Move aom source to a sub-directory under media/libaom

There is no damned reason to treat this differently than any other media lib given its license and there never was.

1 files changed, 0 insertions, 1648 deletions

diff --git a/third_party/aom/aom_dsp/noise_model.c b/third_party/aom/aom_dsp/noise_model.c
deleted file mode 100644
index 2faee8506..000000000
--- a/third_party/aom/aom_dsp/noise_model.c
+++ /dev/null
@@ -1,1648 +0,0 @@
-/*
- * Copyright (c) 2017, Alliance for Open Media. All rights reserved
- *
- * This source code is subject to the terms of the BSD 2 Clause License and
- * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
- * was not distributed with this source code in the LICENSE file, you can
- * obtain it at www.aomedia.org/license/software. If the Alliance for Open
- * Media Patent License 1.0 was not distributed with this source code in the
- * PATENTS file, you can obtain it at www.aomedia.org/license/patent.
- */
-
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#include "aom_dsp/aom_dsp_common.h"
-#include "aom_dsp/noise_model.h"
-#include "aom_dsp/noise_util.h"
-#include "aom_mem/aom_mem.h"
-#include "av1/common/common.h"
-#include "av1/encoder/mathutils.h"
-
-#define kLowPolyNumParams 3
-
-static const int kMaxLag = 4;
-
-// Defines a function that can be used to obtain the mean of a block for the
-// provided data type (uint8_t, or uint16_t)
-#define GET_BLOCK_MEAN(INT_TYPE, suffix)                                    \
-  static double get_block_mean_##suffix(const INT_TYPE *data, int w, int h, \
-                                        int stride, int x_o, int y_o,       \
-                                        int block_size) {                   \
-    const int max_h = AOMMIN(h - y_o, block_size);                          \
-    const int max_w = AOMMIN(w - x_o, block_size);                          \
-    double block_mean = 0;                                                  \
-    for (int y = 0; y < max_h; ++y) {                                       \
-      for (int x = 0; x < max_w; ++x) {                                     \
-        block_mean += data[(y_o + y) * stride + x_o + x];                   \
-      }                                                                     \
-    }                                                                       \
-    return block_mean / (max_w * max_h);                                    \
-  }
-
-GET_BLOCK_MEAN(uint8_t, lowbd);
-GET_BLOCK_MEAN(uint16_t, highbd);
-
-static INLINE double get_block_mean(const uint8_t *data, int w, int h,
-                                    int stride, int x_o, int y_o,
-                                    int block_size, int use_highbd) {
-  if (use_highbd)
-    return get_block_mean_highbd((const uint16_t *)data, w, h, stride, x_o, y_o,
-                                 block_size);
-  return get_block_mean_lowbd(data, w, h, stride, x_o, y_o, block_size);
-}
-
-// Defines a function that can be used to obtain the variance of a block
-// for the provided data type (uint8_t, or uint16_t)
-#define GET_NOISE_VAR(INT_TYPE, suffix)                                  \
-  static double get_noise_var_##suffix(                                  \
-      const INT_TYPE *data, const INT_TYPE *denoised, int stride, int w, \
-      int h, int x_o, int y_o, int block_size_x, int block_size_y) {     \
-    const int max_h = AOMMIN(h - y_o, block_size_y);                     \
-    const int max_w = AOMMIN(w - x_o, block_size_x);                     \
-    double noise_var = 0;                                                \
-    double noise_mean = 0;                                               \
-    for (int y = 0; y < max_h; ++y) {                                    \
-      for (int x = 0; x < max_w; ++x) {                                  \
-        double noise = (double)data[(y_o + y) * stride + x_o + x] -      \
-                       denoised[(y_o + y) * stride + x_o + x];           \
-        noise_mean += noise;                                             \
-        noise_var += noise * noise;                                      \
-      }                                                                  \
-    }                                                                    \
-    noise_mean /= (max_w * max_h);                                       \
-    return noise_var / (max_w * max_h) - noise_mean * noise_mean;        \
-  }
-
-GET_NOISE_VAR(uint8_t, lowbd);
-GET_NOISE_VAR(uint16_t, highbd);
-
-static INLINE double get_noise_var(const uint8_t *data, const uint8_t *denoised,
-                                   int w, int h, int stride, int x_o, int y_o,
-                                   int block_size_x, int block_size_y,
-                                   int use_highbd) {
-  if (use_highbd)
-    return get_noise_var_highbd((const uint16_t *)data,
-                                (const uint16_t *)denoised, w, h, stride, x_o,
-                                y_o, block_size_x, block_size_y);
-  return get_noise_var_lowbd(data, denoised, w, h, stride, x_o, y_o,
-                             block_size_x, block_size_y);
-}
-
-static void equation_system_clear(aom_equation_system_t *eqns) {
-  const int n = eqns->n;
-  memset(eqns->A, 0, sizeof(*eqns->A) * n * n);
-  memset(eqns->x, 0, sizeof(*eqns->x) * n);
-  memset(eqns->b, 0, sizeof(*eqns->b) * n);
-}
-
-static void equation_system_copy(aom_equation_system_t *dst,
-                                 const aom_equation_system_t *src) {
-  const int n = dst->n;
-  memcpy(dst->A, src->A, sizeof(*dst->A) * n * n);
-  memcpy(dst->x, src->x, sizeof(*dst->x) * n);
-  memcpy(dst->b, src->b, sizeof(*dst->b) * n);
-}
-
-static int equation_system_init(aom_equation_system_t *eqns, int n) {
-  eqns->A = (double *)aom_malloc(sizeof(*eqns->A) * n * n);
-  eqns->b = (double *)aom_malloc(sizeof(*eqns->b) * n);
-  eqns->x = (double *)aom_malloc(sizeof(*eqns->x) * n);
-  eqns->n = n;
-  if (!eqns->A || !eqns->b || !eqns->x) {
-    fprintf(stderr, "Failed to allocate system of equations of size %d\n", n);
-    aom_free(eqns->A);
-    aom_free(eqns->b);
-    aom_free(eqns->x);
-    memset(eqns, 0, sizeof(*eqns));
-    return 0;
-  }
-  equation_system_clear(eqns);
-  return 1;
-}
-
-static int equation_system_solve(aom_equation_system_t *eqns) {
-  const int n = eqns->n;
-  double *b = (double *)aom_malloc(sizeof(*b) * n);
-  double *A = (double *)aom_malloc(sizeof(*A) * n * n);
-  int ret = 0;
-  if (A == NULL || b == NULL) {
-    fprintf(stderr, "Unable to allocate temp values of size %dx%d\n", n, n);
-    aom_free(b);
-    aom_free(A);
-    return 0;
-  }
-  memcpy(A, eqns->A, sizeof(*eqns->A) * n * n);
-  memcpy(b, eqns->b, sizeof(*eqns->b) * n);
-  ret = linsolve(n, A, eqns->n, b, eqns->x);
-  aom_free(b);
-  aom_free(A);
-
-  if (ret == 0) {
-    return 0;
-  }
-  return 1;
-}
-
-static void equation_system_add(aom_equation_system_t *dest,
-                                aom_equation_system_t *src) {
-  const int n = dest->n;
-  int i, j;
-  for (i = 0; i < n; ++i) {
-    for (j = 0; j < n; ++j) {
-      dest->A[i * n + j] += src->A[i * n + j];
-    }
-    dest->b[i] += src->b[i];
-  }
-}
-
-static void equation_system_free(aom_equation_system_t *eqns) {
-  if (!eqns) return;
-  aom_free(eqns->A);
-  aom_free(eqns->b);
-  aom_free(eqns->x);
-  memset(eqns, 0, sizeof(*eqns));
-}
-
-static void noise_strength_solver_clear(aom_noise_strength_solver_t *solver) {
-  equation_system_clear(&solver->eqns);
-  solver->num_equations = 0;
-  solver->total = 0;
-}
-
-static void noise_strength_solver_add(aom_noise_strength_solver_t *dest,
-                                      aom_noise_strength_solver_t *src) {
-  equation_system_add(&dest->eqns, &src->eqns);
-  dest->num_equations += src->num_equations;
-  dest->total += src->total;
-}
-
-// Return the number of coefficients required for the given parameters
-static int num_coeffs(const aom_noise_model_params_t params) {
-  const int n = 2 * params.lag + 1;
-  switch (params.shape) {
-    case AOM_NOISE_SHAPE_DIAMOND: return params.lag * (params.lag + 1);
-    case AOM_NOISE_SHAPE_SQUARE: return (n * n) / 2;
-  }
-  return 0;
-}
-
-static int noise_state_init(aom_noise_state_t *state, int n, int bit_depth) {
-  const int kNumBins = 20;
-  if (!equation_system_init(&state->eqns, n)) {
-    fprintf(stderr, "Failed initialization noise state with size %d\n", n);
-    return 0;
-  }
-  state->ar_gain = 1.0;
-  state->num_observations = 0;
-  return aom_noise_strength_solver_init(&state->strength_solver, kNumBins,
-                                        bit_depth);
-}
-
-static void set_chroma_coefficient_fallback_soln(aom_equation_system_t *eqns) {
-  const double kTolerance = 1e-6;
-  const int last = eqns->n - 1;
-  // Set all of the AR coefficients to zero, but try to solve for correlation
-  // with the luma channel
-  memset(eqns->x, 0, sizeof(*eqns->x) * eqns->n);
-  if (fabs(eqns->A[last * eqns->n + last]) > kTolerance) {
-    eqns->x[last] = eqns->b[last] / eqns->A[last * eqns->n + last];
-  }
-}
-
-int aom_noise_strength_lut_init(aom_noise_strength_lut_t *lut, int num_points) {
-  if (!lut) return 0;
-  lut->points = (double(*)[2])aom_malloc(num_points * sizeof(*lut->points));
-  if (!lut->points) return 0;
-  lut->num_points = num_points;
-  memset(lut->points, 0, sizeof(*lut->points) * num_points);
-  return 1;
-}
-
-void aom_noise_strength_lut_free(aom_noise_strength_lut_t *lut) {
-  if (!lut) return;
-  aom_free(lut->points);
-  memset(lut, 0, sizeof(*lut));
-}
-
-double aom_noise_strength_lut_eval(const aom_noise_strength_lut_t *lut,
-                                   double x) {
-  int i = 0;
-  // Constant extrapolation for x <  x_0.
