ref: f38616e1a21a64617c790f26a638097ff0e5ad3d
dir: /vpx_dsp/ssim.c/
/* * Copyright (c) 2010 The WebM project authors. All Rights Reserved. * * Use of this source code is governed by a BSD-style license * that can be found in the LICENSE file in the root of the source * tree. An additional intellectual property rights grant can be found * in the file PATENTS. All contributing project authors may * be found in the AUTHORS file in the root of the source tree. */ #include <assert.h> #include <math.h> #include "./vpx_dsp_rtcd.h" #include "vpx_dsp/ssim.h" #include "vpx_ports/mem.h" #include "vpx_ports/system_state.h" void vpx_ssim_parms_16x16_c(const uint8_t *s, int sp, const uint8_t *r, int rp, uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s, uint32_t *sum_sq_r, uint32_t *sum_sxr) { int i, j; for (i = 0; i < 16; i++, s += sp, r += rp) { for (j = 0; j < 16; j++) { *sum_s += s[j]; *sum_r += r[j]; *sum_sq_s += s[j] * s[j]; *sum_sq_r += r[j] * r[j]; *sum_sxr += s[j] * r[j]; } } } void vpx_ssim_parms_8x8_c(const uint8_t *s, int sp, const uint8_t *r, int rp, uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s, uint32_t *sum_sq_r, uint32_t *sum_sxr) { int i, j; for (i = 0; i < 8; i++, s += sp, r += rp) { for (j = 0; j < 8; j++) { *sum_s += s[j]; *sum_r += r[j]; *sum_sq_s += s[j] * s[j]; *sum_sq_r += r[j] * r[j]; *sum_sxr += s[j] * r[j]; } } } #if CONFIG_VP9_HIGHBITDEPTH void vpx_highbd_ssim_parms_8x8_c(const uint16_t *s, int sp, const uint16_t *r, int rp, uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s, uint32_t *sum_sq_r, uint32_t *sum_sxr) { int i, j; for (i = 0; i < 8; i++, s += sp, r += rp) { for (j = 0; j < 8; j++) { *sum_s += s[j]; *sum_r += r[j]; *sum_sq_s += s[j] * s[j]; *sum_sq_r += r[j] * r[j]; *sum_sxr += s[j] * r[j]; } } } #endif // CONFIG_VP9_HIGHBITDEPTH static const int64_t cc1 = 26634; // (64^2*(.01*255)^2 static const int64_t cc2 = 239708; // (64^2*(.03*255)^2 static const int64_t cc1_10 = 428658; // (64^2*(.01*1023)^2 static const int64_t cc2_10 = 3857925; // (64^2*(.03*1023)^2 static const int64_t cc1_12 = 6868593; // (64^2*(.01*4095)^2 static const int64_t cc2_12 = 61817334; // (64^2*(.03*4095)^2 static double similarity(uint32_t sum_s, uint32_t sum_r, uint32_t sum_sq_s, uint32_t sum_sq_r, uint32_t sum_sxr, int count, uint32_t bd) { int64_t ssim_n, ssim_d; int64_t c1, c2; if (bd == 8) { // scale the constants by number of pixels c1 = (cc1 * count * count) >> 12; c2 = (cc2 * count * count) >> 12; } else if (bd == 10) { c1 = (cc1_10 * count * count) >> 12; c2 = (cc2_10 * count * count) >> 12; } else if (bd == 12) { c1 = (cc1_12 * count * count) >> 12; c2 = (cc2_12 * count * count) >> 12; } else { c1 = c2 = 0; assert(0); } ssim_n = (2 * sum_s * sum_r + c1) * ((int64_t)2 * count * sum_sxr - (int64_t)2 * sum_s * sum_r + c2); ssim_d = (sum_s * sum_s + sum_r * sum_r + c1) * ((int64_t)count * sum_sq_s - (int64_t)sum_s * sum_s + (int64_t)count * sum_sq_r - (int64_t)sum_r * sum_r + c2); return ssim_n * 1.0 / ssim_d; } static double ssim_8x8(const uint8_t *s, int sp, const uint8_t *r, int rp) { uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0; vpx_ssim_parms_8x8(s, sp, r, rp, &sum_s, &sum_r, &sum_sq_s, &sum_sq_r, &sum_sxr); return similarity(sum_s, sum_r, sum_sq_s, sum_sq_r, sum_sxr, 64, 8); } #if CONFIG_VP9_HIGHBITDEPTH static double highbd_ssim_8x8(const uint16_t *s, int sp, const uint16_t *r, int rp, uint32_t bd, uint32_t shift) { uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0; vpx_highbd_ssim_parms_8x8(s, sp, r, rp, &sum_s, &sum_r, &sum_sq_s, &sum_sq_r, &sum_sxr); return similarity(sum_s >> shift, sum_r >> shift, sum_sq_s >> (2 * shift), sum_sq_r >> (2 * shift), sum_sxr >> (2 * shift), 64, bd); } #endif // CONFIG_VP9_HIGHBITDEPTH // We are using a 8x8 moving window with starting location of each 8x8 window // on the 4x4 pixel grid. Such arrangement allows the windows to overlap // block boundaries to penalize blocking artifacts. static double vpx_ssim2(const uint8_t *img1, const uint8_t *img2, int stride_img1, int stride_img2, int width, int height) { int i, j; int samples = 0; double ssim_total = 0; // sample point start with each 4x4 location for (i = 0; i <= height - 8; i += 4, img1 += stride_img1 * 4, img2 += stride_img2 * 4) { for (j = 0; j <= width - 8; j += 4) { double v = ssim_8x8(img1 + j, stride_img1, img2 + j, stride_img2); ssim_total += v; samples++; } } ssim_total /= samples; return ssim_total; } #if CONFIG_VP9_HIGHBITDEPTH static double vpx_highbd_ssim2(const uint8_t *img1, const uint8_t *img2, int stride_img1, int stride_img2, int width, int height, uint32_t bd, uint32_t shift) { int i, j; int samples = 0; double ssim_total = 0; // sample point start with each 4x4 location for (i = 0; i <= height - 8; i += 4, img1 += stride_img1 * 4, img2 += stride_img2 * 4) { for (j = 0; j <= width - 8; j += 4) { double v = highbd_ssim_8x8(CONVERT_TO_SHORTPTR(img1 + j), stride_img1, CONVERT_TO_SHORTPTR(img2 + j), stride_img2, bd, shift); ssim_total += v; samples++; } } ssim_total /= samples; return ssim_total; } #endif // CONFIG_VP9_HIGHBITDEPTH double vpx_calc_ssim(const YV12_BUFFER_CONFIG *source, const YV12_BUFFER_CONFIG *dest, double *weight) { double a, b, c; double ssimv; a = vpx_ssim2(source->y_buffer, dest->y_buffer, source->y_stride, dest->y_stride, source->y_crop_width, source->y_crop_height); b = vpx_ssim2(source->u_buffer, dest->u_buffer, source->uv_stride, dest->uv_stride, source->uv_crop_width, source->uv_crop_height); c = vpx_ssim2(source->v_buffer, dest->v_buffer, source->uv_stride, dest->uv_stride, source->uv_crop_width, source->uv_crop_height); ssimv = a * .8 + .1 * (b + c); *weight = 1; return ssimv; } // traditional ssim as per: http://en.wikipedia.org/wiki/Structural_similarity // // Re working out the math -> // // ssim(x,y) = (2*mean(x)*mean(y) + c1)*(2*cov(x,y)+c2) / // ((mean(x)^2+mean(y)^2+c1)*(var(x)+var(y)+c2)) // // mean(x) = sum(x) / n // // cov(x,y) = (n*sum(xi*yi)-sum(x)*sum(y))/(n*n) // // var(x) = (n*sum(xi*xi)-sum(xi)*sum(xi))/(n*n) // // ssim(x,y) = // (2*sum(x)*sum(y)/(n*n) + c1)*(2*(n*sum(xi*yi)-sum(x)*sum(y))/(n*n)+c2) / // (((sum(x)*sum(x)+sum(y)*sum(y))/(n*n) +c1) * // ((n*sum(xi*xi) - sum(xi)*sum(xi))/(n*n)+ // (n*sum(yi*yi) - sum(yi)*sum(yi))/(n*n)+c2))) // // factoring out n*n // // ssim(x,y) = // (2*sum(x)*sum(y) + n*n*c1)*(2*(n*sum(xi*yi)-sum(x)*sum(y))+n*n*c2) / // (((sum(x)*sum(x)+sum(y)*sum(y)) + n*n*c1) * // (n*sum(xi*xi)-sum(xi)*sum(xi)+n*sum(yi*yi)-sum(yi)*sum(yi)+n*n*c2)) // // Replace c1 with n*n * c1 for the final step that leads to this code: // The final step scales by 12 bits so we don't lose precision in the constants. static double ssimv_similarity(const Ssimv *sv, int64_t n) { // Scale the constants by number of pixels. const int64_t c1 = (cc1 * n * n) >> 12; const int64_t c2 = (cc2 * n * n) >> 12; const double l = 1.0 * (2 * sv->sum_s * sv->sum_r + c1) / (sv->sum_s * sv->sum_s + sv->sum_r * sv->sum_r + c1); // Since these variables are unsigned sums, convert to double so // math is done in double arithmetic. const double v = (2.0 * n * sv->sum_sxr - 2 * sv->sum_s * sv->sum_r + c2) / (n * sv->sum_sq_s - sv->sum_s * sv->sum_s + n * sv->sum_sq_r - sv->sum_r * sv->sum_r + c2); return l * v; } // The first term of the ssim metric is a luminance factor. // // (2*mean(x)*mean(y) + c1)/ (mean(x)^2+mean(y)^2+c1) // // This luminance factor is super sensitive to the dark side