This source file includes following definitions.
- PredictLine
- DoHorizontalFilter
- HorizontalFilter
- HorizontalUnfilter
- DoVerticalFilter
- VerticalFilter
- VerticalUnfilter
- GradientPredictor
- DoGradientFilter
- GradientFilter
- GradientUnfilter
- EstimateBestFilter
#include "./filters.h"
#include <assert.h>
#include <stdlib.h>
#include <string.h>
# 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;
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];
}
}
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) {
out[0] = in[0];
PredictLine(in + 1, preds, out + 1, width - 1, inverse);
row = 1;
preds += stride;
in += stride;
out += stride;
}
while (row < last_row) {
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);
}
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) {
out[0] = in[0];
PredictLine(in + 1, preds, out + 1, width - 1, inverse);
row = 1;
in += stride;
out += stride;
} else {
preds -= stride;
}
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);
}
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;
}
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;
if (row == 0) {
out[0] = in[0];
PredictLine(in + 1, preds, out + 1, width - 1, inverse);
row = 1;
preds += stride;
in += stride;
out += stride;
}
while (row < last_row) {
int w;
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
#define SMAX 16
#define SDIFF(a, b) (abs((a) - (b)) >> 4)
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));
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,
HorizontalFilter,
VerticalFilter,
GradientFilter
};
const WebPUnfilterFunc WebPUnfilters[WEBP_FILTER_LAST] = {
NULL,
HorizontalUnfilter,
VerticalUnfilter,
GradientUnfilter
};