vpx/vp9/encoder/vp9_encodemb.c

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2010-05-18 17:58:33 +02:00
/*
* Copyright (c) 2010 The WebM project authors. All Rights Reserved.
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*
* Use of this source code is governed by a BSD-style license
* that can be found in the LICENSE 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.
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*/
#include "./vpx_config.h"
#include "vp9/encoder/vp9_encodemb.h"
#include "vp9/common/vp9_reconinter.h"
#include "vp9/encoder/vp9_quantize.h"
#include "vp9/encoder/vp9_tokenize.h"
#include "vp9/common/vp9_invtrans.h"
#include "vp9/common/vp9_reconintra.h"
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#include "vpx_mem/vpx_mem.h"
#include "vp9/encoder/vp9_rdopt.h"
#include "vp9/common/vp9_systemdependent.h"
#include "vp9_rtcd.h"
2010-05-18 17:58:33 +02:00
void vp9_subtract_b_c(BLOCK *be, BLOCKD *bd, int pitch) {
uint8_t *src_ptr = (*(be->base_src) + be->src);
int16_t *diff_ptr = be->src_diff;
uint8_t *pred_ptr = *(bd->base_dst) + bd->dst;
int src_stride = be->src_stride;
int dst_stride = bd->dst_stride;
int r, c;
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for (r = 0; r < 4; r++) {
for (c = 0; c < 4; c++)
diff_ptr[c] = src_ptr[c] - pred_ptr[c];
diff_ptr += pitch;
pred_ptr += dst_stride;
src_ptr += src_stride;
}
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}
void vp9_subtract_4b_c(BLOCK *be, BLOCKD *bd, int pitch) {
uint8_t *src_ptr = (*(be->base_src) + be->src);
int16_t *diff_ptr = be->src_diff;
uint8_t *pred_ptr = *(bd->base_dst) + bd->dst;
int src_stride = be->src_stride;
int dst_stride = bd->dst_stride;
int r, c;
for (r = 0; r < 8; r++) {
for (c = 0; c < 8; c++)
diff_ptr[c] = src_ptr[c] - pred_ptr[c];
diff_ptr += pitch;
pred_ptr += dst_stride;
src_ptr += src_stride;
}
}
void vp9_subtract_sby_s_c(int16_t *diff, const uint8_t *src, int src_stride,
const uint8_t *pred, int dst_stride,
BLOCK_SIZE_TYPE bsize) {
const int bh = 16 << mb_height_log2(bsize), bw = 16 << mb_width_log2(bsize);
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
int r, c;
for (r = 0; r < bh; r++) {
for (c = 0; c < bw; c++)
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
diff[c] = src[c] - pred[c];
diff += bw;
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
pred += dst_stride;
src += src_stride;
}
}
void vp9_subtract_sbuv_s_c(int16_t *diff, const uint8_t *usrc,
const uint8_t *vsrc, int src_stride,
const uint8_t *upred,
const uint8_t *vpred, int dst_stride,
BLOCK_SIZE_TYPE bsize) {
const int bhl = mb_height_log2(bsize), bwl = mb_width_log2(bsize);
const int uoff = (16 * 16) << (bhl + bwl), voff = (uoff * 5) >> 2;
const int bw = 8 << bwl, bh = 8 << bhl;
int16_t *udiff = diff + uoff;
int16_t *vdiff = diff + voff;
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
int r, c;
for (r = 0; r < bh; r++) {
for (c = 0; c < bw; c++)
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
udiff[c] = usrc[c] - upred[c];
udiff += bw;
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
upred += dst_stride;
usrc += src_stride;
}
for (r = 0; r < bh; r++) {
for (c = 0; c < bw; c++)
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
vdiff[c] = vsrc[c] - vpred[c];
vdiff += bw;
vpred += dst_stride;
vsrc += src_stride;
}
}
static void subtract_mb(MACROBLOCK *x) {
MACROBLOCKD *xd = &x->e_mbd;
vp9_subtract_sby_s_c(x->src_diff, x->src.y_buffer, x->src.y_stride,
xd->dst.y_buffer, xd->dst.y_stride,
