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