tiny_ssim.c: resolve missing declarations

-Wmissing-declarations exposed several unused functions.

BUG=webm:1584

Change-Id: I88dfeb8ffa31253a0fb7674f6fe5fcd496179f96

1 files changed, 9 insertions, 263 deletions

diff --git a/tools/tiny_ssim.c b/tools/tiny_ssim.c
index be52e4445..2d945bb98 100644
--- a/tools/tiny_ssim.c
+++ b/tools/tiny_ssim.c
@@ -181,23 +181,9 @@ static size_t read_input_file(input_file_t *in, unsigned char **y,
   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) {
+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;
   if (s == NULL || r == NULL || sum_s == NULL || sum_r == NULL ||
       sum_sq_s == NULL || sum_sq_r == NULL || sum_sxr == NULL) {
@@ -215,9 +201,11 @@ void ssim_parms_8x8(const uint8_t *s, int sp, const uint8_t *r, int rp,
   }
 }
 
-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 @@ void highbd_ssim_parms_8x8(const uint16_t *s, int sp, const uint16_t *r, int rp,
     }
   }
 }
+#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,
@@ -326,249 +315,6 @@ static double highbd_ssim2(const uint8_t *img1, const uint8_t *img2,
 }
 #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;
   int bit_depth = 8;