BLOCK_SIZE_MB16X16);
vp9_subtract_sbuv_s_c(x->src_diff, x->src.u_buffer, x->src.v_buffer,
x->src.uv_stride,
xd->dst.u_buffer, xd->dst.v_buffer, xd->dst.uv_stride,
BLOCK_SIZE_MB16X16);
2010-05-18 17:58:33 +02:00
}
void vp9_transform_sby_32x32(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) - 1, bw = 1 << bwl;
const int bh = 1 << (mb_height_log2(bsize) - 1);
const int stride = 32 << bwl;
int n;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
vp9_short_fdct32x32(x->src_diff + y_idx * stride * 32 + x_idx * 32,
x->coeff + n * 1024, stride * 2);
}
}
void vp9_transform_sby_16x16(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize), bw = 1 << bwl;
const int bh = 1 << mb_height_log2(bsize);
const int stride = 16 << bwl, bstride = 4 << bwl;
MACROBLOCKD *const xd = &x->e_mbd;
int n;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
const TX_TYPE tx_type = get_tx_type_16x16(xd,
(y_idx * bstride + x_idx) * 4);
if (tx_type != DCT_DCT) {
vp9_short_fht16x16(x->src_diff + y_idx * stride * 16 + x_idx * 16,
x->coeff + n * 256, stride, tx_type);
} else {
x->fwd_txm16x16(x->src_diff + y_idx * stride * 16 + x_idx * 16,
x->coeff + n * 256, stride * 2);
}
}
}
void vp9_transform_sby_8x8(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 1, bw = 1 << bwl;
const int bh = 1 << (mb_height_log2(bsize) + 1);
const int stride = 8 << bwl, bstride = 2 << bwl;
MACROBLOCKD *const xd = &x->e_mbd;
int n;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
const TX_TYPE tx_type = get_tx_type_8x8(xd, (y_idx * bstride + x_idx) * 2);
if (tx_type != DCT_DCT) {
vp9_short_fht8x8(x->src_diff + y_idx * stride * 8 + x_idx * 8,
x->coeff + n * 64, stride, tx_type);
} else {
x->fwd_txm8x8(x->src_diff + y_idx * stride * 8 + x_idx * 8,
x->coeff + n * 64, stride * 2);
}
}
}
void vp9_transform_sby_4x4(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 2, bw = 1 << bwl;
const int bh = 1 << (mb_height_log2(bsize) + 2);
const int stride = 4 << bwl;
MACROBLOCKD *const xd = &x->e_mbd;
int n;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
const TX_TYPE tx_type = get_tx_type_4x4(xd, n);
if (tx_type != DCT_DCT) {
vp9_short_fht4x4(x->src_diff + y_idx * stride * 4 + x_idx * 4,
x->coeff + n * 16, stride, tx_type);
} else {
x->fwd_txm4x4(x->src_diff + y_idx * stride * 4 + x_idx * 4,
x->coeff + n * 16, stride * 2);
}
}
}
void vp9_transform_sbuv_32x32(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
assert(bsize == BLOCK_SIZE_SB64X64);
vp9_clear_system_state();
vp9_short_fdct32x32(x->src_diff + 4096,
x->coeff + 4096, 64);
vp9_short_fdct32x32(x->src_diff + 4096 + 1024,
x->coeff + 4096 + 1024, 64);
}
void vp9_transform_sbuv_16x16(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize), bhl = mb_height_log2(bsize);
const int uoff = (16 * 16) << (bwl + bhl), voff = (uoff * 5) >> 2;
const int bw = 1 << (bwl - 1), bh = 1 << (bhl - 1);
const int stride = 16 << (bwl - 1);
int n;
vp9_clear_system_state();
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
x->fwd_txm16x16(x->src_diff + uoff + y_idx * stride * 16 + x_idx * 16,
x->coeff + uoff + n * 256, stride * 2);
x->fwd_txm16x16(x->src_diff + voff + y_idx * stride * 16 + x_idx * 16,
x->coeff + voff + n * 256, stride * 2);
}
}
void vp9_transform_sbuv_8x8(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 1, bhl = mb_height_log2(bsize) + 1;
const int uoff = (8 * 8) << (bwl + bhl), voff = (uoff * 5) >> 2;
const int bw = 1 << (bwl - 1), bh = 1 << (bhl - 1);
const int stride = 8 << (bwl - 1);
