/*
 *  Copyright (c) 2016 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 <assert.h>
#include <errno.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "vpx/vpx_codec.h"
#include "vpx/vpx_integer.h"
#include "./y4minput.h"
#include "vpx_dsp/ssim.h"
#include "vpx_ports/mem.h"

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

#if CONFIG_VP9_HIGHBITDEPTH
static uint64_t calc_plane_error16(uint16_t *orig, int orig_stride,
                                   uint16_t *recon, int recon_stride,
                                   unsigned int cols, unsigned int rows) {
  unsigned int row, col;
  uint64_t total_sse = 0;
  int diff;
  if (orig == NULL || recon == NULL) {
    assert(0);
    return 0;
  }

  for (row = 0; row < rows; row++) {
    for (col = 0; col < cols; col++) {
      diff = orig[col] - recon[col];
      total_sse += diff * diff;
    }

    orig += orig_stride;
    recon += recon_stride;
  }
  return total_sse;
}
#endif
static uint64_t calc_plane_error(uint8_t *orig, int orig_stride, uint8_t *recon,
                                 int recon_stride, unsigned int cols,
                                 unsigned int rows) {
  unsigned int row, col;
  uint64_t total_sse = 0;
  int diff;
  if (orig == NULL || recon == NULL) {
    assert(0);
    return 0;
  }

  for (row = 0; row < rows; row++) {
    for (col = 0; col < cols; col++) {
      diff = orig[col] - recon[col];
      total_sse += diff * diff;
    }

    orig += orig_stride;
    recon += recon_stride;
  }
  return total_sse;
}

#define MAX_PSNR 100
static double mse2psnr(double samples, double peak, double mse) {
  double psnr;

  if (mse > 0.0)
    psnr = 10.0 * log10(peak * peak * samples / mse);
  else
    psnr = MAX_PSNR;  // Limit to prevent / 0

  if (psnr > MAX_PSNR) psnr = MAX_PSNR;

  return psnr;
}

typedef enum { RAW_YUV, Y4M } input_file_type;

typedef struct input_file {
  FILE *file;
  input_file_type type;
  unsigned char *buf;
  y4m_input y4m;
  vpx_image_t img;
  int w;
  int h;
  int bit_depth;
  int frame_size;
} 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 bit_depth) {
  char y4m_buf[4];
  size_t r1;
  input->w = w;
  input->h = h;
  input->bit_depth = bit_depth;
  input->type = RAW_YUV;
  input->buf = NULL;
  input->file = strcmp(file_name, "-") ? fopen(file_name, "rb") : stdin;
  if (input->file == NULL) return -1;
  r1 = fread(y4m_buf, 1, 4, input->file);
  if (r1 == 4) {
    if (memcmp(y4m_buf, "YUV4", 4) == 0) input->type = Y4M;
    switch (input->type) {
      case Y4M:
        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));
        break;
      case RAW_YUV:
        fseek(input->file, 0, SEEK_SET);
        input->w = w;
        input->h = h;
        // handle odd frame sizes
        input->frame_size = w * h + ((w + 1) / 2) * ((h + 1) / 2) * 2;
        if (bit_depth > 8) {
          input->frame_size *= 2;
        }
        input->buf = malloc(input->frame_size);
        break;
    }
  }
  return 0;
}

static void close_input_file(input_file_t *in) {
  if (in->file) fclose(in->file);
  if (in->type == Y4M) {
    vpx_img_free(&in->img);
  } else {
    free(in->buf);
  }
}