-  if (x < lut->points[0][0]) return lut->points[0][1];
-  for (i = 0; i < lut->num_points - 1; ++i) {
-    if (x >= lut->points[i][0] && x <= lut->points[i + 1][0]) {
-      const double a =
-          (x - lut->points[i][0]) / (lut->points[i + 1][0] - lut->points[i][0]);
-      return lut->points[i + 1][1] * a + lut->points[i][1] * (1.0 - a);
-    }
-  }
-  // Constant extrapolation for x > x_{n-1}
-  return lut->points[lut->num_points - 1][1];
-}
-
-static double noise_strength_solver_get_bin_index(
-    const aom_noise_strength_solver_t *solver, double value) {
-  const double val =
-      fclamp(value, solver->min_intensity, solver->max_intensity);
-  const double range = solver->max_intensity - solver->min_intensity;
-  return (solver->num_bins - 1) * (val - solver->min_intensity) / range;
-}
-
-static double noise_strength_solver_get_value(
-    const aom_noise_strength_solver_t *solver, double x) {
-  const double bin = noise_strength_solver_get_bin_index(solver, x);
-  const int bin_i0 = (int)floor(bin);
-  const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
-  const double a = bin - bin_i0;
-  return (1.0 - a) * solver->eqns.x[bin_i0] + a * solver->eqns.x[bin_i1];
-}
-
-void aom_noise_strength_solver_add_measurement(
-    aom_noise_strength_solver_t *solver, double block_mean, double noise_std) {
-  const double bin = noise_strength_solver_get_bin_index(solver, block_mean);
-  const int bin_i0 = (int)floor(bin);
-  const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
-  const double a = bin - bin_i0;
-  const int n = solver->num_bins;
-  solver->eqns.A[bin_i0 * n + bin_i0] += (1.0 - a) * (1.0 - a);
-  solver->eqns.A[bin_i1 * n + bin_i0] += a * (1.0 - a);
-  solver->eqns.A[bin_i1 * n + bin_i1] += a * a;
-  solver->eqns.A[bin_i0 * n + bin_i1] += a * (1.0 - a);
-  solver->eqns.b[bin_i0] += (1.0 - a) * noise_std;
-  solver->eqns.b[bin_i1] += a * noise_std;
-  solver->total += noise_std;
-  solver->num_equations++;
-}
-
-int aom_noise_strength_solver_solve(aom_noise_strength_solver_t *solver) {
-  // Add regularization proportional to the number of constraints
-  const int n = solver->num_bins;
-  const double kAlpha = 2.0 * (double)(solver->num_equations) / n;
-  int result = 0;
-  double mean = 0;
-
-  // Do this in a non-destructive manner so it is not confusing to the caller
-  double *old_A = solver->eqns.A;
-  double *A = (double *)aom_malloc(sizeof(*A) * n * n);
-  if (!A) {
-    fprintf(stderr, "Unable to allocate copy of A\n");
-    return 0;
-  }
-  memcpy(A, old_A, sizeof(*A) * n * n);
-
-  for (int i = 0; i < n; ++i) {
-    const int i_lo = AOMMAX(0, i - 1);
-    const int i_hi = AOMMIN(n - 1, i + 1);
-    A[i * n + i_lo] -= kAlpha;
-    A[i * n + i] += 2 * kAlpha;
-    A[i * n + i_hi] -= kAlpha;
-  }
-
-  // Small regularization to give average noise strength
-  mean = solver->total / solver->num_equations;
-  for (int i = 0; i < n; ++i) {
-    A[i * n + i] += 1.0 / 8192.;
-    solver->eqns.b[i] += mean / 8192.;
-  }
-  solver->eqns.A = A;
-  result = equation_system_solve(&solver->eqns);
-  solver->eqns.A = old_A;
-
-  aom_free(A);
-  return result;
-}
-
-int aom_noise_strength_solver_init(aom_noise_strength_solver_t *solver,
-                                   int num_bins, int bit_depth) {
-  if (!solver) return 0;
-  memset(solver, 0, sizeof(*solver));
-  solver->num_bins = num_bins;
-  solver->min_intensity = 0;
-  solver->max_intensity = (1 << bit_depth) - 1;
-  solver->total = 0;
-  solver->num_equations = 0;
-  return equation_system_init(&solver->eqns, num_bins);
-}
-
-void aom_noise_strength_solver_free(aom_noise_strength_solver_t *solver) {
-  if (!solver) return;
-  equation_system_free(&solver->eqns);
-}
-
-double aom_noise_strength_solver_get_center(
-    const aom_noise_strength_solver_t *solver, int i) {
-  const double range = solver->max_intensity - solver->min_intensity;
-  const int n = solver->num_bins;
-  return ((double)i) / (n - 1) * range + solver->min_intensity;
-}
-
-// Computes the residual if a point were to be removed from the lut. This is
-// calculated as the area between the output of the solver and the line segment
-// that would be formed between [x_{i - 1}, x_{i + 1}).
-static void update_piecewise_linear_residual(
-    const aom_noise_strength_solver_t *solver,
-    const aom_noise_strength_lut_t *lut, double *residual, int start, int end) {
-  const double dx = 255. / solver->num_bins;
-  for (int i = AOMMAX(start, 1); i < AOMMIN(end, lut->num_points - 1); ++i) {
-    const int lower = AOMMAX(0, (int)floor(noise_strength_solver_get_bin_index(
-                                    solver, lut->points[i - 1][0])));
-    const int upper = AOMMIN(solver->num_bins - 1,
-                             (int)ceil(noise_strength_solver_get_bin_index(
-                                 solver, lut->points[i + 1][0])));
-    double r = 0;
-    for (int j = lower; j <= upper; ++j) {
-      const double x = aom_noise_strength_solver_get_center(solver, j);
-      if (x < lut->points[i - 1][0]) continue;
-      if (x >= lut->points[i + 1][0]) continue;
-      const double y = solver->eqns.x[j];
-      const double a = (x - lut->points[i - 1][0]) /
-                       (lut->points[i + 1][0] - lut->points[i - 1][0]);
-      const double estimate_y =
-          lut->points[i - 1][1] * (1.0 - a) + lut->points[i + 1][1] * a;
-      r += fabs(y - estimate_y);
-    }
-    residual[i] = r * dx;
-  }
-}
-
-int aom_noise_strength_solver_fit_piecewise(
-    const aom_noise_strength_solver_t *solver, int max_output_points,
-    aom_noise_strength_lut_t *lut) {
-  // The tolerance is normalized to be give consistent results between
-  // different bit-depths.
-  const double kTolerance = solver->max_intensity * 0.00625 / 255.0;
-  if (!aom_noise_strength_lut_init(lut, solver->num_bins)) {
-    fprintf(stderr, "Failed to init lut\n");
-    return 0;
-  }
-  for (int i = 0; i < solver->num_bins; ++i) {
-    lut->points[i][0] = aom_noise_strength_solver_get_center(solver, i);
-    lut->points[i][1] = solver->eqns.x[i];
-  }
-  if (max_output_points < 0) {
-    max_output_points = solver->num_bins;
-  }
-
-  double *residual = aom_malloc(solver->num_bins * sizeof(*residual));
-  memset(residual, 0, sizeof(*residual) * solver->num_bins);
-
-  update_piecewise_linear_residual(solver, lut, residual, 0, solver->num_bins);
-
-  // Greedily remove points if there are too many or if it doesn't hurt local
-  // approximation (never remove the end points)
-  while (lut->num_points > 2) {
-    int min_index = 1;
-    for (int j = 1; j < lut->num_points - 1; ++j) {
-      if (residual[j] < residual[min_index]) {
-        min_index = j;
-      }
-    }
-    const double dx =
-        lut->points[min_index + 1][0] - lut->points[min_index - 1][0];
-    const double avg_residual = residual[min_index] / dx;
-    if (lut->num_points <= max_output_points && avg_residual > kTolerance) {
-      break;
-    }
-
-    const int num_remaining = lut->num_points - min_index - 1;
-    memmove(lut->points + min_index, lut->points + min_index + 1,
-            sizeof(lut->points[0]) * num_remaining);
-    lut->num_points--;
-
-    update_piecewise_linear_residual(solver, lut, residual, min_index - 1,
-                                     min_index + 1);
-  }
-  aom_free(residual);
-  return 1;
-}
-
-int aom_flat_block_finder_init(aom_flat_block_finder_t *block_finder,
-                               int block_size, int bit_depth, int use_highbd) {
-  const int n = block_size * block_size;
-  aom_equation_system_t eqns;
-  double *AtA_inv = 0;
-  double *A = 0;
-  int x = 0, y = 0, i = 0, j = 0;
-  if (!equation_system_init(&eqns, kLowPolyNumParams)) {
-    fprintf(stderr, "Failed to init equation system for block_size=%d\n",
-            block_size);
-    return 0;
-  }
-
-  AtA_inv = (double *)aom_malloc(kLowPolyNumParams * kLowPolyNumParams *
-                                 sizeof(*AtA_inv));
-  A = (double *)aom_malloc(kLowPolyNumParams * n * sizeof(*A));
-  if (AtA_inv == NULL || A == NULL) {
-    fprintf(stderr, "Failed to alloc A or AtA_inv for block_size=%d\n",
-            block_size);
-    aom_free(AtA_inv);
-    aom_free(A);
-    equation_system_free(&eqns);
-    return 0;
-  }
-
-  block_finder->A = A;
-  block_finder->AtA_inv = AtA_inv;
-  block_finder->block_size = block_size;
-  block_finder->normalization = (1 << bit_depth) - 1;
-  block_finder->use_highbd = use_highbd;
-
-  for (y = 0; y < block_size; ++y) {
-    const double yd = ((double)y - block_size / 2.) / (block_size / 2.);
-    for (x = 0; x < block_size; ++x) {
-      const double xd = ((double)x - block_size / 2.) / (block_size / 2.);
-      const double coords[3] = { yd, xd, 1 };
-      const int row = y * block_size + x;
-      A[kLowPolyNumParams * row + 0] = yd;
-      A[kLowPolyNumParams * row + 1] = xd;
-      A[kLowPolyNumParams * row + 2] = 1;
-
-      for (i = 0; i < kLowPolyNumParams; ++i) {
-        for (j = 0; j < kLowPolyNumParams; ++j) {
-          eqns.A[kLowPolyNumParams * i + j] += coords[i] * coords[j];
-        }
-      }
-    }
-  }
-
-  // Lazy inverse using existing equation solver.
