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"use strict";
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.FastStats = void 0;
exports.ssim = ssim;
/**
* Copyright (c) Microsoft Corporation.
*
* Licensed under the Apache License, Version 2.0 (the 'License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Image channel has a 8-bit depth.
const DYNAMIC_RANGE = 2 ** 8 - 1;
function ssim(stats, x1, y1, x2, y2) {
const mean1 = stats.meanC1(x1, y1, x2, y2);
const mean2 = stats.meanC2(x1, y1, x2, y2);
const var1 = stats.varianceC1(x1, y1, x2, y2);
const var2 = stats.varianceC2(x1, y1, x2, y2);
const cov = stats.covariance(x1, y1, x2, y2);
const c1 = (0.01 * DYNAMIC_RANGE) ** 2;
const c2 = (0.03 * DYNAMIC_RANGE) ** 2;
return (2 * mean1 * mean2 + c1) * (2 * cov + c2) / (mean1 ** 2 + mean2 ** 2 + c1) / (var1 + var2 + c2);
}
class FastStats {
constructor(c1, c2) {
this.c1 = void 0;
this.c2 = void 0;
this._partialSumC1 = void 0;
this._partialSumC2 = void 0;
this._partialSumMult = void 0;
this._partialSumSq1 = void 0;
this._partialSumSq2 = void 0;
this.c1 = c1;
this.c2 = c2;
const {
width,
height
} = c1;
this._partialSumC1 = new Array(width * height);
this._partialSumC2 = new Array(width * height);
this._partialSumSq1 = new Array(width * height);
this._partialSumSq2 = new Array(width * height);
this._partialSumMult = new Array(width * height);
const recalc = (mx, idx, initial, x, y) => {
mx[idx] = initial;
if (y > 0) mx[idx] += mx[(y - 1) * width + x];
if (x > 0) mx[idx] += mx[y * width + x - 1];
if (x > 0 && y > 0) mx[idx] -= mx[(y - 1) * width + x - 1];
};
for (let y = 0; y < height; ++y) {
for (let x = 0; x < width; ++x) {
const idx = y * width + x;
recalc(this._partialSumC1, idx, this.c1.data[idx], x, y);
recalc(this._partialSumC2, idx, this.c2.data[idx], x, y);
recalc(this._partialSumSq1, idx, this.c1.data[idx] * this.c1.data[idx], x, y);
recalc(this._partialSumSq2, idx, this.c2.data[idx] * this.c2.data[idx], x, y);
recalc(this._partialSumMult, idx, this.c1.data[idx] * this.c2.data[idx], x, y);
}
}
}
_sum(partialSum, x1, y1, x2, y2) {
const width = this.c1.width;
let result = partialSum[y2 * width + x2];
if (y1 > 0) result -= partialSum[(y1 - 1) * width + x2];
if (x1 > 0) result -= partialSum[y2 * width + x1 - 1];
if (x1 > 0 && y1 > 0) result += partialSum[(y1 - 1) * width + x1 - 1];
return result;
}
meanC1(x1, y1, x2, y2) {
const N = (y2 - y1 + 1) * (x2 - x1 + 1);
return this._sum(this._partialSumC1, x1, y1, x2, y2) / N;
}
meanC2(x1, y1, x2, y2) {
const N = (y2 - y1 + 1) * (x2 - x1 + 1);
return this._sum(this._partialSumC2, x1, y1, x2, y2) / N;
}
varianceC1(x1, y1, x2, y2) {
const N = (y2 - y1 + 1) * (x2 - x1 + 1);
return (this._sum(this._partialSumSq1, x1, y1, x2, y2) - this._sum(this._partialSumC1, x1, y1, x2, y2) ** 2 / N) / N;
}
varianceC2(x1, y1, x2, y2) {
const N = (y2 - y1 + 1) * (x2 - x1 + 1);
return (this._sum(this._partialSumSq2, x1, y1, x2, y2) - this._sum(this._partialSumC2, x1, y1, x2, y2) ** 2 / N) / N;
}
covariance(x1, y1, x2, y2) {
const N = (y2 - y1 + 1) * (x2 - x1 + 1);
return (this._sum(this._partialSumMult, x1, y1, x2, y2) - this._sum(this._partialSumC1, x1, y1, x2, y2) * this._sum(this._partialSumC2, x1, y1, x2, y2) / N) / N;
}
}
exports.FastStats = FastStats;