int n;
vp9_clear_system_state();
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
x->fwd_txm8x8(x->src_diff + uoff + y_idx * stride * 8 + x_idx * 8,
x->coeff + uoff + n * 64, stride * 2);
x->fwd_txm8x8(x->src_diff + voff + y_idx * stride * 8 + x_idx * 8,
x->coeff + voff + n * 64, stride * 2);
}
}
void vp9_transform_sbuv_4x4(MACROBLOCK *x, BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 2, bhl = mb_height_log2(bsize) + 2;
const int uoff = (4 * 4) << (bwl + bhl), voff = (uoff * 5) >> 2;
const int bw = 1 << (bwl - 1), bh = 1 << (bhl - 1);
const int stride = 4 << (bwl - 1);
int n;
vp9_clear_system_state();
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
x->fwd_txm4x4(x->src_diff + uoff + y_idx * stride * 4 + x_idx * 4,
x->coeff + uoff + n * 16, stride * 2);
x->fwd_txm4x4(x->src_diff + voff + y_idx * stride * 4 + x_idx * 4,
x->coeff + voff + n * 16, stride * 2);
}
32x32 transform for superblocks. This adds Debargha's DCT/DWT hybrid and a regular 32x32 DCT, and adds code all over the place to wrap that in the bitstream/encoder/decoder/RD. Some implementation notes (these probably need careful review): - token range is extended by 1 bit, since the value range out of this transform is [-16384,16383]. - the coefficients coming out of the FDCT are manually scaled back by 1 bit, or else they won't fit in int16_t (they are 17 bits). Because of this, the RD error scoring does not right-shift the MSE score by two (unlike for 4x4/8x8/16x16). - to compensate for this loss in precision, the quantizer is halved also. This is currently a little hacky. - FDCT and IDCT is double-only right now. Needs a fixed-point impl. - There are no default probabilities for the 32x32 transform yet; I'm simply using the 16x16 luma ones. A future commit will add newly generated probabilities for all transforms. - No ADST version. I don't think we'll add one for this level; if an ADST is desired, transform-size selection can scale back to 16x16 or lower, and use an ADST at that level. Additional notes specific to Debargha's DWT/DCT hybrid: - coefficient scale is different for the top/left 16x16 (DCT-over-DWT) block than for the rest (DWT pixel differences) of the block. Therefore, RD error scoring isn't easily scalable between coefficient and pixel domain. Thus, unfortunately, we need to compute the RD distortion in the pixel domain until we figure out how to scale these appropriately. Change-Id: I00386f20f35d7fabb19aba94c8162f8aee64ef2b
2012-12-07 23:45:05 +01:00
}
#define RDTRUNC(RM,DM,R,D) ( (128+(R)*(RM)) & 0xFF )
#define RDTRUNC_8x8(RM,DM,R,D) ( (128+(R)*(RM)) & 0xFF )
typedef struct vp9_token_state vp9_token_state;
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
struct vp9_token_state {
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
int rate;
int error;
int next;
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
signed char token;
short qc;
};
// TODO: experiments to find optimal multiple numbers
#define Y1_RD_MULT 4
#define UV_RD_MULT 2
static const int plane_rd_mult[4] = {
Y1_RD_MULT,
UV_RD_MULT,
};
#define UPDATE_RD_COST()\
{\
rd_cost0 = RDCOST(rdmult, rddiv, rate0, error0);\
rd_cost1 = RDCOST(rdmult, rddiv, rate1, error1);\
if (rd_cost0 == rd_cost1) {\
rd_cost0 = RDTRUNC(rdmult, rddiv, rate0, error0);\
rd_cost1 = RDTRUNC(rdmult, rddiv, rate1, error1);\
}\
}
// This function is a place holder for now but may ultimately need
// to scan previous tokens to work out the correct context.
