1/*
2 *  Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
3 *
4 *  Use of this source code is governed by a BSD-style license
5 *  that can be found in the LICENSE file in the root of the source
6 *  tree.
7 */
8
9/* More information about these options at jshint.com/docs/options */
10'use strict';
11
12/* This is an implementation of the algorithm for calculating the Structural
13 * SIMilarity (SSIM) index between two images. Please refer to the article [1],
14 * the website [2] and/or the Wikipedia article [3]. This code takes the value
15 * of the constants C1 and C2 from the Matlab implementation in [4].
16 *
17 * [1] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality
18 * assessment: From error measurement to structural similarity",
19 * IEEE Transactions on Image Processing, vol. 13, no. 1, Jan. 2004.
20 * [2] http://www.cns.nyu.edu/~lcv/ssim/
21 * [3] http://en.wikipedia.org/wiki/Structural_similarity
22 * [4] http://www.cns.nyu.edu/~lcv/ssim/ssim_index.m
23 */
24
25function Ssim() {}
26
27Ssim.prototype = {
28  // Implementation of Eq.2, a simple average of a vector and Eq.4., except the
29  // square root. The latter is actually an unbiased estimate of the variance,
30  // not the exact variance.
31  statistics: function(a) {
32    var accu = 0;
33    var i;
34    for (i = 0; i < a.length; ++i) {
35      accu += a[i];
36    }
37    var meanA = accu / (a.length - 1);
38    var diff = 0;
39    for (i = 1; i < a.length; ++i) {
40      diff = a[i - 1] - meanA;
41      accu += a[i] + (diff * diff);
42    }
43    return {mean : meanA, variance : accu / a.length};
44  },
45
46  // Implementation of Eq.11., cov(Y, Z) = E((Y - uY), (Z - uZ)).
47  covariance: function(a, b, meanA, meanB) {
48    var accu = 0;
49    for (var i = 0; i < a.length; i += 1) {
50      accu += (a[i] - meanA) * (b[i] - meanB);
51    }
52    return accu / a.length;
53  },
54
55  calculate: function(x, y) {
56    if (x.length !== y.length) {
57      return 0;
58    }
59
60    // Values of the constants come from the Matlab code referred before.
61    var K1 = 0.01;
62    var K2 = 0.03;
63    var L = 255;
64    var C1 = (K1 * L) * (K1 * L);
65    var C2 = (K2 * L) * (K2 * L);
66    var C3 = C2 / 2;
67
68    var statsX = this.statistics(x);
69    var muX = statsX.mean;
70    var sigmaX2 = statsX.variance;
71    var sigmaX = Math.sqrt(sigmaX2);
72    var statsY = this.statistics(y);
73    var muY = statsY.mean;
74    var sigmaY2 = statsY.variance;
75    var sigmaY = Math.sqrt(sigmaY2);
76    var sigmaXy = this.covariance(x, y, muX, muY);
77
78    // Implementation of Eq.6.
79    var luminance = (2 * muX * muY + C1) /
80        ((muX * muX) + (muY * muY) + C1);
81    // Implementation of Eq.10.
82    var structure = (sigmaXy + C3) / (sigmaX * sigmaY + C3);
83    // Implementation of Eq.9.
84    var contrast = (2 * sigmaX * sigmaY + C2) / (sigmaX2 + sigmaY2 + C2);
85
86    // Implementation of Eq.12.
87    return luminance * contrast * structure;
88  }
89};
90