Use the upstream version of libwebp, v0.4.3.

third_party/libwebp/utils/filters.c

Index: third_party/libwebp/utils/filters.c
diff --git a/third_party/libwebp/utils/filters.c b/third_party/libwebp/utils/filters.c
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+++ b/third_party/libwebp/utils/filters.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.
+// -----------------------------------------------------------------------------
+//
+// Spatial prediction using various filters
+//
+// Author: Urvang (urvang@google.com)
+
+#include "./filters.h"
+#include <assert.h>
+#include <stdlib.h>
+#include <string.h>
+
+//------------------------------------------------------------------------------
+// Helpful macro.
+
+# define SANITY_CHECK(in, out)                                                 \
+  assert(in != NULL);                                                          \
+  assert(out != NULL);                                                         \
+  assert(width > 0);                                                           \
+  assert(height > 0);                                                          \
+  assert(stride >= width);                                                     \
+  assert(row >= 0 && num_rows > 0 && row + num_rows <= height);                \
+  (void)height;  // Silence unused warning.
+
+static WEBP_INLINE void PredictLine(const uint8_t* src, const uint8_t* pred,
+                                    uint8_t* dst, int length, int inverse) {
+  int i;
+  if (inverse) {
+    for (i = 0; i < length; ++i) dst[i] = src[i] + pred[i];
+  } else {
+    for (i = 0; i < length; ++i) dst[i] = src[i] - pred[i];
+  }
+}
+
+//------------------------------------------------------------------------------
+// Horizontal filter.
+
+static WEBP_INLINE void DoHorizontalFilter(const uint8_t* in,
+                                           int width, int height, int stride,
+                                           int row, int num_rows,
+                                           int inverse, uint8_t* out) {
+  const uint8_t* preds;
+  const size_t start_offset = row * stride;
+  const int last_row = row + num_rows;
+  SANITY_CHECK(in, out);
+  in += start_offset;
+  out += start_offset;
+  preds = inverse ? out : in;
+
+  if (row == 0) {
+    // Leftmost pixel is the same as input for topmost scanline.
+    out[0] = in[0];
+    PredictLine(in + 1, preds, out + 1, width - 1, inverse);
+    row = 1;
+    preds += stride;
+    in += stride;
+    out += stride;
+  }
+
+  // Filter line-by-line.
+  while (row < last_row) {
+    // Leftmost pixel is predicted from above.
+    PredictLine(in, preds - stride, out, 1, inverse);
+    PredictLine(in + 1, preds, out + 1, width - 1, inverse);
+    ++row;
+    preds += stride;
+    in += stride;
+    out += stride;
+  }
+}
+
+static void HorizontalFilter(const uint8_t* data, int width, int height,
+                             int stride, uint8_t* filtered_data) {
+  DoHorizontalFilter(data, width, height, stride, 0, height, 0, filtered_data);
+}
+
+static void HorizontalUnfilter(int width, int height, int stride, int row,
+                               int num_rows, uint8_t* data) {
+  DoHorizontalFilter(data, width, height, stride, row, num_rows, 1, data);
+}
+
+//------------------------------------------------------------------------------
+// Vertical filter.
+
+static WEBP_INLINE void DoVerticalFilter(const uint8_t* in,
+                                         int width, int height, int stride,
+                                         int row, int num_rows,
+                                         int inverse, uint8_t* out) {
+  const uint8_t* preds;
+  const size_t start_offset = row * stride;
+  const int last_row = row + num_rows;
+  SANITY_CHECK(in, out);
+  in += start_offset;
+  out += start_offset;
+  preds = inverse ? out : in;
+
+  if (row == 0) {
+    // Very first top-left pixel is copied.
+    out[0] = in[0];
+    // Rest of top scan-line is left-predicted.
+    PredictLine(in + 1, preds, out + 1, width - 1, inverse);
+    row = 1;
+    in += stride;
+    out += stride;
+  } else {
+    // We are starting from in-between. Make sure 'preds' points to prev row.
+    preds -= stride;
+  }
+
+  // Filter line-by-line.
+  while (row < last_row) {
+    PredictLine(in, preds, out, width, inverse);
+    ++row;
+    preds += stride;
+    in += stride;
+    out += stride;
+  }
+}
+
+static void VerticalFilter(const uint8_t* data, int width, int height,
+                           int stride, uint8_t* filtered_data) {
+  DoVerticalFilter(data, width, height, stride, 0, height, 0, filtered_data);
+}
+
+static void VerticalUnfilter(int width, int height, int stride, int row,
+                             int num_rows, uint8_t* data) {
+  DoVerticalFilter(data, width, height, stride, row, num_rows, 1, data);
+}
+
+//------------------------------------------------------------------------------
+// Gradient filter.
