add 10 and 12 bit to tiny_ssim

Change-Id: I92e4dba2d1682a0d77ad9a214ec4312b1cf4d42e

tools.mk

@@ -13,6 +13,8 @@ TOOLS-yes            += tiny_ssim.c
 tiny_ssim.SRCS       += vpx/vpx_integer.h y4minput.c y4minput.h \
                         vpx/vpx_codec.h vpx/src/vpx_image.c
 tiny_ssim.SRCS       += vpx_mem/vpx_mem.c vpx_mem/vpx_mem.h
+tiny_ssim.SRCS       += vpx_dsp/ssim.h vpx_scale/yv12config.h
+tiny_ssim.SRCS       += vpx_ports/mem.h vpx_ports/mem.h
 tiny_ssim.SRCS       += vpx_mem/include/vpx_mem_intrnl.h
 tiny_ssim.GUID        = 3afa9b05-940b-4d68-b5aa-55157d8ed7b4
 tiny_ssim.DESCRIPTION = Generate SSIM/PSNR from raw .yuv files

tools/tiny_ssim.c

@@ -8,6 +8,7 @@
  *  be found in the AUTHORS file in the root of the source tree.
  */
 
+#include <assert.h>
 #include <errno.h>
 #include <math.h>
 #include <stdio.h>
@@ -16,72 +17,36 @@
 #include "vpx/vpx_codec.h"
 #include "vpx/vpx_integer.h"
 #include "./y4minput.h"
-
+#include "vpx_dsp/ssim.h"
-static void ssim_parms_8x8(unsigned char *s, int sp, unsigned char *r, int rp,
+#include "vpx_ports/mem.h"
-                           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,
+#if CONFIG_VP9_HIGHBITDEPTH
-                         uint32_t sum_sq_r, uint32_t sum_sxr, int count) {
+static uint64_t calc_plane_error16(uint16_t *orig, int orig_stride,
-  int64_t ssim_n, ssim_d;
+                                   uint16_t *recon, int recon_stride,
-  int64_t c1, c2;
+                                   unsigned int cols, unsigned int rows) {
+  unsigned int row, col;
+  uint64_t total_sse = 0;
+  int diff;
 
-  // scale the constants by number of pixels
+  for (row = 0; row < rows; row++) {
-  c1 = (cc1 * count * count) >> 12;
+    for (col = 0; col < cols; col++) {
-  c2 = (cc2 * count * count) >> 12;
+      diff = orig[col] - recon[col];
-
+      total_sse += diff * diff;
-  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(unsigned char *s, int sp, unsigned char *r, int rp) {
+    orig += orig_stride;
-  uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0;
+    recon += recon_stride;
-  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);
   }
-
+  return total_sse;
-// 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 ssim2(unsigned char *img1, unsigned char *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++;
 }
-  }
+#endif
-  ssim_total /= samples;
-  return ssim_total;
-}
-
 static uint64_t calc_plane_error(uint8_t *orig, int orig_stride, uint8_t *recon,
                                  int recon_stride, unsigned int cols,
                                  unsigned int rows) {
@@ -125,11 +90,12 @@ typedef struct input_file {
   vpx_image_t img;
   int w;
   int h;
+  int bit_depth;
 } input_file_t;
 
 // Open a file and determine if its y4m or raw.  If y4m get the header.
 static int open_input_file(const char *file_name, input_file_t *input, int w,
-                           int h) {
+                           int h, int bit_depth) {
   char y4m_buf[4];
   size_t r1;
   input->type = RAW_YUV;
@@ -144,6 +110,7 @@ static int open_input_file(const char *file_name, input_file_t *input, int w,
         y4m_input_open(&input->y4m, input->file, y4m_buf, 4, 0);
         input->w = input->y4m.pic_w;
         input->h = input->y4m.pic_h;
+        input->bit_depth = input->y4m.bit_depth;
         // Y4M alloc's its own buf. Init this to avoid problems if we never
         // read frames.
         memset(&input->img, 0, sizeof(input->img));
@@ -152,7 +119,10 @@ static int open_input_file(const char *file_name, input_file_t *input, int w,
         fseek(input->file, 0, SEEK_SET);
         input->w = w;
         input->h = h;
+        if (bit_depth < 9)
           input->buf = malloc(w * h * 3 / 2);
+        else
+          input->buf = malloc(w * h * 3);
         break;
     }
   }
@@ -169,7 +139,7 @@ static void close_input_file(input_file_t *in) {
 }
 
