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Diffstat (limited to 'third_party/aom/aom_dsp/ssim.c')
| -rw-r--r-- | third_party/aom/aom_dsp/ssim.c | 439 |
1 files changed, 439 insertions, 0 deletions
diff --git a/third_party/aom/aom_dsp/ssim.c b/third_party/aom/aom_dsp/ssim.c new file mode 100644 index 0000000000..681770ba97 --- /dev/null +++ b/third_party/aom/aom_dsp/ssim.c @@ -0,0 +1,439 @@ +/* + * Copyright (c) 2016, Alliance for Open Media. All rights reserved + * + * This source code is subject to the terms of the BSD 2 Clause License and + * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License + * was not distributed with this source code in the LICENSE file, you can + * obtain it at www.aomedia.org/license/software. If the Alliance for Open + * Media Patent License 1.0 was not distributed with this source code in the + * PATENTS file, you can obtain it at www.aomedia.org/license/patent. + */ + +#include <assert.h> +#include <math.h> + +#include "config/aom_dsp_rtcd.h" + +#include "aom_dsp/ssim.h" +#include "aom_ports/mem.h" +#include "aom_ports/system_state.h" + +void aom_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 aom_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]; + } + } +} + +void aom_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]; + } + } +} + +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; + aom_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); +} + +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; + aom_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); +} + +// 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 aom_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; +} + +static double aom_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; +} + +double aom_calc_ssim(const YV12_BUFFER_CONFIG *source, + const YV12_BUFFER_CONFIG *dest, double *weight) { + double abc[3]; + for (int i = 0; i < 3; ++i) { + const int is_uv = i > 0; + abc[i] = aom_ssim2(source->buffers[i], dest->buffers[i], + source->strides[is_uv], dest->strides[is_uv], + source->crop_widths[is_uv], source->crop_heights[is_uv]); + } + + *weight = 1; + return abc[0] * .8 + .1 * (abc[1] + abc[2]); +} + +// 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) { + aom_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 aom_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; + aom_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, 0, 0, 0, 0, 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; +} + +double aom_highbd_calc_ssim(const YV12_BUFFER_CONFIG *source, + const YV12_BUFFER_CONFIG *dest, double *weight, + uint32_t bd, uint32_t in_bd) { + assert(bd >= in_bd); + const uint32_t shift = bd - in_bd; + + double abc[3]; + for (int i = 0; i < 3; ++i) { + const int is_uv = i > 0; + abc[i] = aom_highbd_ssim2(source->buffers[i], dest->buffers[i], + source->strides[is_uv], dest->strides[is_uv], + source->crop_widths[is_uv], + source->crop_heights[is_uv], in_bd, shift); + } + + *weight = 1; + return abc[0] * .8 + .1 * (abc[1] + abc[2]); +} |