static int trellis_get_coeff_context(const int *scan,
const int *nb,
int idx, int token,
uint8_t *token_cache,
int pad, int l) {
int bak = token_cache[idx], pt;
token_cache[idx] = token;
pt = vp9_get_coef_context(scan, nb, pad, token_cache, idx + 1, l);
token_cache[idx] = bak;
return pt;
}
static void optimize_b(VP9_COMMON *const cm,
MACROBLOCK *mb, int ib, PLANE_TYPE type,
const int16_t *dequant_ptr,
ENTROPY_CONTEXT *a, ENTROPY_CONTEXT *l,
int tx_size, int y_blocks) {
const int ref = mb->e_mbd.mode_info_context->mbmi.ref_frame != INTRA_FRAME;
MACROBLOCKD *const xd = &mb->e_mbd;
vp9_token_state tokens[1025][2];
unsigned best_index[1025][2];
const struct plane_block_idx pb_idx = plane_block_idx(y_blocks, ib);
const int16_t *coeff_ptr = mb->coeff + ib * 16;
int16_t *qcoeff_ptr;
int16_t *dqcoeff_ptr;
int eob = xd->plane[pb_idx.plane].eobs[pb_idx.block], final_eob, sz = 0;
const int i0 = 0;
int rc, x, next, i;
int64_t rdmult, rddiv, rd_cost0, rd_cost1;
int rate0, rate1, error0, error1, t0, t1;
int best, band, pt;
int err_mult = plane_rd_mult[type];
int default_eob, pad;
int const *scan, *nb;
const int mul = 1 + (tx_size == TX_32X32);
uint8_t token_cache[1024];
#if CONFIG_CODE_NONZEROCOUNT
// TODO(debargha): the dynamic programming approach used in this function
// is not compatible with the true rate cost when nzcs are used. Note
// the total rate is the sum of the nzc rate and the indicvidual token
// rates. The latter part can be optimized in this function, but because
// the nzc rate is a function of all the other tokens without a Markov
// relationship this rate cannot be considered correctly.
// The current implementation uses a suboptimal approach to account for
// the nzc rates somewhat, but in reality the optimization approach needs
// to change substantially.
const int nzc_used = get_nzc_used(tx_size);
uint16_t nzc = xd->nzcs[ib];
uint16_t nzc0, nzc1;
uint16_t final_nzc = 0, final_nzc_exp;
int nzc_context = vp9_get_nzc_context(cm, xd, ib);
unsigned int *nzc_cost;
nzc0 = nzc1 = nzc;
#endif
assert((!type && !pb_idx.plane) || (type && pb_idx.plane));
dqcoeff_ptr = BLOCK_OFFSET(xd->plane[pb_idx.plane].dqcoeff, pb_idx.block, 16);
qcoeff_ptr = BLOCK_OFFSET(xd->plane[pb_idx.plane].qcoeff, pb_idx.block, 16);
switch (tx_size) {
default:
case TX_4X4: {
const TX_TYPE tx_type = get_tx_type_4x4(xd, ib);
default_eob = 16;
#if CONFIG_CODE_NONZEROCOUNT
nzc_cost = mb->nzc_costs_4x4[nzc_context][ref][type];
#endif
if (tx_type == DCT_ADST) {
scan = vp9_col_scan_4x4;
} else if (tx_type == ADST_DCT) {
scan = vp9_row_scan_4x4;
} else {
scan = vp9_default_zig_zag1d_4x4;
}
break;
}
case TX_8X8: {
const BLOCK_SIZE_TYPE sb_type = xd->mode_info_context->mbmi.sb_type;
const int sz = 3 + mb_width_log2(sb_type);
const int x = ib & ((1 << sz) - 1), y = ib - x;
const TX_TYPE tx_type = get_tx_type_8x8(xd, y + (x >> 1));
if (tx_type == DCT_ADST) {
scan = vp9_col_scan_8x8;
} else if (tx_type == ADST_DCT) {
scan = vp9_row_scan_8x8;
} else {
scan = vp9_default_zig_zag1d_8x8;
}
default_eob = 64;
#if CONFIG_CODE_NONZEROCOUNT
nzc_cost = mb->nzc_costs_8x8[nzc_context][ref][type];
#endif
break;
}
case TX_16X16: {
const BLOCK_SIZE_TYPE sb_type = xd->mode_info_context->mbmi.sb_type;
const int sz = 4 + mb_width_log2(sb_type);