 static size_t read_input_file(input_file_t *in, unsigned char **y,
-                              unsigned char **u, unsigned char **v) {
+                              unsigned char **u, unsigned char **v, int bd) {
   size_t r1 = 0;
   switch (in->type) {
     case Y4M:
@@ -179,18 +149,429 @@ static size_t read_input_file(input_file_t *in, unsigned char **y,
       *v = in->img.planes[2];
       break;
     case RAW_YUV:
+      if (bd < 9) {
         r1 = fread(in->buf, in->w * in->h * 3 / 2, 1, in->file);
         *y = in->buf;
         *u = in->buf + in->w * in->h;
         *v = in->buf + 5 * in->w * in->h / 4;
+      } else {
+        r1 = fread(in->buf, in->w * in->h * 3, 1, in->file);
+        *y = in->buf;
+        *u = in->buf + in->w * in->h / 2;
+        *v = *u + in->w * in->h / 2;
+      }
       break;
   }
 
   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) {
+  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;
+  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 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;
+  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 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 = 0, c2 = 0;
+  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 {
+    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;
+  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;
+  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 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 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;
+}
+
+// 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 };
+      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 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 = 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 = 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 = 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;
+}
+
 int main(int argc, char *argv[]) {
   FILE *framestats = NULL;
+  int bit_depth = 8;
   int w = 0, h = 0, tl_skip = 0, tl_skips_remaining = 0;
   double ssimavg = 0, ssimyavg = 0, ssimuavg = 0, ssimvavg = 0;
   double psnrglb = 0, psnryglb = 0, psnruglb = 0, psnrvglb = 0;
@@ -200,11 +581,12 @@ int main(int argc, char *argv[]) {
   size_t i, n_frames = 0, allocated_frames = 0;
   int return_value = 0;
   input_file_t in[2];
+  double peak = 255.0;
 
   if (argc < 2) {
     fprintf(stderr,
             "Usage: %s file1.{yuv|y4m} file2.{yuv|y4m}"
-            "[WxH tl_skip={0,1,3}]\n",
+            "[WxH tl_skip={0,1,3} frame_stats_file bits]\n",
             argv[0]);
     return_value = 1;
     goto clean_up;
@@ -214,7 +596,11 @@ int main(int argc, char *argv[]) {
     sscanf(argv[3], "%dx%d", &w, &h);
   }
 
-  if (open_input_file(argv[1], &in[0], w, h) < 0) {
+  if (argc > 6) {
+    sscanf(argv[6], "%d", &bit_depth);
+  }
+
+  if (open_input_file(argv[1], &in[0], w, h, bit_depth) < 0) {
     fprintf(stderr, "File %s can't be opened or parsed!\n", argv[2]);
     goto clean_up;
   }
@@ -223,9 +609,13 @@ int main(int argc, char *argv[]) {
     // If a y4m is the first file and w, h is not set grab from first file.
     w = in[0].w;
     h = in[0].h;
+    bit_depth = in[0].bit_depth;
   }
+  if (bit_depth == 10) peak = 1023.0;
 
-  if (open_input_file(argv[2], &in[1], w, h) < 0) {
+  if (bit_depth == 12) peak = 4095;
+
+  if (open_input_file(argv[2], &in[1], w, h, bit_depth) < 0) {
     fprintf(stderr, "File %s can't be opened or parsed!\n", argv[2]);
     goto clean_up;
   }
@@ -264,7 +654,7 @@ int main(int argc, char *argv[]) {
     size_t r1, r2;
     unsigned char *y[2], *u[2], *v[2];
 
