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-rw-r--r--media/libwebp/utils/quant_levels_utils.c140
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diff --git a/media/libwebp/utils/quant_levels_utils.c b/media/libwebp/utils/quant_levels_utils.c
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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;
+}
+