зеркало из https://github.com/mozilla/gecko-dev.git
258 строки
10 KiB
C
258 строки
10 KiB
C
/*
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* Copyright (c) 2016, Alliance for Open Media. All rights reserved
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*
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* This source code is subject to the terms of the BSD 2 Clause License and
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* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
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* was not distributed with this source code in the LICENSE file, you can
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* obtain it at www.aomedia.org/license/software. If the Alliance for Open
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* Media Patent License 1.0 was not distributed with this source code in the
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* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
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*/
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#include <math.h>
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#include <stdlib.h>
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#include "config/aom_dsp_rtcd.h"
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#include "config/av1_rtcd.h"
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#include "av1/common/cdef.h"
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/* Generated from gen_filter_tables.c. */
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DECLARE_ALIGNED(16, const int, cdef_directions[8][2]) = {
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{ -1 * CDEF_BSTRIDE + 1, -2 * CDEF_BSTRIDE + 2 },
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{ 0 * CDEF_BSTRIDE + 1, -1 * CDEF_BSTRIDE + 2 },
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{ 0 * CDEF_BSTRIDE + 1, 0 * CDEF_BSTRIDE + 2 },
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{ 0 * CDEF_BSTRIDE + 1, 1 * CDEF_BSTRIDE + 2 },
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{ 1 * CDEF_BSTRIDE + 1, 2 * CDEF_BSTRIDE + 2 },
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{ 1 * CDEF_BSTRIDE + 0, 2 * CDEF_BSTRIDE + 1 },
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{ 1 * CDEF_BSTRIDE + 0, 2 * CDEF_BSTRIDE + 0 },
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{ 1 * CDEF_BSTRIDE + 0, 2 * CDEF_BSTRIDE - 1 }
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};
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/* Detect direction. 0 means 45-degree up-right, 2 is horizontal, and so on.
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The search minimizes the weighted variance along all the lines in a
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particular direction, i.e. the squared error between the input and a
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"predicted" block where each pixel is replaced by the average along a line
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in a particular direction. Since each direction have the same sum(x^2) term,
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that term is never computed. See Section 2, step 2, of:
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http://jmvalin.ca/notes/intra_paint.pdf */
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int cdef_find_dir_c(const uint16_t *img, int stride, int32_t *var,
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int coeff_shift) {
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int i;
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int32_t cost[8] = { 0 };
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int partial[8][15] = { { 0 } };
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int32_t best_cost = 0;
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int best_dir = 0;
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/* Instead of dividing by n between 2 and 8, we multiply by 3*5*7*8/n.
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The output is then 840 times larger, but we don't care for finding
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the max. */
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static const int div_table[] = { 0, 840, 420, 280, 210, 168, 140, 120, 105 };
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for (i = 0; i < 8; i++) {
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int j;
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for (j = 0; j < 8; j++) {
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int x;
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/* We subtract 128 here to reduce the maximum range of the squared
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partial sums. */
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x = (img[i * stride + j] >> coeff_shift) - 128;
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partial[0][i + j] += x;
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partial[1][i + j / 2] += x;
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partial[2][i] += x;
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partial[3][3 + i - j / 2] += x;
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partial[4][7 + i - j] += x;
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partial[5][3 - i / 2 + j] += x;
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partial[6][j] += x;
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partial[7][i / 2 + j] += x;
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}
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}
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for (i = 0; i < 8; i++) {
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cost[2] += partial[2][i] * partial[2][i];
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cost[6] += partial[6][i] * partial[6][i];
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}
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cost[2] *= div_table[8];
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cost[6] *= div_table[8];
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for (i = 0; i < 7; i++) {
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cost[0] += (partial[0][i] * partial[0][i] +
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partial[0][14 - i] * partial[0][14 - i]) *
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div_table[i + 1];
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cost[4] += (partial[4][i] * partial[4][i] +
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partial[4][14 - i] * partial[4][14 - i]) *
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div_table[i + 1];
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}
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cost[0] += partial[0][7] * partial[0][7] * div_table[8];
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cost[4] += partial[4][7] * partial[4][7] * div_table[8];
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for (i = 1; i < 8; i += 2) {
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int j;
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for (j = 0; j < 4 + 1; j++) {
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cost[i] += partial[i][3 + j] * partial[i][3 + j];