const int x = ib & ((1 << sz) - 1), y = ib - x;
const TX_TYPE tx_type = get_tx_type_16x16(xd, y + (x >> 2));
if (tx_type == DCT_ADST) {
scan = vp9_col_scan_16x16;
} else if (tx_type == ADST_DCT) {
scan = vp9_row_scan_16x16;
} else {
scan = vp9_default_zig_zag1d_16x16;
}
default_eob = 256;
#if CONFIG_CODE_NONZEROCOUNT
nzc_cost = mb->nzc_costs_16x16[nzc_context][ref][type];
#endif
break;
}
case TX_32X32:
scan = vp9_default_zig_zag1d_32x32;
default_eob = 1024;
#if CONFIG_CODE_NONZEROCOUNT
nzc_cost = mb->nzc_costs_32x32[nzc_context][ref][type];
#endif
break;
}
assert(eob <= default_eob);
/* Now set up a Viterbi trellis to evaluate alternative roundings. */
rdmult = mb->rdmult * err_mult;
if (mb->e_mbd.mode_info_context->mbmi.ref_frame == INTRA_FRAME)
rdmult = (rdmult * 9) >> 4;
rddiv = mb->rddiv;
memset(best_index, 0, sizeof(best_index));
/* Initialize the sentinel node of the trellis. */
#if CONFIG_CODE_NONZEROCOUNT
tokens[eob][0].rate = nzc_used ? nzc_cost[nzc] : 0;
#else
tokens[eob][0].rate = 0;
#endif
tokens[eob][0].error = 0;
tokens[eob][0].next = default_eob;
tokens[eob][0].token = DCT_EOB_TOKEN;
tokens[eob][0].qc = 0;
*(tokens[eob] + 1) = *(tokens[eob] + 0);
next = eob;
for (i = 0; i < eob; i++)
token_cache[i] = vp9_dct_value_tokens_ptr[qcoeff_ptr[scan[i]]].Token;
nb = vp9_get_coef_neighbors_handle(scan, &pad);
for (i = eob; i-- > i0;) {
int base_bits, d2, dx;
#if CONFIG_CODE_NONZEROCOUNT
int new_nzc0, new_nzc1;
#endif
rc = scan[i];
x = qcoeff_ptr[rc];
/* Only add a trellis state for non-zero coefficients. */
if (x) {
int shortcut = 0;
error0 = tokens[next][0].error;
error1 = tokens[next][1].error;
/* Evaluate the first possibility for this state. */
rate0 = tokens[next][0].rate;
rate1 = tokens[next][1].rate;
t0 = (vp9_dct_value_tokens_ptr + x)->Token;
/* Consider both possible successor states. */
if (next < default_eob) {
band = get_coef_band(scan, tx_size, i + 1);
pt = trellis_get_coeff_context(scan, nb, i, t0, token_cache,
pad, default_eob);
rate0 +=
mb->token_costs[tx_size][type][ref][band][pt][tokens[next][0].token];
rate1 +=
mb->token_costs[tx_size][type][ref][band][pt][tokens[next][1].token];
}
UPDATE_RD_COST();
/* And pick the best. */
best = rd_cost1 < rd_cost0;
base_bits = *(vp9_dct_value_cost_ptr + x);
dx = mul * (dqcoeff_ptr[rc] - coeff_ptr[rc]);
d2 = dx * dx;
tokens[i][0].rate = base_bits + (best ? rate1 : rate0);
tokens[i][0].error = d2 + (best ? error1 : error0);
tokens[i][0].next = next;
tokens[i][0].token = t0;
tokens[i][0].qc = x;
best_index[i][0] = best;
#if CONFIG_CODE_NONZEROCOUNT
new_nzc0 = (best ? nzc1 : nzc0);
#endif
/* Evaluate the second possibility for this state. */
rate0 = tokens[next][0].rate;
rate1 = tokens[next][1].rate;
if ((abs(x)*dequant_ptr[rc != 0] > abs(coeff_ptr[rc]) * mul) &&
(abs(x)*dequant_ptr[rc != 0] < abs(coeff_ptr[rc]) * mul +
dequant_ptr[rc != 0]))
shortcut = 1;
else
shortcut = 0;
if (shortcut) {
sz = -(x < 0);
x -= 2 * sz + 1;
}
/* Consider both possible successor states. */
if (!x) {
/* If we reduced this coefficient to zero, check to see if
* we need to move the EOB back here.
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
*/
t0 = tokens[next][0].token == DCT_EOB_TOKEN ?
DCT_EOB_TOKEN : ZERO_TOKEN;
t1 = tokens[next][1].token == DCT_EOB_TOKEN ?