-  for (i = 0; i < kLowPolyNumParams; ++i) {
-    memset(eqns.b, 0, sizeof(*eqns.b) * kLowPolyNumParams);
-    eqns.b[i] = 1;
-    equation_system_solve(&eqns);
-
-    for (j = 0; j < kLowPolyNumParams; ++j) {
-      AtA_inv[j * kLowPolyNumParams + i] = eqns.x[j];
-    }
-  }
-  equation_system_free(&eqns);
-  return 1;
-}
-
-void aom_flat_block_finder_free(aom_flat_block_finder_t *block_finder) {
-  if (!block_finder) return;
-  aom_free(block_finder->A);
-  aom_free(block_finder->AtA_inv);
-  memset(block_finder, 0, sizeof(*block_finder));
-}
-
-void aom_flat_block_finder_extract_block(
-    const aom_flat_block_finder_t *block_finder, const uint8_t *const data,
-    int w, int h, int stride, int offsx, int offsy, double *plane,
-    double *block) {
-  const int block_size = block_finder->block_size;
-  const int n = block_size * block_size;
-  const double *A = block_finder->A;
-  const double *AtA_inv = block_finder->AtA_inv;
-  double plane_coords[kLowPolyNumParams];
-  double AtA_inv_b[kLowPolyNumParams];
-  int xi, yi, i;
-
-  if (block_finder->use_highbd) {
-    const uint16_t *const data16 = (const uint16_t *const)data;
-    for (yi = 0; yi < block_size; ++yi) {
-      const int y = clamp(offsy + yi, 0, h - 1);
-      for (xi = 0; xi < block_size; ++xi) {
-        const int x = clamp(offsx + xi, 0, w - 1);
-        block[yi * block_size + xi] =
-            ((double)data16[y * stride + x]) / block_finder->normalization;
-      }
-    }
-  } else {
-    for (yi = 0; yi < block_size; ++yi) {
-      const int y = clamp(offsy + yi, 0, h - 1);
-      for (xi = 0; xi < block_size; ++xi) {
-        const int x = clamp(offsx + xi, 0, w - 1);
-        block[yi * block_size + xi] =
-            ((double)data[y * stride + x]) / block_finder->normalization;
-      }
-    }
-  }
-  multiply_mat(block, A, AtA_inv_b, 1, n, kLowPolyNumParams);
-  multiply_mat(AtA_inv, AtA_inv_b, plane_coords, kLowPolyNumParams,
-               kLowPolyNumParams, 1);
-  multiply_mat(A, plane_coords, plane, n, kLowPolyNumParams, 1);
-
-  for (i = 0; i < n; ++i) {
-    block[i] -= plane[i];
-  }
-}
-
-typedef struct {
-  int index;
-  float score;
-} index_and_score_t;
-
-static int compare_scores(const void *a, const void *b) {
-  const float diff =
-      ((index_and_score_t *)a)->score - ((index_and_score_t *)b)->score;
-  if (diff < 0)
-    return -1;
-  else if (diff > 0)
-    return 1;
-  return 0;
-}
-
-int aom_flat_block_finder_run(const aom_flat_block_finder_t *block_finder,
-                              const uint8_t *const data, int w, int h,
-                              int stride, uint8_t *flat_blocks) {
-  // The gradient-based features used in this code are based on:
-  //  A. Kokaram, D. Kelly, H. Denman and A. Crawford, "Measuring noise
-  //  correlation for improved video denoising," 2012 19th, ICIP.
-  // The thresholds are more lenient to allow for correct grain modeling
-  // if extreme cases.
-  const int block_size = block_finder->block_size;
-  const int n = block_size * block_size;
-  const double kTraceThreshold = 0.15 / (32 * 32);
-  const double kRatioThreshold = 1.25;
-  const double kNormThreshold = 0.08 / (32 * 32);
-  const double kVarThreshold = 0.005 / (double)n;
-  const int num_blocks_w = (w + block_size - 1) / block_size;
-  const int num_blocks_h = (h + block_size - 1) / block_size;
-  int num_flat = 0;
-  int bx = 0, by = 0;
-  double *plane = (double *)aom_malloc(n * sizeof(*plane));
-  double *block = (double *)aom_malloc(n * sizeof(*block));
-  index_and_score_t *scores = (index_and_score_t *)aom_malloc(
-      num_blocks_w * num_blocks_h * sizeof(*scores));
-  if (plane == NULL || block == NULL || scores == NULL) {
-    fprintf(stderr, "Failed to allocate memory for block of size %d\n", n);
-    aom_free(plane);
-    aom_free(block);
-    aom_free(scores);
-    return -1;
-  }
-
-#ifdef NOISE_MODEL_LOG_SCORE
-  fprintf(stderr, "score = [");
-#endif
-  for (by = 0; by < num_blocks_h; ++by) {
-    for (bx = 0; bx < num_blocks_w; ++bx) {
-      // Compute gradient covariance matrix.
-      double Gxx = 0, Gxy = 0, Gyy = 0;
-      double var = 0;
-      double mean = 0;
-      int xi, yi;
-      aom_flat_block_finder_extract_block(block_finder, data, w, h, stride,
-                                          bx * block_size, by * block_size,
-                                          plane, block);
-
-      for (yi = 1; yi < block_size - 1; ++yi) {
-        for (xi = 1; xi < block_size - 1; ++xi) {
-          const double gx = (block[yi * block_size + xi + 1] -
-                             block[yi * block_size + xi - 1]) /
-                            2;
-          const double gy = (block[yi * block_size + xi + block_size] -
-                             block[yi * block_size + xi - block_size]) /
-                            2;
-          Gxx += gx * gx;
-          Gxy += gx * gy;
-          Gyy += gy * gy;
-
-          mean += block[yi * block_size + xi];
-          var += block[yi * block_size + xi] * block[yi * block_size + xi];
-        }
-      }
-      mean /= (block_size - 2) * (block_size - 2);
-
-      // Normalize gradients by block_size.
-      Gxx /= ((block_size - 2) * (block_size - 2));
-      Gxy /= ((block_size - 2) * (block_size - 2));
-      Gyy /= ((block_size - 2) * (block_size - 2));
-      var = var / ((block_size - 2) * (block_size - 2)) - mean * mean;
-
-      {
-        const double trace = Gxx + Gyy;
-        const double det = Gxx * Gyy - Gxy * Gxy;
-        const double e1 = (trace + sqrt(trace * trace - 4 * det)) / 2.;
-        const double e2 = (trace - sqrt(trace * trace - 4 * det)) / 2.;
-        const double norm = e1;  // Spectral norm
-        const double ratio = (e1 / AOMMAX(e2, 1e-6));
-        const int is_flat = (trace < kTraceThreshold) &&
-                            (ratio < kRatioThreshold) &&
-                            (norm < kNormThreshold) && (var > kVarThreshold);
-        // The following weights are used to combine the above features to give
-        // a sigmoid score for flatness. If the input was normalized to [0,100]
-        // the magnitude of these values would be close to 1 (e.g., weights
-        // corresponding to variance would be a factor of 10000x smaller).
-        // The weights are given in the following order:
-        //    [{var}, {ratio}, {trace}, {norm}, offset]
-        // with one of the most discriminative being simply the variance.