of luminance // values and completely insensitive on the white side. check out 2 sets // (1,3) and (250,252) the term gives ( 2*1*3/(1+9) = .60 // 2*250*252/ (250^2+252^2) => .99999997 // // As a result in this tweaked version of the calculation in which the // luminance is taken as percentage off from peak possible. // // 255 * 255 - (sum_s - sum_r) / count * (sum_s - sum_r) / count // static double ssimv_similarity2(const Ssimv *sv, int64_t n) { // Scale the constants by number of pixels. const int64_t c1 = (cc1 * n * n) >> 12; const int64_t c2 = (cc2 * n * n) >> 12; const double mean_diff = (1.0 * sv->sum_s - sv->sum_r) / n; const double l = (255 * 255 - mean_diff * mean_diff + c1) / (255 * 255 + c1); // Since these variables are unsigned, sums convert to double so // math is done in double arithmetic. const double v = (2.0 * n * sv->sum_sxr - 2 * sv->sum_s * sv->sum_r + c2) / (n * sv->sum_sq_s - sv->sum_s * sv->sum_s + n * sv->sum_sq_r - sv->sum_r * sv->sum_r + c2); return l * v; } static void ssimv_parms(uint8_t *img1, int img1_pitch, uint8_t *img2, int img2_pitch, Ssimv *sv) { vpx_ssim_parms_8x8(img1, img1_pitch, img2, img2_pitch, &sv->sum_s, &sv->sum_r, &sv->sum_sq_s, &sv->sum_sq_r, &sv->sum_sxr); } double vpx_get_ssim_metrics(uint8_t *img1, int img1_pitch, uint8_t *img2, int img2_pitch, int width, int height, Ssimv *sv2, Metrics *m, int do_inconsistency) { double dssim_total = 0; double ssim_total = 0; double ssim2_total = 0; double inconsistency_total = 0; int i, j; int c = 0; double norm; double old_ssim_total = 0; vpx_clear_system_state(); // We can sample points as frequently as we like start with 1 per 4x4. for (i = 0; i < height; i += 4, img1 += img1_pitch * 4, img2 += img2_pitch * 4) { for (j = 0; j < width; j += 4, ++c) { Ssimv sv = { 0 }; double ssim; double ssim2; double dssim; uint32_t var_new; uint32_t var_old; uint32_t mean_new; uint32_t mean_old; double ssim_new; double ssim_old; // Not sure there's a great way to handle the edge pixels // in ssim when using a window. Seems biased against edge pixels // however you handle this. This uses only samples that are // fully in the frame. if (j + 8 <= width && i + 8 <= height) { ssimv_parms(img1 + j, img1_pitch, img2 + j, img2_pitch, &sv); } ssim = ssimv_similarity(&sv, 64); ssim2 = ssimv_similarity2(&sv, 64); sv.ssim = ssim2; // dssim is calculated to use as an actual error metric and // is scaled up to the same range as sum square error. // Since we are subsampling every 16th point maybe this should be // *16 ? dssim = 255 * 255 * (1 - ssim2) / 2; // Here I introduce a new error metric: consistency-weighted // SSIM-inconsistency. This metric isolates frames where the // SSIM 'suddenly' changes, e.g. if one frame in every 8 is much // sharper or blurrier than the others. Higher values indicate a // temporally inconsistent SSIM. There are two ideas at work: // // 1) 'SSIM-inconsistency': the total inconsistency value // reflects how much SSIM values are changing between this // source / reference frame pair and the previous pair. // // 2) 'consistency-weighted': weights de-emphasize areas in the // frame where the scene content has changed. Changes in scene // content are detected via changes in local variance and local // mean. // // Thus the overall measure reflects how inconsistent the SSIM // values are, over consistent regions of the frame. // // The metric has three terms: // // term 1 -> uses change in scene Variance to weight error score // 2 * var(Fi)*var(Fi-1) / (var(Fi)^2+var(Fi-1)^2) // larger changes from one frame to the next mean we care // less about consistency. // // term 2 -> uses change in local scene luminance to weight error // 2 * avg(Fi)*avg(Fi-1) / (avg(Fi)^2+avg(Fi-1)^2) // larger changes from one frame to the next mean we care // less about consistency. // // term3 -> measures inconsistency in ssim scores between frames // 1 - ( 2 * ssim(Fi)*ssim(Fi-1)/(ssim(Fi)^2+sssim(Fi-1)^2). // // This term compares the ssim score for the same location in 2 // subsequent frames. var_new = sv.sum_sq_s - sv.sum_s * sv.sum_s / 64; var_old = sv2[c].sum_sq_s - sv2[c].sum_s * sv2[c].sum_s / 64; mean_new = sv.sum_s; mean_old = sv2[c].sum_s; ssim_new = sv.ssim; ssim_old = sv2[c].ssim; if (do_inconsistency) { // We do the metric once for every 4x4 block in the image. Since // we are scaling the error to SSE for use in a psnr calculation // 1.0 = 4x4x255x255 the worst error we can possibly have. static const double kScaling = 4. * 4 * 255 * 255; // The constants have to be non 0 to avoid potential divide by 0 // issues other than that they affect kind of a weighting between // the terms. No testing of what the right terms should be has been // done. static const double c1 = 1, c2 = 1, c3 = 1; // This measures how much consistent variance is in two consecutive // source frames. 1.0 means they have exactly the same variance. const double variance_term = (2.0 * var_old * var_new + c1) / (1.0 * var_old * var_old + 1.0 * var_new * var_new + c1); // This measures how consistent the local mean are between two // consecutive frames. 1.0 means they have exactly the same mean. const double mean_term = (2.0 * mean_old * mean_new + c2) / (1.0 * mean_old * mean_old + 1.0 * mean_new * mean_new + c2); // This measures how consistent the ssims of two // consecutive frames is. 1.0 means they are exactly the same. double ssim_term = pow((2.0 * ssim_old * ssim_new + c3) / (ssim_old * ssim_old + ssim_new * ssim_new + c3), 5); double this_inconsistency; // Floating point math sometimes makes this > 1 by a tiny bit. // We want the metric to scale between 0 and 1.0 so we can convert // it to an snr scaled value. if (ssim_term > 1) ssim_term = 1; // This converts the consistency metric to an inconsistency metric // ( so we can scale it like psnr to something like sum square error. // The reason for the variance and mean terms is the assumption that // if there are big changes in the source we shouldn't penalize // inconsistency in ssim scores a bit less as it will be less visible // to the user. this_inconsistency = (1 - ssim_term) * variance_term * mean_term; this_inconsistency *= kScaling; inconsistency_total += this_inconsistency; } sv2[c] = sv; ssim_total += ssim; ssim2_total += ssim2; dssim_total += dssim; old_ssim_total += ssim_old; } old_ssim_total += 0; } norm = 1. / (width / 4) / (height / 4); ssim_total *= norm; ssim2_total *= norm; m->ssim2 = ssim2_total; m->ssim = ssim_total; if (old_ssim_total == 0) inconsistency_total = 0; m->ssimc = inconsistency_total; m->dssim = dssim_total; return inconsistency_total; } #if CONFIG_VP9_HIGHBITDEPTH double vpx_highbd_calc_ssim(const YV12_BUFFER_CONFIG *source, const YV12_BUFFER_CONFIG *dest, double *weight, uint32_t bd, uint32_t in_bd) { double a, b, c; double ssimv; uint32_t shift = 0; assert(bd >= in_bd); shift = bd - in_bd; a = vpx_highbd_ssim2(source->y_buffer, dest->y_buffer, source->y_stride, dest->y_stride, source->y_crop_width, source->y_crop_height, in_bd, shift); b = vpx_highbd_ssim2(source->u_buffer, dest->u_buffer, source->uv_stride, dest->uv_stride, source->uv_crop_width, source->uv_crop_height, in_bd, shift); c = vpx_highbd_ssim2(source->v_buffer, dest->v_buffer, source->uv_stride, dest->uv_stride, source->uv_crop_width, source->uv_crop_height, in_bd, shift); ssimv = a * .8 + .1 * (b + c); *weight = 1; return ssimv; } #endif // CONFIG_VP9_HIGHBITDEPTH