summaryrefslogtreecommitdiffstats
path: root/media/libaom/src/aom_dsp/noise_util.c
blob: 87e8e9fecc89c35f2699efbf734f7be71c2a5f7f (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
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;
}