-    r1 = read_input_file(&in[0], &y[0], &u[0], &v[0]);
+    r1 = read_input_file(&in[0], &y[0], &u[0], &v[0], bit_depth);
 
     if (r1) {
       // Reading parts of file1.yuv that were not used in temporal layer.
@@ -276,7 +666,7 @@ int main(int argc, char *argv[]) {
       tl_skips_remaining = tl_skip;
     }
 
-    r2 = read_input_file(&in[1], &y[1], &u[1], &v[1]);
+    r2 = read_input_file(&in[1], &y[1], &u[1], &v[1], bit_depth);
 
     if (r1 && r2 && r1 != r2) {
       fprintf(stderr, "Failed to read data: %s [%d/%d]\n", strerror(errno),
@@ -286,9 +676,22 @@ int main(int argc, char *argv[]) {
     } else if (r1 == 0 || r2 == 0) {
       break;
     }
+#if CONFIG_VP9_HIGHBITDEPTH
+#define psnr_and_ssim(ssim, psnr, buf0, buf1, w, h)                            \
+  if (bit_depth < 9) {                                                         \
+    ssim = ssim2(buf0, buf1, w, w, w, h);                                      \
+    psnr = calc_plane_error(buf0, w, buf1, w, w, h);                           \
+  } else {                                                                     \
+    ssim = highbd_ssim2(CONVERT_TO_BYTEPTR(buf0), CONVERT_TO_BYTEPTR(buf1), w, \
+                        w, w, h, bit_depth, bit_depth - 8);                    \
+    psnr = calc_plane_error16(CAST_TO_SHORTPTR(buf0), w,                       \
+                              CAST_TO_SHORTPTR(buf1), w, w, h);                \
+  }
+#else
 #define psnr_and_ssim(ssim, psnr, buf0, buf1, w, h) \
   ssim = ssim2(buf0, buf1, w, w, w, h);             \
   psnr = calc_plane_error(buf0, w, buf1, w, w, h);
+#endif
 
     if (n_frames == allocated_frames) {
       allocated_frames = allocated_frames == 0 ? 1024 : allocated_frames * 2;
@@ -322,10 +725,10 @@ int main(int argc, char *argv[]) {
     ssimvavg += ssimv[i];
 
     frame_psnr =
-        mse2psnr(w * h * 6 / 4, 255.0, (double)psnry[i] + psnru[i] + psnrv[i]);
+        mse2psnr(w * h * 6 / 4, peak, (double)psnry[i] + psnru[i] + psnrv[i]);
-    frame_psnry = mse2psnr(w * h * 4 / 4, 255.0, (double)psnry[i]);
+    frame_psnry = mse2psnr(w * h * 4 / 4, peak, (double)psnry[i]);
-    frame_psnru = mse2psnr(w * h * 1 / 4, 255.0, (double)psnru[i]);
+    frame_psnru = mse2psnr(w * h * 1 / 4, peak, (double)psnru[i]);
-    frame_psnrv = mse2psnr(w * h * 1 / 4, 255.0, (double)psnrv[i]);
+    frame_psnrv = mse2psnr(w * h * 1 / 4, peak, (double)psnrv[i]);
 
     psnravg += frame_psnr;
     psnryavg += frame_psnry;
@@ -367,10 +770,10 @@ int main(int argc, char *argv[]) {
   puts("");
 
   psnrglb = psnryglb + psnruglb + psnrvglb;
-  psnrglb = mse2psnr((double)n_frames * w * h * 6 / 4, 255.0, psnrglb);
+  psnrglb = mse2psnr((double)n_frames * w * h * 6 / 4, peak, psnrglb);
-  psnryglb = mse2psnr((double)n_frames * w * h * 4 / 4, 255.0, psnryglb);
+  psnryglb = mse2psnr((double)n_frames * w * h * 4 / 4, peak, psnryglb);
-  psnruglb = mse2psnr((double)n_frames * w * h * 1 / 4, 255.0, psnruglb);
+  psnruglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnruglb);
-  psnrvglb = mse2psnr((double)n_frames * w * h * 1 / 4, 255.0, psnrvglb);
+  psnrvglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnrvglb);
 
   printf("GlbPSNR: %lf\n", psnrglb);
   printf("GlbPSNR-Y: %lf\n", psnryglb);