av1/common/cdef_block.c - aom - Git at Google

 /*
  * 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);
   }
 }
	/*
	* 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 357*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);
	}
	}