-        const double weights[5] = { -6682, -0.2056, 13087, -12434, 2.5694 };
-        const float score =
-            (float)(1.0 / (1 + exp(-(weights[0] * var + weights[1] * ratio +
-                                     weights[2] * trace + weights[3] * norm +
-                                     weights[4]))));
-        flat_blocks[by * num_blocks_w + bx] = is_flat ? 255 : 0;
-        scores[by * num_blocks_w + bx].score = var > kVarThreshold ? score : 0;
-        scores[by * num_blocks_w + bx].index = by * num_blocks_w + bx;
-#ifdef NOISE_MODEL_LOG_SCORE
-        fprintf(stderr, "%g %g %g %g %g %d ", score, var, ratio, trace, norm,
-                is_flat);
-#endif
-        num_flat += is_flat;
-      }
-    }
-#ifdef NOISE_MODEL_LOG_SCORE
-    fprintf(stderr, "\n");
-#endif
-  }
-#ifdef NOISE_MODEL_LOG_SCORE
-  fprintf(stderr, "];\n");
-#endif
-  // Find the top-scored blocks (most likely to be flat) and set the flat blocks
-  // be the union of the thresholded results and the top 10th percentile of the
-  // scored results.
-  qsort(scores, num_blocks_w * num_blocks_h, sizeof(*scores), &compare_scores);
-  const int top_nth_percentile = num_blocks_w * num_blocks_h * 90 / 100;
-  const float score_threshold = scores[top_nth_percentile].score;
-  for (int i = 0; i < num_blocks_w * num_blocks_h; ++i) {
-    if (scores[i].score >= score_threshold) {
-      num_flat += flat_blocks[scores[i].index] == 0;
-      flat_blocks[scores[i].index] |= 1;
-    }
-  }
-  aom_free(block);
-  aom_free(plane);
-  aom_free(scores);
-  return num_flat;
-}
-
-int aom_noise_model_init(aom_noise_model_t *model,
-                         const aom_noise_model_params_t params) {
-  const int n = num_coeffs(params);
-  const int lag = params.lag;
-  const int bit_depth = params.bit_depth;
-  int x = 0, y = 0, i = 0, c = 0;
-
-  memset(model, 0, sizeof(*model));
-  if (params.lag < 1) {
-    fprintf(stderr, "Invalid noise param: lag = %d must be >= 1\n", params.lag);
-    return 0;
-  }
-  if (params.lag > kMaxLag) {
-    fprintf(stderr, "Invalid noise param: lag = %d must be <= %d\n", params.lag,
-            kMaxLag);
-    return 0;
-  }
-
-  memcpy(&model->params, &params, sizeof(params));
-  for (c = 0; c < 3; ++c) {
-    if (!noise_state_init(&model->combined_state[c], n + (c > 0), bit_depth)) {
-      fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
-      aom_noise_model_free(model);
-      return 0;
-    }
-    if (!noise_state_init(&model->latest_state[c], n + (c > 0), bit_depth)) {
-      fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
-      aom_noise_model_free(model);
-      return 0;
-    }
-  }
-  model->n = n;
-  model->coords = (int(*)[2])aom_malloc(sizeof(*model->coords) * n);
-
-  for (y = -lag; y <= 0; ++y) {
-    const int max_x = y == 0 ? -1 : lag;
-    for (x = -lag; x <= max_x; ++x) {
-      switch (params.shape) {
-        case AOM_NOISE_SHAPE_DIAMOND:
-          if (abs(x) <= y + lag) {
-            model->coords[i][0] = x;
-            model->coords[i][1] = y;
-            ++i;
-          }
-          break;
-        case AOM_NOISE_SHAPE_SQUARE:
-          model->coords[i][0] = x;
-          model->coords[i][1] = y;
-          ++i;
-          break;
-        default:
-          fprintf(stderr, "Invalid shape\n");
-          aom_noise_model_free(model);
-          return 0;
-      }
-    }
-  }
-  assert(i == n);
-  return 1;
-}
-
-void aom_noise_model_free(aom_noise_model_t *model) {
-  int c = 0;
-  if (!model) return;
-
-  aom_free(model->coords);
-  for (c = 0; c < 3; ++c) {
-    equation_system_free(&model->latest_state[c].eqns);
-    equation_system_free(&model->combined_state[c].eqns);
-
-    equation_system_free(&model->latest_state[c].strength_solver.eqns);
-    equation_system_free(&model->combined_state[c].strength_solver.eqns);
-  }
-  memset(model, 0, sizeof(*model));
-}
-
-// Extracts the neighborhood defined by coords around point (x, y) from
-// the difference between the data and denoised images. Also extracts the
-// entry (possibly downsampled) for (x, y) in the alt_data (e.g., luma).
-#define EXTRACT_AR_ROW(INT_TYPE, suffix)                                   \
-  static double extract_ar_row_##suffix(                                   \
-      int(*coords)[2], int num_coords, const INT_TYPE *const data,         \
-      const INT_TYPE *const denoised, int stride, int sub_log2[2],         \
-      const INT_TYPE *const alt_data, const INT_TYPE *const alt_denoised,  \
-      int alt_stride, int x, int y, double *buffer) {                      \
-    for (int i = 0; i < num_coords; ++i) {                                 \
-      const int x_i = x + coords[i][0], y_i = y + coords[i][1];            \
-      buffer[i] =                                                          \
-          (double)data[y_i * stride + x_i] - denoised[y_i * stride + x_i]; \
-    }                                                                      \
-    const double val =                                                     \
-        (double)data[y * stride + x] - denoised[y * stride + x];           \
-                                                                           \
-    if (alt_data && alt_denoised) {                                        \
-      double avg_data = 0, avg_denoised = 0;                               \
-      int num_samples = 0;                                                 \
-      for (int dy_i = 0; dy_i < (1 << sub_log2[1]); dy_i++) {              \
-        const int y_up = (y << sub_log2[1]) + dy_i;                        \
-        for (int dx_i = 0; dx_i < (1 << sub_log2[0]); dx_i++) {            \
-          const int x_up = (x << sub_log2[0]) + dx_i;                      \
-          avg_data += alt_data[y_up * alt_stride + x_up];                  \
-          avg_denoised += alt_denoised[y_up * alt_stride + x_up];          \
-          num_samples++;                                                   \
-        }                                                                  \
-      }                                                                    \
-      buffer[num_coords] = (avg_data - avg_denoised) / num_samples;        \
-    }                                                                      \
-    return val;                                                            \
-  }
-
-EXTRACT_AR_ROW(uint8_t, lowbd);
-EXTRACT_AR_ROW(uint16_t, highbd);
-
-static int add_block_observations(
-    aom_noise_model_t *noise_model, int c, const uint8_t *const data,
-    const uint8_t *const denoised, int w, int h, int stride, int sub_log2[2],
-    const uint8_t *const alt_data, const uint8_t *const alt_denoised,
-    int alt_stride, const uint8_t *const flat_blocks, int block_size,
-    int num_blocks_w, int num_blocks_h) {
-  const int lag = noise_model->params.lag;
-  const int num_coords = noise_model->n;
-  const double normalization = (1 << noise_model->params.bit_depth) - 1;
-  double *A = noise_model->latest_state[c].eqns.A;
-  double *b = noise_model->latest_state[c].eqns.b;
-  double *buffer = (double *)aom_malloc(sizeof(*buffer) * (num_coords + 1));
-  const int n = noise_model->latest_state[c].eqns.n;
-
-  if (!buffer) {
-    fprintf(stderr, "Unable to allocate buffer of size %d\n", num_coords + 1);
-    return 0;
-  }
-  for (int by = 0; by < num_blocks_h; ++by) {
-    const int y_o = by * (block_size >> sub_log2[1]);
-    for (int bx = 0; bx < num_blocks_w; ++bx) {
-      const int x_o = bx * (block_size >> sub_log2[0]);
-      if (!flat_blocks[by * num_blocks_w + bx]) {
-        continue;
-      }
-      int y_start =
-          (by > 0 && flat_blocks[(by - 1) * num_blocks_w + bx]) ? 0 : lag;
-      int x_start =
-          (bx > 0 && flat_blocks[by * num_blocks_w + bx - 1]) ? 0 : lag;
-      int y_end = AOMMIN((h >> sub_log2[1]) - by * (block_size >> sub_log2[1]),
-                         block_size >> sub_log2[1]);
-      int x_end = AOMMIN(
-          (w >> sub_log2[0]) - bx * (block_size >> sub_log2[0]) - lag,
-          (bx + 1 < num_blocks_w && flat_blocks[by * num_blocks_w + bx + 1])
-              ? (block_size >> sub_log2[0])
-              : ((block_size >> sub_log2[0]) - lag));
-      for (int y = y_start; y < y_end; ++y) {
-        for (int x = x_start; x < x_end; ++x) {
-          const double val =
-              noise_model->params.use_highbd
-                  ? extract_ar_row_highbd(noise_model->coords, num_coords,
-                                          (const uint16_t *const)data,
-                                          (const uint16_t *const)denoised,
-                                          stride, sub_log2,
-                                          (const uint16_t *const)alt_data,
-                                          (const uint16_t *const)alt_denoised,
-                                          alt_stride, x + x_o, y + y_o, buffer)
-                  : extract_ar_row_lowbd(noise_model->coords, num_coords, data,
-                                         denoised, stride, sub_log2, alt_data,
-                                         alt_denoised, alt_stride, x + x_o,
-                                         y + y_o, buffer);
-          for (int i = 0; i < n; ++i) {
-            for (int j = 0; j < n; ++j) {
-              A[i * n + j] +=
-                  (buffer[i] * buffer[j]) / (normalization * normalization);
-            }
-            b[i] += (buffer[i] * val) / (normalization * normalization);
-          }
-          noise_model->latest_state[c].num_observations++;
-        }
-      }
-    }
-  }
-  aom_free(buffer);
-  return 1;
-}
-
-static void add_noise_std_observations(
-    aom_noise_model_t *noise_model, int c, const double *coeffs,
-    const uint8_t *const data, const uint8_t *const denoised, int w, int h,
-    int stride, int sub_log2[2], const uint8_t *const alt_data, int alt_stride,
-    const uint8_t *const flat_blocks, int block_size, int num_blocks_w,
-    int num_blocks_h) {
-  const int num_coords = noise_model->n;
-  aom_noise_strength_solver_t *noise_strength_solver =
-      &noise_model->latest_state[c].strength_solver;
-
-  const aom_noise_strength_solver_t *noise_strength_luma =
-      &noise_model->latest_state[0].strength_solver;
-  const double luma_gain = noise_model->latest_state[0].ar_gain;
-  const double noise_gain = noise_model->latest_state[c].ar_gain;
-  for (int by = 0; by < num_blocks_h; ++by) {
-    const int y_o = by * (block_size >> sub_log2[1]);
-    for (int bx = 0; bx < num_blocks_w; ++bx) {
-      const int x_o = bx * (block_size >> sub_log2[0]);
-      if (!flat_blocks[by * num_blocks_w + bx]) {
-        continue;
-      }
-      const int num_samples_h =
-          AOMMIN((h >> sub_log2[1]) - by * (block_size >> sub_log2[1]),
-                 block_size >> sub_log2[1]);
-      const int num_samples_w =
-          AOMMIN((w >> sub_log2[0]) - bx * (block_size >> sub_log2[0]),
-                 (block_size >> sub_log2[0]));
-      // Make sure that we have a reasonable amount of samples to consider the
-      // block
-      if (num_samples_w * num_samples_h > block_size) {
-        const double block_mean = get_block_mean(
-            alt_data ? alt_data : data, w, h, alt_data ? alt_stride : stride,
-            x_o << sub_log2[0], y_o << sub_log2[1], block_size,
-            noise_model->params.use_highbd);
-        const double noise_var = get_noise_var(
-            data, denoised, stride, w >> sub_log2[0], h >> sub_log2[1], x_o,
-            y_o, block_size >> sub_log2[0], block_size >> sub_log2[1],
-            noise_model->params.use_highbd);
-        // We want to remove the part of the noise that came from being
-        // correlated with luma. Note that the noise solver for luma must
-        // have already been run.