static size_t read_input_file(input_file_t *in, unsigned char **y,
                              unsigned char **u, unsigned char **v, int bd) {
  size_t r1 = 0;
  switch (in->type) {
    case Y4M:
      r1 = y4m_input_fetch_frame(&in->y4m, in->file, &in->img);
      *y = in->img.planes[0];
      *u = in->img.planes[1];
      *v = in->img.planes[2];
      break;
    case RAW_YUV:
      if (bd < 9) {
        r1 = fread(in->buf, in->frame_size, 1, in->file);
        *y = in->buf;
        *u = in->buf + in->w * in->h;
        *v = *u + ((1 + in->w) / 2) * ((1 + in->h) / 2);
      } else {
        r1 = fread(in->buf, in->frame_size, 1, in->file);
        *y = in->buf;
        *u = in->buf + (in->w * in->h) * 2;
        *v = *u + 2 * ((1 + in->w) / 2) * ((1 + 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;
  if (s == NULL || r == NULL || sum_s == NULL || sum_r == NULL ||
      sum_sq_s == NULL || sum_sq_r == NULL || sum_sxr == NULL) {
    assert(0);
    return;
  }
  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;
  if (s == NULL || r == NULL || sum_s == NULL || sum_r == NULL ||
      sum_sq_s == NULL || sum_sq_r == NULL || sum_sxr == NULL) {
    assert(0);
    return;
  }
  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) {
  double 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 = (2.0 * sum_s * sum_r + c1) *
           (2.0 * count * sum_sxr - 2.0 * sum_s * sum_r + c2);

  ssim_d = ((double)sum_s * sum_s + (double)sum_r * sum_r + c1) *
           ((double)count * sum_sq_s - (double)sum_s * sum_s +
            (double)count * sum_sq_r - (double)sum_r * sum_r + c2);

  return ssim_n / 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, 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 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;
  double psnravg = 0, psnryavg = 0, psnruavg = 0, psnrvavg = 0;
  double *ssimy = NULL, *ssimu = NULL, *ssimv = NULL;
  uint64_t *psnry = NULL, *psnru = NULL, *psnrv = NULL;
  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} frame_stats_file bits]\n",
            argv[0]);
    return_value = 1;
    goto clean_up;
  }

  if (argc > 3) {
    sscanf(argv[3], "%dx%d", &w, &h);
  }

  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[1]);
    goto clean_up;
  }

  if (w == 0 && h == 0) {
    // 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 (bit_depth == 12) peak = 4095.0;

  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;
  }

  if (in[0].w != in[1].w || in[0].h != in[1].h || in[0].w != w ||
      in[0].h != h || w == 0 || h == 0) {
    fprintf(stderr,
            "Failing: Image dimensions don't match or are unspecified!\n");
    return_value = 1;
    goto clean_up;
  }

  // Number of frames to skip from file1.yuv for every frame used. Normal
  // values 0, 1 and 3 correspond to TL2, TL1 and TL0 respectively for a 3TL
  // encoding in mode 10. 7 would be reasonable for comparing TL0 of a 4-layer
  // encoding.
  if (argc > 4) {
    sscanf(argv[4], "%d", &tl_skip);
    if (argc > 5) {
      framestats = fopen(argv[5], "w");
      if (!framestats) {
        fprintf(stderr, "Could not open \"%s\" for writing: %s\n", argv[5],
                strerror(errno));
        return_value = 1;
        goto clean_up;
      }
    }
  }

  while (1) {
    size_t r1, r2;
    unsigned char *y[2], *u[2], *v[2];

    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.
      if (tl_skips_remaining > 0) {
        --tl_skips_remaining;
        continue;
      }
      // Use frame, but skip |tl_skip| after it.
      tl_skips_remaining = tl_skip;
    }

    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),
              (int)r1, (int)r2);
      return_value = 1;
      goto clean_up;
    } 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;
      ssimy = realloc(ssimy, allocated_frames * sizeof(*ssimy));
      ssimu = realloc(ssimu, allocated_frames * sizeof(*ssimu));
      ssimv = realloc(ssimv, allocated_frames * sizeof(*ssimv));
      psnry = realloc(psnry, allocated_frames * sizeof(*psnry));
      psnru = realloc(psnru, allocated_frames * sizeof(*psnru));
      psnrv = realloc(psnrv, allocated_frames * sizeof(*psnrv));
    }
    psnr_and_ssim(ssimy[n_frames], psnry[n_frames], y[0], y[1], w, h);
    psnr_and_ssim(ssimu[n_frames], psnru[n_frames], u[0], u[1], (w + 1) / 2,
                  (h + 1) / 2);
    psnr_and_ssim(ssimv[n_frames], psnrv[n_frames], v[0], v[1], (w + 1) / 2,
                  (h + 1) / 2);

