Update aom to v1.0.0

Update aom to commit id d14c5bb4f336ef1842046089849dee4a301fbbf0.

1 files changed, 1460 insertions, 0 deletions

diff --git a/third_party/aom/aom_dsp/noise_model.c b/third_party/aom/aom_dsp/noise_model.c
new file mode 100644
index 000000000..a1287f74f
--- /dev/null
+++ b/third_party/aom/aom_dsp/noise_model.c
@@ -0,0 +1,1460 @@
+/*
+ * 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;
+  }
+  memset(film_grain, 0, sizeof(*film_grain));
+
+  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;
+}