-        const double luma_strength =
-            c > 0 ? luma_gain * noise_strength_solver_get_value(
-                                    noise_strength_luma, block_mean)
-                  : 0;
-        const double corr = c > 0 ? coeffs[num_coords] : 0;
-        // Chroma noise:
-        //    N(0, noise_var) = N(0, uncorr_var) + corr * N(0, luma_strength^2)
-        // The uncorrelated component:
-        //   uncorr_var = noise_var - (corr * luma_strength)^2
-        // But don't allow fully correlated noise (hence the max), since the
-        // synthesis cannot model it.
-        const double uncorr_std = sqrt(
-            AOMMAX(noise_var / 16, noise_var - pow(corr * luma_strength, 2)));
-        // After we've removed correlation with luma, undo the gain that will
-        // come from running the IIR filter.
-        const double adjusted_strength = uncorr_std / noise_gain;
-        aom_noise_strength_solver_add_measurement(
-            noise_strength_solver, block_mean, adjusted_strength);
-      }
-    }
-  }
-}
-
-// Return true if the noise estimate appears to be different from the combined
-// (multi-frame) estimate. The difference is measured by checking whether the
-// AR coefficients have diverged (using a threshold on normalized cross
-// correlation), or whether the noise strength has changed.
-static int is_noise_model_different(aom_noise_model_t *const noise_model) {
-  // These thresholds are kind of arbitrary and will likely need further tuning
-  // (or exported as parameters). The threshold on noise strength is a weighted
-  // difference between the noise strength histograms
-  const double kCoeffThreshold = 0.9;
-  const double kStrengthThreshold =
-      0.005 * (1 << (noise_model->params.bit_depth - 8));
-  for (int c = 0; c < 1; ++c) {
-    const double corr =
-        aom_normalized_cross_correlation(noise_model->latest_state[c].eqns.x,
-                                         noise_model->combined_state[c].eqns.x,
-                                         noise_model->combined_state[c].eqns.n);
-    if (corr < kCoeffThreshold) return 1;
-
-    const double dx =
-        1.0 / noise_model->latest_state[c].strength_solver.num_bins;
-
-    const aom_equation_system_t *latest_eqns =
-        &noise_model->latest_state[c].strength_solver.eqns;
-    const aom_equation_system_t *combined_eqns =
-        &noise_model->combined_state[c].strength_solver.eqns;
-    double diff = 0;
-    double total_weight = 0;
-    for (int j = 0; j < latest_eqns->n; ++j) {
-      double weight = 0;
-      for (int i = 0; i < latest_eqns->n; ++i) {
-        weight += latest_eqns->A[i * latest_eqns->n + j];
-      }
-      weight = sqrt(weight);
-      diff += weight * fabs(latest_eqns->x[j] - combined_eqns->x[j]);
-      total_weight += weight;
-    }
-    if (diff * dx / total_weight > kStrengthThreshold) return 1;
-  }
-  return 0;
-}
-
-static int ar_equation_system_solve(aom_noise_state_t *state, int is_chroma) {
-  const int ret = equation_system_solve(&state->eqns);
-  state->ar_gain = 1.0;
-  if (!ret) return ret;
-
-  // Update the AR gain from the equation system as it will be used to fit
-  // the noise strength as a function of intensity.  In the Yule-Walker
-  // equations, the diagonal should be the variance of the correlated noise.
-  // In the case of the least squares estimate, there will be some variability
-  // in the diagonal. So use the mean of the diagonal as the estimate of
-  // overall variance (this works for least squares or Yule-Walker formulation).
-  double var = 0;
-  const int n = state->eqns.n;
-  for (int i = 0; i < (state->eqns.n - is_chroma); ++i) {
-    var += state->eqns.A[i * n + i] / state->num_observations;
-  }
-  var /= (n - is_chroma);
-
-  // Keep track of E(Y^2) = <b, x> + E(X^2)
-  // In the case that we are using chroma and have an estimate of correlation
-  // with luma we adjust that estimate slightly to remove the correlated bits by
-  // subtracting out the last column of a scaled by our correlation estimate
-  // from b. E(y^2) = <b - A(:, end)*x(end), x>
-  double sum_covar = 0;
-  for (int i = 0; i < state->eqns.n - is_chroma; ++i) {
-    double bi = state->eqns.b[i];
-    if (is_chroma) {
-      bi -= state->eqns.A[i * n + (n - 1)] * state->eqns.x[n - 1];
-    }
-    sum_covar += (bi * state->eqns.x[i]) / state->num_observations;
-  }
-  // Now, get an estimate of the variance of uncorrelated noise signal and use
-  // it to determine the gain of the AR filter.
-  const double noise_var = AOMMAX(var - sum_covar, 1e-6);
-  state->ar_gain = AOMMAX(1, sqrt(AOMMAX(var / noise_var, 1e-6)));
-  return ret;
-}
-
-aom_noise_status_t aom_noise_model_update(
-    aom_noise_model_t *const noise_model, const uint8_t *const data[3],
-    const uint8_t *const denoised[3], int w, int h, int stride[3],
-    int chroma_sub_log2[2], const uint8_t *const flat_blocks, int block_size) {
-  const int num_blocks_w = (w + block_size - 1) / block_size;
-  const int num_blocks_h = (h + block_size - 1) / block_size;
-  int y_model_different = 0;
-  int num_blocks = 0;
-  int i = 0, channel = 0;
-
-  if (block_size <= 1) {
-    fprintf(stderr, "block_size = %d must be > 1\n", block_size);
-    return AOM_NOISE_STATUS_INVALID_ARGUMENT;
-  }
-
-  if (block_size < noise_model->params.lag * 2 + 1) {
-    fprintf(stderr, "block_size = %d must be >= %d\n", block_size,
-            noise_model->params.lag * 2 + 1);
-    return AOM_NOISE_STATUS_INVALID_ARGUMENT;
-  }
-
-  // Clear the latest equation system
-  for (i = 0; i < 3; ++i) {
-    equation_system_clear(&noise_model->latest_state[i].eqns);
-    noise_model->latest_state[i].num_observations = 0;
-    noise_strength_solver_clear(&noise_model->latest_state[i].strength_solver);
-  }
-
-  // Check that we have enough flat blocks
-  for (i = 0; i < num_blocks_h * num_blocks_w; ++i) {
-    if (flat_blocks[i]) {
-      num_blocks++;
-    }
-  }
-
-  if (num_blocks <= 1) {
-    fprintf(stderr, "Not enough flat blocks to update noise estimate\n");
-    return AOM_NOISE_STATUS_INSUFFICIENT_FLAT_BLOCKS;
-  }
-
-  for (channel = 0; channel < 3; ++channel) {
-    int no_subsampling[2] = { 0, 0 };
-    const uint8_t *alt_data = channel > 0 ? data[0] : 0;
-    const uint8_t *alt_denoised = channel > 0 ? denoised[0] : 0;
-    int *sub = channel > 0 ? chroma_sub_log2 : no_subsampling;
-    const int is_chroma = channel != 0;
-    if (!data[channel] || !denoised[channel]) break;
-    if (!add_block_observations(noise_model, channel, data[channel],
-                                denoised[channel], w, h, stride[channel], sub,
-                                alt_data, alt_denoised, stride[0], flat_blocks,
-                                block_size, num_blocks_w, num_blocks_h)) {
-      fprintf(stderr, "Adding block observation failed\n");
-      return AOM_NOISE_STATUS_INTERNAL_ERROR;
-    }
-
-    if (!ar_equation_system_solve(&noise_model->latest_state[channel],
-                                  is_chroma)) {
-      if (is_chroma) {
-        set_chroma_coefficient_fallback_soln(
-            &noise_model->latest_state[channel].eqns);
-      } else {
-        fprintf(stderr, "Solving latest noise equation system failed %d!\n",
-                channel);
-        return AOM_NOISE_STATUS_INTERNAL_ERROR;
-      }
-    }
-
-    add_noise_std_observations(
-        noise_model, channel, noise_model->latest_state[channel].eqns.x,
-        data[channel], denoised[channel], w, h, stride[channel], sub, alt_data,
-        stride[0], flat_blocks, block_size, num_blocks_w, num_blocks_h);
-
-    if (!aom_noise_strength_solver_solve(
-            &noise_model->latest_state[channel].strength_solver)) {
-      fprintf(stderr, "Solving latest noise strength failed!\n");
-      return AOM_NOISE_STATUS_INTERNAL_ERROR;
-    }
-
-    // Check noise characteristics and return if error.