    n_frames++;
  }

  if (framestats) {
    fprintf(framestats,
            "ssim,ssim-y,ssim-u,ssim-v,psnr,psnr-y,psnr-u,psnr-v\n");
  }

  for (i = 0; i < n_frames; ++i) {
    double frame_ssim;
    double frame_psnr, frame_psnry, frame_psnru, frame_psnrv;

    frame_ssim = 0.8 * ssimy[i] + 0.1 * (ssimu[i] + ssimv[i]);
    ssimavg += frame_ssim;
    ssimyavg += ssimy[i];
    ssimuavg += ssimu[i];
    ssimvavg += ssimv[i];

    frame_psnr =
        mse2psnr(w * h * 6 / 4, peak, (double)psnry[i] + psnru[i] + psnrv[i]);
    frame_psnry = mse2psnr(w * h * 4 / 4, peak, (double)psnry[i]);
    frame_psnru = mse2psnr(w * h * 1 / 4, peak, (double)psnru[i]);
    frame_psnrv = mse2psnr(w * h * 1 / 4, peak, (double)psnrv[i]);

    psnravg += frame_psnr;
    psnryavg += frame_psnry;
    psnruavg += frame_psnru;
    psnrvavg += frame_psnrv;

    psnryglb += psnry[i];
    psnruglb += psnru[i];
    psnrvglb += psnrv[i];

    if (framestats) {
      fprintf(framestats, "%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf\n", frame_ssim,
              ssimy[i], ssimu[i], ssimv[i], frame_psnr, frame_psnry,
              frame_psnru, frame_psnrv);
    }
  }

  ssimavg /= n_frames;
  ssimyavg /= n_frames;
  ssimuavg /= n_frames;
  ssimvavg /= n_frames;

  printf("VpxSSIM: %lf\n", 100 * pow(ssimavg, 8.0));
  printf("SSIM: %lf\n", ssimavg);
  printf("SSIM-Y: %lf\n", ssimyavg);
  printf("SSIM-U: %lf\n", ssimuavg);
  printf("SSIM-V: %lf\n", ssimvavg);
  puts("");

  psnravg /= n_frames;
  psnryavg /= n_frames;
  psnruavg /= n_frames;
  psnrvavg /= n_frames;

  printf("AvgPSNR: %lf\n", psnravg);
  printf("AvgPSNR-Y: %lf\n", psnryavg);
  printf("AvgPSNR-U: %lf\n", psnruavg);
  printf("AvgPSNR-V: %lf\n", psnrvavg);
  puts("");

  psnrglb = psnryglb + psnruglb + psnrvglb;
  psnrglb = mse2psnr((double)n_frames * w * h * 6 / 4, peak, psnrglb);
  psnryglb = mse2psnr((double)n_frames * w * h * 4 / 4, peak, psnryglb);
  psnruglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnruglb);
  psnrvglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnrvglb);

  printf("GlbPSNR: %lf\n", psnrglb);
  printf("GlbPSNR-Y: %lf\n", psnryglb);
  printf("GlbPSNR-U: %lf\n", psnruglb);
  printf("GlbPSNR-V: %lf\n", psnrvglb);
  puts("");

  printf("Nframes: %d\n", (int)n_frames);

clean_up:

  close_input_file(&in[0]);
  close_input_file(&in[1]);

  if (framestats) fclose(framestats);

  free(ssimy);
  free(ssimu);
  free(ssimv);

  free(psnry);
  free(psnru);
  free(psnrv);

  return return_value;
}