ref: 189585564142fbf4f37843a21341a6a08914fb61
parent: 61fcdc382107a171d396531b8aa907348c594811
parent: 850e014a8e600aaee80662892a4c3bf5dece21a6
author: Johann Koenig <johannkoenig@google.com>
date: Thu Dec 20 18:12:17 EST 2018
Merge "tiny_ssim.c: resolve missing declarations"
--- a/tools/tiny_ssim.c
+++ b/tools/tiny_ssim.c
@@ -181,24 +181,10 @@
return r1;
}
-void ssim_parms_16x16(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) {+static void ssim_parms_8x8(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 ssim_parms_8x8(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;
if (s == NULL || r == NULL || sum_s == NULL || sum_r == NULL ||
sum_sq_s == NULL || sum_sq_r == NULL || sum_sxr == NULL) {assert(0);
@@ -215,9 +201,11 @@
}
}
-void highbd_ssim_parms_8x8(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) {+#if CONFIG_VP9_HIGHBITDEPTH
+static void highbd_ssim_parms_8x8(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;
if (s == NULL || r == NULL || sum_s == NULL || sum_r == NULL ||
sum_sq_s == NULL || sum_sq_r == NULL || sum_sxr == NULL) {@@ -234,6 +222,7 @@
}
}
}
+#endif // CONFIG_VP9_HIGHBITDEPTH
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,
@@ -325,249 +314,6 @@
return ssim_total;
}
#endif // CONFIG_VP9_HIGHBITDEPTH
-
-// 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) {- 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 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;
-
- // 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;
-}
int main(int argc, char *argv[]) {FILE *framestats = NULL;