Update aom to v1.0.0

Update aom to commit id d14c5bb4f336ef1842046089849dee4a301fbbf0.

1 files changed, 221 insertions, 0 deletions

diff --git a/third_party/aom/aom_dsp/noise_util.c b/third_party/aom/aom_dsp/noise_util.c
new file mode 100644
index 000000000..87e8e9fec
--- /dev/null
+++ b/third_party/aom/aom_dsp/noise_util.c
@@ -0,0 +1,221 @@
+/*
+ * 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/noise_util.h"
+#include "aom_dsp/fft_common.h"
+#include "aom_mem/aom_mem.h"
+#include "config/aom_dsp_rtcd.h"
+
+float aom_noise_psd_get_default_value(int block_size, float factor) {
+  return (factor * factor / 10000) * block_size * block_size / 8;
+}
+
+// Internal representation of noise transform. It keeps track of the
+// transformed data and a temporary working buffer to use during the
+// transform.
+struct aom_noise_tx_t {
+  float *tx_block;
+  float *temp;
+  int block_size;
+  void (*fft)(const float *, float *, float *);
+  void (*ifft)(const float *, float *, float *);
+};
+
+struct aom_noise_tx_t *aom_noise_tx_malloc(int block_size) {
+  struct aom_noise_tx_t *noise_tx =
+      (struct aom_noise_tx_t *)aom_malloc(sizeof(struct aom_noise_tx_t));
+  if (!noise_tx) return NULL;
+  memset(noise_tx, 0, sizeof(*noise_tx));
+  switch (block_size) {
+    case 2:
+      noise_tx->fft = aom_fft2x2_float;
+      noise_tx->ifft = aom_ifft2x2_float;
+      break;
+    case 4:
+      noise_tx->fft = aom_fft4x4_float;
+      noise_tx->ifft = aom_ifft4x4_float;
+      break;
+    case 8:
+      noise_tx->fft = aom_fft8x8_float;
+      noise_tx->ifft = aom_ifft8x8_float;
+      break;
+    case 16:
+      noise_tx->fft = aom_fft16x16_float;
+      noise_tx->ifft = aom_ifft16x16_float;
+      break;
+    case 32:
+      noise_tx->fft = aom_fft32x32_float;
+      noise_tx->ifft = aom_ifft32x32_float;
+      break;
+    default:
+      aom_free(noise_tx);
+      fprintf(stderr, "Unsupported block size %d\n", block_size);
+      return NULL;
+  }
+  noise_tx->block_size = block_size;
+  noise_tx->tx_block = (float *)aom_memalign(
+      32, 2 * sizeof(*noise_tx->tx_block) * block_size * block_size);
+  noise_tx->temp = (float *)aom_memalign(
+      32, 2 * sizeof(*noise_tx->temp) * block_size * block_size);
+  if (!noise_tx->tx_block || !noise_tx->temp) {
+    aom_noise_tx_free(noise_tx);
+    return NULL;
+  }
+  // Clear the buffers up front. Some outputs of the forward transform are
+  // real only (the imaginary component will never be touched)
+  memset(noise_tx->tx_block, 0,
+         2 * sizeof(*noise_tx->tx_block) * block_size * block_size);
+  memset(noise_tx->temp, 0,
+         2 * sizeof(*noise_tx->temp) * block_size * block_size);
+  return noise_tx;
+}
+
+void aom_noise_tx_forward(struct aom_noise_tx_t *noise_tx, const float *data) {
+  noise_tx->fft(data, noise_tx->temp, noise_tx->tx_block);
+}
+
+void aom_noise_tx_filter(struct aom_noise_tx_t *noise_tx, const float *psd) {
+  const int block_size = noise_tx->block_size;
+  const float kBeta = 1.1f;
+  const float kEps = 1e-6f;
+  for (int y = 0; y < block_size; ++y) {
+    for (int x = 0; x < block_size; ++x) {
+      int i = y * block_size + x;
+      float *c = noise_tx->tx_block + 2 * i;
+      const float p = c[0] * c[0] + c[1] * c[1];
+      if (p > kBeta * psd[i] && p > 1e-6) {
+        noise_tx->tx_block[2 * i + 0] *= (p - psd[i]) / AOMMAX(p, kEps);
+        noise_tx->tx_block[2 * i + 1] *= (p - psd[i]) / AOMMAX(p, kEps);
