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- // Copyright 2011 Google Inc. All Rights Reserved.
- //
- // Use of this source code is governed by a BSD-style license
- // that can be found in the COPYING file in the root of the source
- // tree. An additional intellectual property rights grant can be found
- // in the file PATENTS. All contributing project authors may
- // be found in the AUTHORS file in the root of the source tree.
- // -----------------------------------------------------------------------------
- //
- // Quantize levels for specified number of quantization-levels ([2, 256]).
- // Min and max values are preserved (usual 0 and 255 for alpha plane).
- //
- // Author: Skal (pascal.massimino@gmail.com)
- #include <assert.h>
- #include "./quant_levels_utils.h"
- #define NUM_SYMBOLS 256
- #define MAX_ITER 6 // Maximum number of convergence steps.
- #define ERROR_THRESHOLD 1e-4 // MSE stopping criterion.
- // -----------------------------------------------------------------------------
- // Quantize levels.
- int QuantizeLevels(uint8_t* const data, int width, int height,
- int num_levels, uint64_t* const sse) {
- int freq[NUM_SYMBOLS] = { 0 };
- int q_level[NUM_SYMBOLS] = { 0 };
- double inv_q_level[NUM_SYMBOLS] = { 0 };
- int min_s = 255, max_s = 0;
- const size_t data_size = height * width;
- int i, num_levels_in, iter;
- double last_err = 1.e38, err = 0.;
- const double err_threshold = ERROR_THRESHOLD * data_size;
- if (data == NULL) {
- return 0;
- }
- if (width <= 0 || height <= 0) {
- return 0;
- }
- if (num_levels < 2 || num_levels > 256) {
- return 0;
- }
- {
- size_t n;
- num_levels_in = 0;
- for (n = 0; n < data_size; ++n) {
- num_levels_in += (freq[data[n]] == 0);
- if (min_s > data[n]) min_s = data[n];
- if (max_s < data[n]) max_s = data[n];
- ++freq[data[n]];
- }
- }
- if (num_levels_in <= num_levels) goto End; // nothing to do!
- // Start with uniformly spread centroids.
- for (i = 0; i < num_levels; ++i) {
- inv_q_level[i] = min_s + (double)(max_s - min_s) * i / (num_levels - 1);
- }
- // Fixed values. Won't be changed.
- q_level[min_s] = 0;
- q_level[max_s] = num_levels - 1;
- assert(inv_q_level[0] == min_s);
- assert(inv_q_level[num_levels - 1] == max_s);
- // k-Means iterations.
- for (iter = 0; iter < MAX_ITER; ++iter) {
- double q_sum[NUM_SYMBOLS] = { 0 };
- double q_count[NUM_SYMBOLS] = { 0 };
- int s, slot = 0;
- // Assign classes to representatives.
- for (s = min_s; s <= max_s; ++s) {
- // Keep track of the nearest neighbour 'slot'
- while (slot < num_levels - 1 &&
- 2 * s > inv_q_level[slot] + inv_q_level[slot + 1]) {
- ++slot;
- }
- if (freq[s] > 0) {
- q_sum[slot] += s * freq[s];
- q_count[slot] += freq[s];
- }
- q_level[s] = slot;
- }
- // Assign new representatives to classes.
- if (num_levels > 2) {
- for (slot = 1; slot < num_levels - 1; ++slot) {
- const double count = q_count[slot];
- if (count > 0.) {
- inv_q_level[slot] = q_sum[slot] / count;
- }
- }
- }
- // Compute convergence error.
- err = 0.;
- for (s = min_s; s <= max_s; ++s) {
- const double error = s - inv_q_level[q_level[s]];
- err += freq[s] * error * error;
- }
- // Check for convergence: we stop as soon as the error is no
- // longer improving.
- if (last_err - err < err_threshold) break;
- last_err = err;
- }
- // Remap the alpha plane to quantized values.
- {
- // double->int rounding operation can be costly, so we do it
- // once for all before remapping. We also perform the data[] -> slot
- // mapping, while at it (avoid one indirection in the final loop).
- uint8_t map[NUM_SYMBOLS];
- int s;
- size_t n;
- for (s = min_s; s <= max_s; ++s) {
- const int slot = q_level[s];
- map[s] = (uint8_t)(inv_q_level[slot] + .5);
- }
- // Final pass.
- for (n = 0; n < data_size; ++n) {
- data[n] = map[data[n]];
- }
- }
- End:
- // Store sum of squared error if needed.
- if (sse != NULL) *sse = (uint64_t)err;
- return 1;
- }
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