two (and rarely, more) steps depending on content of the image (in particular where high-frequencies such as thin lines occur).

Every time you down-sample an image you will loose details and information. You cannot expect the resulting image to be as clear as the original.

If you are then scaling down the images in many steps you will loose a lot of information in total and the result will be poor as you already noticed.

Try with just one extra step, or at tops two.


In case of Photoshop notice that it applies a convolution after the image has been re-sampled, such as sharpen. It's not just bi-cubic interpolation that takes place so in order to fully emulate Photoshop we need to also add the steps Photoshop is doing (with the default setup).

For this example I will use my original answer that you refer to in your post, but I have added a sharpen convolution to it to improve quality as a post process (see demo at bottom).

Here is code for adding sharpen filter (it's based on a generic convolution filter - I put the weight matrix for sharpen inside it as well as a mix factor to adjust the pronunciation of the effect):


sharpen(context, width, height, mixFactor);

The mixFactor is a value between [0.0, 1.0] and allow you do downplay the sharpen effect - rule-of-thumb: the less size the less of the effect is needed.

Function (based on this snippet):

function sharpen(ctx, w, h, mix) {

    var weights =  [0, -1, 0,  -1, 5, -1,  0, -1, 0],
        katet = Math.round(Math.sqrt(weights.length)),
        half = (katet * 0.5) |0,
        dstData = ctx.createImageData(w, h),
        dstBuff =,
        srcBuff = ctx.getImageData(0, 0, w, h).data,
        y = h;

    while(y--) {

        x = w;

        while(x--) {

            var sy = y,
                sx = x,
                dstOff = (y * w + x) * 4,
                r = 0, g = 0, b = 0, a = 0;

            for (var cy = 0; cy < katet; cy++) {
                for (var cx = 0; cx < katet; cx++) {

                    var scy = sy + cy - half;
                    var scx = sx + cx - half;

                    if (scy >= 0 && scy < h && scx >= 0 && scx < w) {

                        var srcOff = (scy * w + scx) * 4;
                        var wt = weights[cy * katet + cx];

                        r += srcBuff[srcOff] * wt;
                        g += srcBuff[srcOff + 1] * wt;
                        b += srcBuff[srcOff + 2] * wt;
                        a += srcBuff[srcOff + 3] * wt;

            dstBuff[dstOff] = r * mix + srcBuff[dstOff] * (1 - mix);
            dstBuff[dstOff + 1] = g * mix + srcBuff[dstOff + 1] * (1 - mix);
            dstBuff[dstOff + 2] = b * mix + srcBuff[dstOff + 2] * (1 - mix)
            dstBuff[dstOff + 3] = srcBuff[dstOff + 3];

    ctx.putImageData(dstData, 0, 0);

The result of using this combination will be:


Depending on how much of the sharpening you want to add to the blend you can get result from default "blurry" to very sharp:

Suggestion 2 - low level algorithm implementation

If you want to get the best result quality-wise you'll need to go low-level and consider to implement for example this brand new algorithm to do this.

See Interpolation-Dependent Image Downsampling (2011) from IEEE.
Here is a link to the paper in full (PDF).

There are no implementations of this algorithm in JavaScript AFAIK of at this time so you're in for a hand-full if you want to throw yourself at this task.

The essence is (excerpts from the paper):


An interpolation oriented adaptive down-sampling algorithm is proposed for low bit-rate image coding in this paper. Given an image, the proposed algorithm is able to obtain a low resolution image, from which a high quality image with the same resolution as the input image can be interpolated. Different from the traditional down-sampling algorithms, which are independent from the interpolation process, the proposed down-sampling algorithm hinges the down-sampling to the interpolation process. Consequently, the proposed down-sampling algorithm is able to maintain the original information of the input image to the largest extent. The down-sampled image is then fed into JPEG. A total variation (TV) based post processing is then applied to the decompressed low resolution image. Ultimately, the processed image is interpolated to maintain the original resolution of the input image. Experimental results verify that utilizing the downsampled image by the proposed algorithm, an interpolated image with much higher quality can be achieved. Besides, the proposed algorithm is able to achieve superior performance than JPEG for low bit rate image coding.

(see provided link for all details, formulas etc.)

Just my 2 cents regarding the divs option.

Famous/Infamous and SamsaraJS (and possibly others) use absolutely positioned non-nested divs (with non-trivial HTML/CSS content), combined with matrix2d/matrix3d for positioning and 2D/3D transformations, and achieve a stable 60FPS on moderate mobile hardware, so I'd argue against divs being a slow option.

There are plenty of screen recordings on Youtube and elsewhere, of high-performance 2D/3D stuff running in the browser with everything being an DOM element which you can Inspect Element on, at 60FPS (mixed with WebGL for certain effects, but not for the main part of the rendering).

javascript css html5 canvas html5-canvas