libwebp: update snapshot to v0.2.0-rc1

third_party/libwebp/utils/quant_levels.c

+// Copyright 2011 Google Inc. All Rights Reserved.
+//
+// This code is licensed under the same terms as WebM:
+//  Software License Agreement:  http://www.webmproject.org/license/software/
+//  Additional IP Rights Grant:  http://www.webmproject.org/license/additional/
+// -----------------------------------------------------------------------------
+//
+// 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.h"
+
+#if defined(__cplusplus) || defined(c_plusplus)
+extern "C" {
+#endif
+
+#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;
+}
+
+int DequantizeLevels(uint8_t* const data, int width, int height) {
+  if (data == NULL || width <= 0 || height <= 0) return 0;
+  // TODO(skal): implement gradient smoothing.
+  (void)data;
+  (void)width;
+  (void)height;
+  return 1;
+}
+
+#if defined(__cplusplus) || defined(c_plusplus)
+}    // extern "C"
+#endif