+      } else {
+        noise_tx->tx_block[2 * i + 0] *= (kBeta - 1.0f) / kBeta;
+        noise_tx->tx_block[2 * i + 1] *= (kBeta - 1.0f) / kBeta;
+      }
+    }
+  }
+}
+
+void aom_noise_tx_inverse(struct aom_noise_tx_t *noise_tx, float *data) {
+  const int n = noise_tx->block_size * noise_tx->block_size;
+  noise_tx->ifft(noise_tx->tx_block, noise_tx->temp, data);
+  for (int i = 0; i < n; ++i) {
+    data[i] /= n;
+  }
+}
+
+void aom_noise_tx_add_energy(const struct aom_noise_tx_t *noise_tx,
+                             float *psd) {
+  const int block_size = noise_tx->block_size;
+  for (int yb = 0; yb < block_size; ++yb) {
+    for (int xb = 0; xb <= block_size / 2; ++xb) {
+      float *c = noise_tx->tx_block + 2 * (yb * block_size + xb);
+      psd[yb * block_size + xb] += c[0] * c[0] + c[1] * c[1];
+    }
+  }
+}
+
+void aom_noise_tx_free(struct aom_noise_tx_t *noise_tx) {
+  if (!noise_tx) return;
+  aom_free(noise_tx->tx_block);
+  aom_free(noise_tx->temp);
+  aom_free(noise_tx);
+}
+
+double aom_normalized_cross_correlation(const double *a, const double *b,
+                                        int n) {
+  double c = 0;
+  double a_len = 0;
+  double b_len = 0;
+  for (int i = 0; i < n; ++i) {
+    a_len += a[i] * a[i];
+    b_len += b[i] * b[i];
+    c += a[i] * b[i];
+  }
+  return c / (sqrt(a_len) * sqrt(b_len));
+}
+
+int aom_noise_data_validate(const double *data, int w, int h) {
+  const double kVarianceThreshold = 2;
+  const double kMeanThreshold = 2;
+
+  int x = 0, y = 0;
+  int ret_value = 1;
+  double var = 0, mean = 0;
+  double *mean_x, *mean_y, *var_x, *var_y;
+
+  // Check that noise variance is not increasing in x or y
+  // and that the data is zero mean.
+  mean_x = (double *)aom_malloc(sizeof(*mean_x) * w);
+  var_x = (double *)aom_malloc(sizeof(*var_x) * w);
+  mean_y = (double *)aom_malloc(sizeof(*mean_x) * h);
+  var_y = (double *)aom_malloc(sizeof(*var_y) * h);
+
+  memset(mean_x, 0, sizeof(*mean_x) * w);
+  memset(var_x, 0, sizeof(*var_x) * w);
+  memset(mean_y, 0, sizeof(*mean_y) * h);
+  memset(var_y, 0, sizeof(*var_y) * h);
+
+  for (y = 0; y < h; ++y) {
+    for (x = 0; x < w; ++x) {
+      const double d = data[y * w + x];
+      var_x[x] += d * d;
+      var_y[y] += d * d;
+      mean_x[x] += d;
+      mean_y[y] += d;
+      var += d * d;
+      mean += d;
+    }
+  }
+  mean /= (w * h);
+  var = var / (w * h) - mean * mean;
+
+  for (y = 0; y < h; ++y) {
+    mean_y[y] /= h;
+    var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y];
+    if (fabs(var_y[y] - var) >= kVarianceThreshold) {
+      fprintf(stderr, "Variance distance too large %f %f\n", var_y[y], var);
+      ret_value = 0;
+      break;
+    }
+    if (fabs(mean_y[y] - mean) >= kMeanThreshold) {
+      fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean);
+      ret_value = 0;
+      break;
+    }
+  }
+
+  for (x = 0; x < w; ++x) {
+    mean_x[x] /= w;
+    var_x[x] = var_x[x] / w - mean_x[x] * mean_x[x];
+    if (fabs(var_x[x] - var) >= kVarianceThreshold) {
+      fprintf(stderr, "Variance distance too large %f %f\n", var_x[x], var);
+      ret_value = 0;
+      break;
+    }
+    if (fabs(mean_x[x] - mean) >= kMeanThreshold) {
+      fprintf(stderr, "Mean distance too large %f %f\n", mean_x[x], mean);
+      ret_value = 0;
+      break;
+    }
+  }
+
+  aom_free(mean_x);
+  aom_free(mean_y);
+  aom_free(var_x);
+  aom_free(var_y);
+
+  return ret_value;
+}