-    if (channel == 0 &&
-        noise_model->combined_state[channel].strength_solver.num_equations >
-            0 &&
-        is_noise_model_different(noise_model)) {
-      y_model_different = 1;
-    }
-
-    // Don't update the combined stats if the y model is different.
-    if (y_model_different) continue;
-
-    noise_model->combined_state[channel].num_observations +=
-        noise_model->latest_state[channel].num_observations;
-    equation_system_add(&noise_model->combined_state[channel].eqns,
-                        &noise_model->latest_state[channel].eqns);
-    if (!ar_equation_system_solve(&noise_model->combined_state[channel],
-                                  is_chroma)) {
-      if (is_chroma) {
-        set_chroma_coefficient_fallback_soln(
-            &noise_model->combined_state[channel].eqns);
-      } else {
-        fprintf(stderr, "Solving combined noise equation system failed %d!\n",
-                channel);
-        return AOM_NOISE_STATUS_INTERNAL_ERROR;
-      }
-    }
-
-    noise_strength_solver_add(
-        &noise_model->combined_state[channel].strength_solver,
-        &noise_model->latest_state[channel].strength_solver);
-
-    if (!aom_noise_strength_solver_solve(
-            &noise_model->combined_state[channel].strength_solver)) {
-      fprintf(stderr, "Solving combined noise strength failed!\n");
-      return AOM_NOISE_STATUS_INTERNAL_ERROR;
-    }
-  }
-
-  return y_model_different ? AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE
-                           : AOM_NOISE_STATUS_OK;
-}
-
-void aom_noise_model_save_latest(aom_noise_model_t *noise_model) {
-  for (int c = 0; c < 3; c++) {
-    equation_system_copy(&noise_model->combined_state[c].eqns,
-                         &noise_model->latest_state[c].eqns);
-    equation_system_copy(&noise_model->combined_state[c].strength_solver.eqns,
-                         &noise_model->latest_state[c].strength_solver.eqns);
-    noise_model->combined_state[c].strength_solver.num_equations =
-        noise_model->latest_state[c].strength_solver.num_equations;
-    noise_model->combined_state[c].num_observations =
-        noise_model->latest_state[c].num_observations;
-    noise_model->combined_state[c].ar_gain =
-        noise_model->latest_state[c].ar_gain;
-  }
-}
-
-int aom_noise_model_get_grain_parameters(aom_noise_model_t *const noise_model,
-                                         aom_film_grain_t *film_grain) {
-  if (noise_model->params.lag > 3) {
-    fprintf(stderr, "params.lag = %d > 3\n", noise_model->params.lag);
-    return 0;
-  }
-  uint16_t random_seed = film_grain->random_seed;
-  memset(film_grain, 0, sizeof(*film_grain));
-  film_grain->random_seed = random_seed;
-
-  film_grain->apply_grain = 1;
-  film_grain->update_parameters = 1;
-
-  film_grain->ar_coeff_lag = noise_model->params.lag;
-
-  // Convert the scaling functions to 8 bit values
-  aom_noise_strength_lut_t scaling_points[3];
-  aom_noise_strength_solver_fit_piecewise(
-      &noise_model->combined_state[0].strength_solver, 14, scaling_points + 0);
-  aom_noise_strength_solver_fit_piecewise(
-      &noise_model->combined_state[1].strength_solver, 10, scaling_points + 1);
-  aom_noise_strength_solver_fit_piecewise(
-      &noise_model->combined_state[2].strength_solver, 10, scaling_points + 2);
-
-  // Both the domain and the range of the scaling functions in the film_grain
-  // are normalized to 8-bit (e.g., they are implicitly scaled during grain
-  // synthesis).
-  const double strength_divisor = 1 << (noise_model->params.bit_depth - 8);
-  double max_scaling_value = 1e-4;
-  for (int c = 0; c < 3; ++c) {
-    for (int i = 0; i < scaling_points[c].num_points; ++i) {
-      scaling_points[c].points[i][0] =
-          AOMMIN(255, scaling_points[c].points[i][0] / strength_divisor);
-      scaling_points[c].points[i][1] =
-          AOMMIN(255, scaling_points[c].points[i][1] / strength_divisor);
-      max_scaling_value =
-          AOMMAX(scaling_points[c].points[i][1], max_scaling_value);
-    }
-  }
-
-  // Scaling_shift values are in the range [8,11]
-  const int max_scaling_value_log2 =
-      clamp((int)floor(log2(max_scaling_value) + 1), 2, 5);
-  film_grain->scaling_shift = 5 + (8 - max_scaling_value_log2);
-
-  const double scale_factor = 1 << (8 - max_scaling_value_log2);
-  film_grain->num_y_points = scaling_points[0].num_points;
-  film_grain->num_cb_points = scaling_points[1].num_points;
-  film_grain->num_cr_points = scaling_points[2].num_points;
-
-  int(*film_grain_scaling[3])[2] = {
-    film_grain->scaling_points_y,
-    film_grain->scaling_points_cb,
-    film_grain->scaling_points_cr,
-  };
-  for (int c = 0; c < 3; c++) {
-    for (int i = 0; i < scaling_points[c].num_points; ++i) {
-      film_grain_scaling[c][i][0] = (int)(scaling_points[c].points[i][0] + 0.5);
-      film_grain_scaling[c][i][1] = clamp(
-          (int)(scale_factor * scaling_points[c].points[i][1] + 0.5), 0, 255);
-    }
-  }
-  aom_noise_strength_lut_free(scaling_points + 0);
-  aom_noise_strength_lut_free(scaling_points + 1);
-  aom_noise_strength_lut_free(scaling_points + 2);
-
-  // Convert the ar_coeffs into 8-bit values
-  const int n_coeff = noise_model->combined_state[0].eqns.n;
-  double max_coeff = 1e-4, min_coeff = -1e-4;
-  double y_corr[2] = { 0, 0 };
-  double avg_luma_strength = 0;
-  for (int c = 0; c < 3; c++) {
-    aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
-    for (int i = 0; i < n_coeff; ++i) {
-      max_coeff = AOMMAX(max_coeff, eqns->x[i]);
-      min_coeff = AOMMIN(min_coeff, eqns->x[i]);
-    }
-    // Since the correlation between luma/chroma was computed in an already
-    // scaled space, we adjust it in the un-scaled space.
-    aom_noise_strength_solver_t *solver =
-        &noise_model->combined_state[c].strength_solver;
-    // Compute a weighted average of the strength for the channel.
-    double average_strength = 0, total_weight = 0;
-    for (int i = 0; i < solver->eqns.n; ++i) {
-      double w = 0;
-      for (int j = 0; j < solver->eqns.n; ++j) {
-        w += solver->eqns.A[i * solver->eqns.n + j];
-      }
-      w = sqrt(w);
-      average_strength += solver->eqns.x[i] * w;
-      total_weight += w;
-    }
-    if (total_weight == 0)
-      average_strength = 1;
-    else
-      average_strength /= total_weight;
-    if (c == 0) {
-      avg_luma_strength = average_strength;
-    } else {
-      y_corr[c - 1] = avg_luma_strength * eqns->x[n_coeff] / average_strength;
-      max_coeff = AOMMAX(max_coeff, y_corr[c - 1]);
-      min_coeff = AOMMIN(min_coeff, y_corr[c - 1]);
-    }
-  }
-  // Shift value: AR coeffs range (values 6-9)
-  // 6: [-2, 2),  7: [-1, 1), 8: [-0.5, 0.5), 9: [-0.25, 0.25)
-  film_grain->ar_coeff_shift =
-      clamp(7 - (int)AOMMAX(1 + floor(log2(max_coeff)), ceil(log2(-min_coeff))),
-            6, 9);
-  double scale_ar_coeff = 1 << film_grain->ar_coeff_shift;
-  int *ar_coeffs[3] = {
-    film_grain->ar_coeffs_y,
-    film_grain->ar_coeffs_cb,
-    film_grain->ar_coeffs_cr,
-  };
-  for (int c = 0; c < 3; ++c) {
-    aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
-    for (int i = 0; i < n_coeff; ++i) {
-      ar_coeffs[c][i] =
-          clamp((int)round(scale_ar_coeff * eqns->x[i]), -128, 127);
-    }
-    if (c > 0) {
-      ar_coeffs[c][n_coeff] =
-          clamp((int)round(scale_ar_coeff * y_corr[c - 1]), -128, 127);
-    }
-  }
-
-  // At the moment, the noise modeling code assumes that the chroma scaling
-  // functions are a function of luma.