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}
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cost[i] *= div_table[8];
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for (j = 0; j < 4 - 1; j++) {
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cost[i] += (partial[i][j] * partial[i][j] +
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partial[i][10 - j] * partial[i][10 - j]) *
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div_table[2 * j + 2];
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}
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}
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for (i = 0; i < 8; i++) {
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if (cost[i] > best_cost) {
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best_cost = cost[i];
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best_dir = i;
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}
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}
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/* Difference between the optimal variance and the variance along the
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orthogonal direction. Again, the sum(x^2) terms cancel out. */
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*var = best_cost - cost[(best_dir + 4) & 7];
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/* We'd normally divide by 840, but dividing by 1024 is close enough
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for what we're going to do with this. */
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*var >>= 10;
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return best_dir;
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}
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const int cdef_pri_taps[2][2] = { { 4, 2 }, { 3, 3 } };
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const int cdef_sec_taps[2][2] = { { 2, 1 }, { 2, 1 } };
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/* Smooth in the direction detected. */
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void cdef_filter_block_c(uint8_t *dst8, uint16_t *dst16, int dstride,
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const uint16_t *in, int pri_strength, int sec_strength,
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int dir, int pri_damping, int sec_damping, int bsize,
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AOM_UNUSED int max_unused, int coeff_shift) {
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int i, j, k;
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const int s = CDEF_BSTRIDE;
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const int *pri_taps = cdef_pri_taps[(pri_strength >> coeff_shift) & 1];
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const int *sec_taps = cdef_sec_taps[(pri_strength >> coeff_shift) & 1];
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for (i = 0; i < 4 << (bsize == BLOCK_8X8 || bsize == BLOCK_4X8); i++) {
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for (j = 0; j < 4 << (bsize == BLOCK_8X8 || bsize == BLOCK_8X4); j++) {
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int16_t sum = 0;
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int16_t y;
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int16_t x = in[i * s + j];
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int max = x;
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int min = x;
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for (k = 0; k < 2; k++) {
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int16_t p0 = in[i * s + j + cdef_directions[dir][k]];
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int16_t p1 = in[i * s + j - cdef_directions[dir][k]];
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sum += pri_taps[k] * constrain(p0 - x, pri_strength, pri_damping);
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sum += pri_taps[k] * constrain(p1 - x, pri_strength, pri_damping);
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if (p0 != CDEF_VERY_LARGE) max = AOMMAX(p0, max);
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if (p1 != CDEF_VERY_LARGE) max = AOMMAX(p1, max);
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min = AOMMIN(p0, min);
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min = AOMMIN(p1, min);
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int16_t s0 = in[i * s + j + cdef_directions[(dir + 2) & 7][k]];
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int16_t s1 = in[i * s + j - cdef_directions[(dir + 2) & 7][k]];
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int16_t s2 = in[i * s + j + cdef_directions[(dir + 6) & 7][k]];
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int16_t s3 = in[i * s + j - cdef_directions[(dir + 6) & 7][k]];
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if (s0 != CDEF_VERY_LARGE) max = AOMMAX(s0, max);
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if (s1 != CDEF_VERY_LARGE) max = AOMMAX(s1, max);
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if (s2 != CDEF_VERY_LARGE) max = AOMMAX(s2, max);
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if (s3 != CDEF_VERY_LARGE) max = AOMMAX(s3, max);
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min = AOMMIN(s0, min);
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min = AOMMIN(s1, min);
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min = AOMMIN(s2, min);
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min = AOMMIN(s3, min);
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sum += sec_taps[k] * constrain(s0 - x, sec_strength, sec_damping);
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sum += sec_taps[k] * constrain(s1 - x, sec_strength, sec_damping);
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sum += sec_taps[k] * constrain(s2 - x, sec_strength, sec_damping);
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sum += sec_taps[k] * constrain(s3 - x, sec_strength, sec_damping);
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}
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y = clamp((int16_t)x + ((8 + sum - (sum < 0)) >> 4), min, max);
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if (dst8)
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dst8[i * dstride + j] = (uint8_t)y;
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else
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dst16[i * dstride + j] = (uint16_t)y;
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}
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}
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}
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/* Compute the primary filter strength for an 8x8 block based on the
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directional variance difference. A high variance difference means
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that we have a highly directional pattern (e.g. a high contrast
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edge), so we can apply more deringing. A low variance means that we
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either have a low contrast edge, or a non-directional texture, so
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we want to be careful not to blur. */