+
+static WEBP_INLINE int GradientPredictor(uint8_t a, uint8_t b, uint8_t c) {
+  const int g = a + b - c;
+  return ((g & ~0xff) == 0) ? g : (g < 0) ? 0 : 255;  // clip to 8bit
+}
+
+static WEBP_INLINE void DoGradientFilter(const uint8_t* in,
+                                         int width, int height, int stride,
+                                         int row, int num_rows,
+                                         int inverse, uint8_t* out) {
+  const uint8_t* preds;
+  const size_t start_offset = row * stride;
+  const int last_row = row + num_rows;
+  SANITY_CHECK(in, out);
+  in += start_offset;
+  out += start_offset;
+  preds = inverse ? out : in;
+
+  // left prediction for top scan-line
+  if (row == 0) {
+    out[0] = in[0];
+    PredictLine(in + 1, preds, out + 1, width - 1, inverse);
+    row = 1;
+    preds += stride;
+    in += stride;
+    out += stride;
+  }
+
+  // Filter line-by-line.
+  while (row < last_row) {
+    int w;
+    // leftmost pixel: predict from above.
+    PredictLine(in, preds - stride, out, 1, inverse);
+    for (w = 1; w < width; ++w) {
+      const int pred = GradientPredictor(preds[w - 1],
+                                         preds[w - stride],
+                                         preds[w - stride - 1]);
+      out[w] = in[w] + (inverse ? pred : -pred);
+    }
+    ++row;
+    preds += stride;
+    in += stride;
+    out += stride;
+  }
+}
+
+static void GradientFilter(const uint8_t* data, int width, int height,
+                           int stride, uint8_t* filtered_data) {
+  DoGradientFilter(data, width, height, stride, 0, height, 0, filtered_data);
+}
+
+static void GradientUnfilter(int width, int height, int stride, int row,
+                             int num_rows, uint8_t* data) {
+  DoGradientFilter(data, width, height, stride, row, num_rows, 1, data);
+}
+
+#undef SANITY_CHECK
+
+// -----------------------------------------------------------------------------
+// Quick estimate of a potentially interesting filter mode to try.
+
+#define SMAX 16
+#define SDIFF(a, b) (abs((a) - (b)) >> 4)   // Scoring diff, in [0..SMAX)
+
+WEBP_FILTER_TYPE EstimateBestFilter(const uint8_t* data,
+                                    int width, int height, int stride) {
+  int i, j;
+  int bins[WEBP_FILTER_LAST][SMAX];
+  memset(bins, 0, sizeof(bins));
+
+  // We only sample every other pixels. That's enough.
+  for (j = 2; j < height - 1; j += 2) {
+    const uint8_t* const p = data + j * stride;
+    int mean = p[0];
+    for (i = 2; i < width - 1; i += 2) {
+      const int diff0 = SDIFF(p[i], mean);
+      const int diff1 = SDIFF(p[i], p[i - 1]);
+      const int diff2 = SDIFF(p[i], p[i - width]);
+      const int grad_pred =
+          GradientPredictor(p[i - 1], p[i - width], p[i - width - 1]);
+      const int diff3 = SDIFF(p[i], grad_pred);
+      bins[WEBP_FILTER_NONE][diff0] = 1;
+      bins[WEBP_FILTER_HORIZONTAL][diff1] = 1;
+      bins[WEBP_FILTER_VERTICAL][diff2] = 1;
+      bins[WEBP_FILTER_GRADIENT][diff3] = 1;
+      mean = (3 * mean + p[i] + 2) >> 2;
+    }
+  }
+  {
+    int filter;
+    WEBP_FILTER_TYPE best_filter = WEBP_FILTER_NONE;
+    int best_score = 0x7fffffff;
+    for (filter = WEBP_FILTER_NONE; filter < WEBP_FILTER_LAST; ++filter) {
+      int score = 0;
+      for (i = 0; i < SMAX; ++i) {
+        if (bins[filter][i] > 0) {
+          score += i;
+        }
+      }
+      if (score < best_score) {
+        best_score = score;
+        best_filter = (WEBP_FILTER_TYPE)filter;
+      }
+    }
+    return best_filter;
+  }
+}
+
+#undef SMAX
+#undef SDIFF
+
+//------------------------------------------------------------------------------
+
+const WebPFilterFunc WebPFilters[WEBP_FILTER_LAST] = {
+  NULL,              // WEBP_FILTER_NONE
+  HorizontalFilter,  // WEBP_FILTER_HORIZONTAL
+  VerticalFilter,    // WEBP_FILTER_VERTICAL
+  GradientFilter     // WEBP_FILTER_GRADIENT
+};
+
+const WebPUnfilterFunc WebPUnfilters[WEBP_FILTER_LAST] = {
+  NULL,                // WEBP_FILTER_NONE
+  HorizontalUnfilter,  // WEBP_FILTER_HORIZONTAL
+  VerticalUnfilter,    // WEBP_FILTER_VERTICAL
+  GradientUnfilter     // WEBP_FILTER_GRADIENT
+};
+
+//------------------------------------------------------------------------------
+