-  film_grain->cb_mult = 128;       // 8 bits
-  film_grain->cb_luma_mult = 192;  // 8 bits
-  film_grain->cb_offset = 256;     // 9 bits
-
-  film_grain->cr_mult = 128;       // 8 bits
-  film_grain->cr_luma_mult = 192;  // 8 bits
-  film_grain->cr_offset = 256;     // 9 bits
-
-  film_grain->chroma_scaling_from_luma = 0;
-  film_grain->grain_scale_shift = 0;
-  film_grain->overlap_flag = 1;
-  return 1;
-}
-
-static void pointwise_multiply(const float *a, float *b, int n) {
-  for (int i = 0; i < n; ++i) {
-    b[i] *= a[i];
-  }
-}
-
-static float *get_half_cos_window(int block_size) {
-  float *window_function =
-      (float *)aom_malloc(block_size * block_size * sizeof(*window_function));
-  for (int y = 0; y < block_size; ++y) {
-    const double cos_yd = cos((.5 + y) * PI / block_size - PI / 2);
-    for (int x = 0; x < block_size; ++x) {
-      const double cos_xd = cos((.5 + x) * PI / block_size - PI / 2);
-      window_function[y * block_size + x] = (float)(cos_yd * cos_xd);
-    }
-  }
-  return window_function;
-}
-
-#define DITHER_AND_QUANTIZE(INT_TYPE, suffix)                               \
-  static void dither_and_quantize_##suffix(                                 \
-      float *result, int result_stride, INT_TYPE *denoised, int w, int h,   \
-      int stride, int chroma_sub_w, int chroma_sub_h, int block_size,       \
-      float block_normalization) {                                          \
-    for (int y = 0; y < (h >> chroma_sub_h); ++y) {                         \
-      for (int x = 0; x < (w >> chroma_sub_w); ++x) {                       \
-        const int result_idx =                                              \
-            (y + (block_size >> chroma_sub_h)) * result_stride + x +        \
-            (block_size >> chroma_sub_w);                                   \
-        INT_TYPE new_val = (INT_TYPE)AOMMIN(                                \
-            AOMMAX(result[result_idx] * block_normalization + 0.5f, 0),     \
-            block_normalization);                                           \
-        const float err =                                                   \
-            -(((float)new_val) / block_normalization - result[result_idx]); \
-        denoised[y * stride + x] = new_val;                                 \
-        if (x + 1 < (w >> chroma_sub_w)) {                                  \
-          result[result_idx + 1] += err * 7.0f / 16.0f;                     \
-        }                                                                   \
-        if (y + 1 < (h >> chroma_sub_h)) {                                  \
-          if (x > 0) {                                                      \
-            result[result_idx + result_stride - 1] += err * 3.0f / 16.0f;   \
-          }                                                                 \
-          result[result_idx + result_stride] += err * 5.0f / 16.0f;         \
-          if (x + 1 < (w >> chroma_sub_w)) {                                \
-            result[result_idx + result_stride + 1] += err * 1.0f / 16.0f;   \
-          }                                                                 \
-        }                                                                   \
-      }                                                                     \
-    }                                                                       \
-  }
-
-DITHER_AND_QUANTIZE(uint8_t, lowbd);
-DITHER_AND_QUANTIZE(uint16_t, highbd);
-
-int aom_wiener_denoise_2d(const uint8_t *const data[3], uint8_t *denoised[3],
-                          int w, int h, int stride[3], int chroma_sub[2],
-                          float *noise_psd[3], int block_size, int bit_depth,
-                          int use_highbd) {
-  float *plane = NULL, *block = NULL, *window_full = NULL,
-        *window_chroma = NULL;
-  double *block_d = NULL, *plane_d = NULL;
-  struct aom_noise_tx_t *tx_full = NULL;
-  struct aom_noise_tx_t *tx_chroma = NULL;
-  const int num_blocks_w = (w + block_size - 1) / block_size;
-  const int num_blocks_h = (h + block_size - 1) / block_size;
-  const int result_stride = (num_blocks_w + 2) * block_size;
-  const int result_height = (num_blocks_h + 2) * block_size;
-  float *result = NULL;
-  int init_success = 1;
-  aom_flat_block_finder_t block_finder_full;
-  aom_flat_block_finder_t block_finder_chroma;
-  const float kBlockNormalization = (float)((1 << bit_depth) - 1);
-  if (chroma_sub[0] != chroma_sub[1]) {
-    fprintf(stderr,
-            "aom_wiener_denoise_2d doesn't handle different chroma "
-            "subsampling");
-    return 0;
-  }
-  init_success &= aom_flat_block_finder_init(&block_finder_full, block_size,
-                                             bit_depth, use_highbd);
-  result = (float *)aom_malloc((num_blocks_h + 2) * block_size * result_stride *
-                               sizeof(*result));
-  plane = (float *)aom_malloc(block_size * block_size * sizeof(*plane));
-  block =
-      (float *)aom_memalign(32, 2 * block_size * block_size * sizeof(*block));
-  block_d = (double *)aom_malloc(block_size * block_size * sizeof(*block_d));
-  plane_d = (double *)aom_malloc(block_size * block_size * sizeof(*plane_d));
-  window_full = get_half_cos_window(block_size);
-  tx_full = aom_noise_tx_malloc(block_size);
-
-  if (chroma_sub[0] != 0) {
-    init_success &= aom_flat_block_finder_init(&block_finder_chroma,
-                                               block_size >> chroma_sub[0],
-                                               bit_depth, use_highbd);
-    window_chroma = get_half_cos_window(block_size >> chroma_sub[0]);
-    tx_chroma = aom_noise_tx_malloc(block_size >> chroma_sub[0]);
-  } else {
-    window_chroma = window_full;
-    tx_chroma = tx_full;
-  }
-
-  init_success &= (tx_full != NULL) && (tx_chroma != NULL) && (plane != NULL) &&
-                  (plane_d != NULL) && (block != NULL) && (block_d != NULL) &&
-                  (window_full != NULL) && (window_chroma != NULL) &&
-                  (result != NULL);
-  for (int c = init_success ? 0 : 3; c < 3; ++c) {
-    float *window_function = c == 0 ? window_full : window_chroma;
-    aom_flat_block_finder_t *block_finder = &block_finder_full;
-    const int chroma_sub_h = c > 0 ? chroma_sub[1] : 0;
-    const int chroma_sub_w = c > 0 ? chroma_sub[0] : 0;
-    struct aom_noise_tx_t *tx =
-        (c > 0 && chroma_sub[0] > 0) ? tx_chroma : tx_full;
-    if (!data[c] || !denoised[c]) continue;
-    if (c > 0 && chroma_sub[0] != 0) {
-      block_finder = &block_finder_chroma;
-    }
-    memset(result, 0, sizeof(*result) * result_stride * result_height);
-    // Do overlapped block processing (half overlapped). The block rows can
-    // easily be done in parallel
-    for (int offsy = 0; offsy < (block_size >> chroma_sub_h);
-         offsy += (block_size >> chroma_sub_h) / 2) {
-      for (int offsx = 0; offsx < (block_size >> chroma_sub_w);
-           offsx += (block_size >> chroma_sub_w) / 2) {
-        // Pad the boundary when processing each block-set.
-        for (int by = -1; by < num_blocks_h; ++by) {
-          for (int bx = -1; bx < num_blocks_w; ++bx) {
-            const int pixels_per_block =
-                (block_size >> chroma_sub_w) * (block_size >> chroma_sub_h);
-            aom_flat_block_finder_extract_block(
-                block_finder, data[c], w >> chroma_sub_w, h >> chroma_sub_h,
-                stride[c], bx * (block_size >> chroma_sub_w) + offsx,
-                by * (block_size >> chroma_sub_h) + offsy, plane_d, block_d);
-            for (int j = 0; j < pixels_per_block; ++j) {
-              block[j] = (float)block_d[j];
-              plane[j] = (float)plane_d[j];
-            }
-            pointwise_multiply(window_function, block, pixels_per_block);
-            aom_noise_tx_forward(tx, block);
-            aom_noise_tx_filter(tx, noise_psd[c]);
-            aom_noise_tx_inverse(tx, block);
-
-            // Apply window function to the plane approximation (we will apply
-            // it to the sum of plane + block when composing the results).