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static INLINE int adjust_strength(int strength, int32_t var) {
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const int i = var >> 6 ? AOMMIN(get_msb(var >> 6), 12) : 0;
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/* We use the variance of 8x8 blocks to adjust the strength. */
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return var ? (strength * (4 + i) + 8) >> 4 : 0;
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}
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void cdef_filter_fb(uint8_t *dst8, uint16_t *dst16, int dstride, uint16_t *in,
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int xdec, int ydec, int dir[CDEF_NBLOCKS][CDEF_NBLOCKS],
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int *dirinit, int var[CDEF_NBLOCKS][CDEF_NBLOCKS], int pli,
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cdef_list *dlist, int cdef_count, int level,
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int sec_strength, int pri_damping, int sec_damping,
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int coeff_shift) {
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int bi;
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int bx;
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int by;
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int bsize, bsizex, bsizey;
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int pri_strength = level << coeff_shift;
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sec_strength <<= coeff_shift;
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sec_damping += coeff_shift - (pli != AOM_PLANE_Y);
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pri_damping += coeff_shift - (pli != AOM_PLANE_Y);
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bsize =
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ydec ? (xdec ? BLOCK_4X4 : BLOCK_8X4) : (xdec ? BLOCK_4X8 : BLOCK_8X8);
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bsizex = 3 - xdec;
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bsizey = 3 - ydec;
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if (dirinit && pri_strength == 0 && sec_strength == 0) {
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// If we're here, both primary and secondary strengths are 0, and
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// we still haven't written anything to y[] yet, so we just copy
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// the input to y[]. This is necessary only for av1_cdef_search()
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// and only av1_cdef_search() sets dirinit.
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for (bi = 0; bi < cdef_count; bi++) {
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by = dlist[bi].by;
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bx = dlist[bi].bx;
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int iy, ix;
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// TODO(stemidts/jmvalin): SIMD optimisations
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for (iy = 0; iy < 1 << bsizey; iy++)
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for (ix = 0; ix < 1 << bsizex; ix++)
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dst16[(bi << (bsizex + bsizey)) + (iy << bsizex) + ix] =
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in[((by << bsizey) + iy) * CDEF_BSTRIDE + (bx << bsizex) + ix];
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}
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return;
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}
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if (pli == 0) {
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if (!dirinit || !*dirinit) {
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for (bi = 0; bi < cdef_count; bi++) {
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by = dlist[bi].by;
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bx = dlist[bi].bx;
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dir[by][bx] = cdef_find_dir(&in[8 * by * CDEF_BSTRIDE + 8 * bx],
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CDEF_BSTRIDE, &var[by][bx], coeff_shift);
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}
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if (dirinit) *dirinit = 1;
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}
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}
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if (pli == 1 && xdec != ydec) {
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for (bi = 0; bi < cdef_count; bi++) {
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static const int conv422[8] = { 7, 0, 2, 4, 5, 6, 6, 6 };
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static const int conv440[8] = { 1, 2, 2, 2, 3, 4, 6, 0 };
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by = dlist[bi].by;
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bx = dlist[bi].bx;
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dir[by][bx] = (xdec ? conv422 : conv440)[dir[by][bx]];
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}
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}
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for (bi = 0; bi < cdef_count; bi++) {
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int t = dlist[bi].skip ? 0 : pri_strength;
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int s = dlist[bi].skip ? 0 : sec_strength;
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by = dlist[bi].by;
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bx = dlist[bi].bx;
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if (dst8)
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cdef_filter_block(&dst8[(by << bsizey) * dstride + (bx << bsizex)], NULL,
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dstride,
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&in[(by * CDEF_BSTRIDE << bsizey) + (bx << bsizex)],
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(pli ? t : adjust_strength(t, var[by][bx])), s,
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t ? dir[by][bx] : 0, pri_damping, sec_damping, bsize,
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(256 << coeff_shift) - 1, coeff_shift);
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else
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cdef_filter_block(
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NULL,
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&dst16[dirinit ? bi << (bsizex + bsizey)
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: (by << bsizey) * dstride + (bx << bsizex)],
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dirinit ? 1 << bsizex : dstride,
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&in[(by * CDEF_BSTRIDE << bsizey) + (bx << bsizex)],
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(pli ? t : adjust_strength(t, var[by][bx])), s, t ? dir[by][bx] : 0,
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pri_damping, sec_damping, bsize, (256 << coeff_shift) - 1,
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coeff_shift);
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}
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}
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