libvpx: Pull from upstream

source/libvpx/vp9/encoder/vp9_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 <math.h>
 #include "./vp9_rtcd.h"
-
 #include "vp9/encoder/vp9_ssim.h"
 
 void vp9_ssim_parms_16x16_c(uint8_t *s, int sp, uint8_t *r,
                             int rp, unsigned long *sum_s, unsigned long *sum_r,
                             unsigned long *sum_sq_s, unsigned long *sum_sq_r,
                             unsigned long *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];
@@ skipping 171 matching lines @@
                 source->uv_stride, dest->uv_stride,
                 source->uv_crop_width, source->uv_crop_height);
   *ssim_y = a;
   *ssim_u = b;
   *ssim_v = c;
   ssim_all = (a * 4 + b + c) / 6;
 
   return ssim_all;
 }
 
+// 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.
+
+double ssimv_similarity(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
+//
+double ssimv_similarity2(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;
+}
+void ssimv_parms(uint8_t *img1, int img1_pitch, uint8_t *img2, int img2_pitch,
+                 Ssimv *sv) {
+  vp9_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 vp9_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;
+  vp9_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 vp9_highbd_calc_ssim(YV12_BUFFER_CONFIG *source,
                             YV12_BUFFER_CONFIG *dest,
                             double *weight, unsigned int bd) {
   double a, b, c;
   double ssimv;
 
   a = vp9_highbd_ssim2(source->y_buffer, dest->y_buffer,
                        source->y_stride, dest->y_stride,
                        source->y_crop_width, source->y_crop_height, bd);
@@ skipping 31 matching lines @@
                        source->uv_stride, dest->uv_stride,
                        source->uv_crop_width, source->uv_crop_height, bd);
   *ssim_y = a;
   *ssim_u = b;
   *ssim_v = c;
   ssim_all = (a * 4 + b + c) / 6;
 
   return ssim_all;
 }
 #endif  // CONFIG_VP9_HIGHBITDEPTH