-            pointwise_multiply(window_function, plane, pixels_per_block);
-
-            for (int y = 0; y < (block_size >> chroma_sub_h); ++y) {
-              const int y_result =
-                  y + (by + 1) * (block_size >> chroma_sub_h) + offsy;
-              for (int x = 0; x < (block_size >> chroma_sub_w); ++x) {
-                const int x_result =
-                    x + (bx + 1) * (block_size >> chroma_sub_w) + offsx;
-                result[y_result * result_stride + x_result] +=
-                    (block[y * (block_size >> chroma_sub_w) + x] +
-                     plane[y * (block_size >> chroma_sub_w) + x]) *
-                    window_function[y * (block_size >> chroma_sub_w) + x];
-              }
-            }
-          }
-        }
-      }
-    }
-    if (use_highbd) {
-      dither_and_quantize_highbd(result, result_stride, (uint16_t *)denoised[c],
-                                 w, h, stride[c], chroma_sub_w, chroma_sub_h,
-                                 block_size, kBlockNormalization);
-    } else {
-      dither_and_quantize_lowbd(result, result_stride, denoised[c], w, h,
-                                stride[c], chroma_sub_w, chroma_sub_h,
-                                block_size, kBlockNormalization);
-    }
-  }
-  aom_free(result);
-  aom_free(plane);
-  aom_free(block);
-  aom_free(plane_d);
-  aom_free(block_d);
-  aom_free(window_full);
-
-  aom_noise_tx_free(tx_full);
-
-  aom_flat_block_finder_free(&block_finder_full);
-  if (chroma_sub[0] != 0) {
-    aom_flat_block_finder_free(&block_finder_chroma);
-    aom_free(window_chroma);
-    aom_noise_tx_free(tx_chroma);
-  }
-  return init_success;
-}
-
-struct aom_denoise_and_model_t {
-  int block_size;
-  int bit_depth;
-  float noise_level;
-
-  // Size of current denoised buffer and flat_block buffer
-  int width;
-  int height;
-  int y_stride;
-  int uv_stride;
-  int num_blocks_w;
-  int num_blocks_h;
-
-  // Buffers for image and noise_psd allocated on the fly
-  float *noise_psd[3];
-  uint8_t *denoised[3];
-  uint8_t *flat_blocks;
-
-  aom_flat_block_finder_t flat_block_finder;
-  aom_noise_model_t noise_model;
-};
-
-struct aom_denoise_and_model_t *aom_denoise_and_model_alloc(int bit_depth,
-                                                            int block_size,
-                                                            float noise_level) {
-  struct aom_denoise_and_model_t *ctx =
-      (struct aom_denoise_and_model_t *)aom_malloc(
-          sizeof(struct aom_denoise_and_model_t));
-  if (!ctx) {
-    fprintf(stderr, "Unable to allocate denoise_and_model struct\n");
-    return NULL;
-  }
-  memset(ctx, 0, sizeof(*ctx));
-
-  ctx->block_size = block_size;
-  ctx->noise_level = noise_level;
-  ctx->bit_depth = bit_depth;
-
-  ctx->noise_psd[0] =
-      aom_malloc(sizeof(*ctx->noise_psd[0]) * block_size * block_size);
-  ctx->noise_psd[1] =
-      aom_malloc(sizeof(*ctx->noise_psd[1]) * block_size * block_size);
-  ctx->noise_psd[2] =
-      aom_malloc(sizeof(*ctx->noise_psd[2]) * block_size * block_size);
-  if (!ctx->noise_psd[0] || !ctx->noise_psd[1] || !ctx->noise_psd[2]) {
-    fprintf(stderr, "Unable to allocate noise PSD buffers\n");
-    aom_denoise_and_model_free(ctx);
-    return NULL;
-  }
-  return ctx;
-}
-
-void aom_denoise_and_model_free(struct aom_denoise_and_model_t *ctx) {
-  aom_free(ctx->flat_blocks);
-  for (int i = 0; i < 3; ++i) {
-    aom_free(ctx->denoised[i]);
-    aom_free(ctx->noise_psd[i]);
-  }
-  aom_noise_model_free(&ctx->noise_model);
-  aom_flat_block_finder_free(&ctx->flat_block_finder);
-  aom_free(ctx);
-}
-
-static int denoise_and_model_realloc_if_necessary(
-    struct aom_denoise_and_model_t *ctx, YV12_BUFFER_CONFIG *sd) {
-  if (ctx->width == sd->y_width && ctx->height == sd->y_height &&
-      ctx->y_stride == sd->y_stride && ctx->uv_stride == sd->uv_stride)
-    return 1;
-  const int use_highbd = (sd->flags & YV12_FLAG_HIGHBITDEPTH) != 0;
-  const int block_size = ctx->block_size;
-
-  ctx->width = sd->y_width;
-  ctx->height = sd->y_height;
-  ctx->y_stride = sd->y_stride;
-  ctx->uv_stride = sd->uv_stride;
-
-  for (int i = 0; i < 3; ++i) {
-    aom_free(ctx->denoised[i]);
-    ctx->denoised[i] = NULL;
-  }
-  aom_free(ctx->flat_blocks);
-  ctx->flat_blocks = NULL;
-
-  ctx->denoised[0] = aom_malloc((sd->y_stride * sd->y_height) << use_highbd);
-  ctx->denoised[1] = aom_malloc((sd->uv_stride * sd->uv_height) << use_highbd);
-  ctx->denoised[2] = aom_malloc((sd->uv_stride * sd->uv_height) << use_highbd);
-  if (!ctx->denoised[0] || !ctx->denoised[1] || !ctx->denoised[2]) {
-    fprintf(stderr, "Unable to allocate denoise buffers\n");
-    return 0;
-  }
-  ctx->num_blocks_w = (sd->y_width + ctx->block_size - 1) / ctx->block_size;
-  ctx->num_blocks_h = (sd->y_height + ctx->block_size - 1) / ctx->block_size;
-  ctx->flat_blocks = aom_malloc(ctx->num_blocks_w * ctx->num_blocks_h);
-
-  aom_flat_block_finder_free(&ctx->flat_block_finder);
-  if (!aom_flat_block_finder_init(&ctx->flat_block_finder, ctx->block_size,
-                                  ctx->bit_depth, use_highbd)) {
-    fprintf(stderr, "Unable to init flat block finder\n");
-    return 0;
-  }
-
-  const aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 3,
-                                            ctx->bit_depth, use_highbd };
-  aom_noise_model_free(&ctx->noise_model);
-  if (!aom_noise_model_init(&ctx->noise_model, params)) {
-    fprintf(stderr, "Unable to init noise model\n");
-    return 0;
-  }
-
-  // Simply use a flat PSD (although we could use the flat blocks to estimate
-  // PSD) those to estimate an actual noise PSD)
-  const float y_noise_level =
-      aom_noise_psd_get_default_value(ctx->block_size, ctx->noise_level);
-  const float uv_noise_level = aom_noise_psd_get_default_value(
-      ctx->block_size >> sd->subsampling_x, ctx->noise_level);
-  for (int i = 0; i < block_size * block_size; ++i) {
-    ctx->noise_psd[0][i] = y_noise_level;
-    ctx->noise_psd[1][i] = ctx->noise_psd[2][i] = uv_noise_level;
-  }
-  return 1;
-}
-
-int aom_denoise_and_model_run(struct aom_denoise_and_model_t *ctx,
-                              YV12_BUFFER_CONFIG *sd,
-                              aom_film_grain_t *film_grain) {
-  const int block_size = ctx->block_size;
-  const int use_highbd = (sd->flags & YV12_FLAG_HIGHBITDEPTH) != 0;
-  uint8_t *raw_data[3] = {
-    use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->y_buffer) : sd->y_buffer,
-    use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->u_buffer) : sd->u_buffer,
-    use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->v_buffer) : sd->v_buffer,
-  };
-  const uint8_t *const data[3] = { raw_data[0], raw_data[1], raw_data[2] };
-  int strides[3] = { sd->y_stride, sd->uv_stride, sd->uv_stride };
-  int chroma_sub_log2[2] = { sd->subsampling_x, sd->subsampling_y };
-
-  if (!denoise_and_model_realloc_if_necessary(ctx, sd)) {
-    fprintf(stderr, "Unable to realloc buffers\n");
-    return 0;
-  }
-
-  aom_flat_block_finder_run(&ctx->flat_block_finder, data[0], sd->y_width,
-                            sd->y_height, strides[0], ctx->flat_blocks);
-
-  if (!aom_wiener_denoise_2d(data, ctx->denoised, sd->y_width, sd->y_height,
-                             strides, chroma_sub_log2, ctx->noise_psd,
-                             block_size, ctx->bit_depth, use_highbd)) {
-    fprintf(stderr, "Unable to denoise image\n");
-    return 0;
-  }
-
-  const aom_noise_status_t status = aom_noise_model_update(
-      &ctx->noise_model, data, (const uint8_t *const *)ctx->denoised,
-      sd->y_width, sd->y_height, strides, chroma_sub_log2, ctx->flat_blocks,
-      block_size);
-  int have_noise_estimate = 0;
-  if (status == AOM_NOISE_STATUS_OK) {
-    have_noise_estimate = 1;
-  } else if (status == AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE) {
-    aom_noise_model_save_latest(&ctx->noise_model);
-    have_noise_estimate = 1;
-  } else {
-    // Unable to update noise model; proceed if we have a previous estimate.
-    have_noise_estimate =
-        (ctx->noise_model.combined_state[0].strength_solver.num_equations > 0);
-  }
-
-  film_grain->apply_grain = 0;
-  if (have_noise_estimate) {
-    if (!aom_noise_model_get_grain_parameters(&ctx->noise_model, film_grain)) {
-      fprintf(stderr, "Unable to get grain parameters.\n");
-      return 0;
-    }
-    if (!film_grain->random_seed) {
-      film_grain->random_seed = 7391;
-    }
-    memcpy(raw_data[0], ctx->denoised[0],
-           (strides[0] * sd->y_height) << use_highbd);
-    memcpy(raw_data[1], ctx->denoised[1],
-           (strides[1] * sd->uv_height) << use_highbd);
-    memcpy(raw_data[2], ctx->denoised[2],
-           (strides[2] * sd->uv_height) << use_highbd);
-  }
-  return 1;
-}