DCT_EOB_TOKEN : ZERO_TOKEN;
#if CONFIG_CODE_NONZEROCOUNT
// Account for rate drop because of the nzc change.
// TODO(debargha): Find a better solution
if (nzc_used) {
rate0 -= nzc_cost[nzc0] - nzc_cost[nzc0 - 1];
rate1 -= nzc_cost[nzc1] - nzc_cost[nzc1 - 1];
}
#endif
} else {
t0 = t1 = (vp9_dct_value_tokens_ptr + x)->Token;
}
if (next < default_eob) {
band = get_coef_band(scan, tx_size, i + 1);
if (t0 != DCT_EOB_TOKEN) {
pt = trellis_get_coeff_context(scan, nb, i, t0, token_cache,
pad, default_eob);
rate0 += mb->token_costs[tx_size][type][ref][band][pt][
tokens[next][0].token];
2010-05-18 17:58:33 +02:00
}
if (t1 != DCT_EOB_TOKEN) {
pt = trellis_get_coeff_context(scan, nb, i, t1, token_cache,
pad, default_eob);
rate1 += mb->token_costs[tx_size][type][ref][band][pt][
tokens[next][1].token];
}
}
2010-05-18 17:58:33 +02:00
UPDATE_RD_COST();
/* And pick the best. */
best = rd_cost1 < rd_cost0;
base_bits = *(vp9_dct_value_cost_ptr + x);
if (shortcut) {
dx -= (dequant_ptr[rc != 0] + sz) ^ sz;
d2 = dx * dx;
}
tokens[i][1].rate = base_bits + (best ? rate1 : rate0);
tokens[i][1].error = d2 + (best ? error1 : error0);
tokens[i][1].next = next;
tokens[i][1].token = best ? t1 : t0;
tokens[i][1].qc = x;
best_index[i][1] = best;
#if CONFIG_CODE_NONZEROCOUNT
new_nzc1 = (best ? nzc1 : nzc0) - (!x);
nzc0 = new_nzc0;
nzc1 = new_nzc1;
#endif
/* Finally, make this the new head of the trellis. */
next = i;
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
}
/* There's no choice to make for a zero coefficient, so we don't
* add a new trellis node, but we do need to update the costs.
*/
else {
band = get_coef_band(scan, tx_size, i + 1);
t0 = tokens[next][0].token;
t1 = tokens[next][1].token;
/* Update the cost of each path if we're past the EOB token. */
if (t0 != DCT_EOB_TOKEN) {
tokens[next][0].rate +=
mb->token_costs[tx_size][type][ref][band][0][t0];
tokens[next][0].token = ZERO_TOKEN;
}
if (t1 != DCT_EOB_TOKEN) {
tokens[next][1].rate +=
mb->token_costs[tx_size][type][ref][band][0][t1];
tokens[next][1].token = ZERO_TOKEN;
}
/* Don't update next, because we didn't add a new node. */
Add trellis quantization. Replace the exponential search for optimal rounding during quantization with a linear Viterbi trellis and enable it by default when using --best. Right now this operates on top of the output of the adaptive zero-bin quantizer in vp8_regular_quantize_b() and gives a small gain. It can be tested as a replacement for that quantizer by enabling the call to vp8_strict_quantize_b(), which uses normal rounding and no zero bin offset. Ultimately, the quantizer will have to become a function of lambda in order to take advantage of activity masking, since there is limited ability to change the quantization factor itself. However, currently vp8_strict_quantize_b() plus the trellis quantizer (which is lambda-dependent) loses to vp8_regular_quantize_b() alone (which is not) on my test clip. Patch Set 3: Fix an issue related to the cost evaluation of successor states when a coefficient is reduced to zero. With this issue fixed, now the trellis search almost exactly matches the exponential search. Patch Set 2: Overall, the goal of this patch set is to make "trellis" search to produce encodings that match the exponential search version. There are three main differences between Patch Set 2 and 1: a. Patch set 1 did not properly account for the scale of 2nd order error, so patch set 2 disable it all together for 2nd blocks. b. Patch set 1 was not consistent on when to enable the the quantization optimization. Patch set 2 restore the condition to be consistent. c. Patch set 1 checks quantized level L-1, and L for any input coefficient was quantized to L. Patch set 2 limits the candidate coefficient to those that were rounded up to L. It is worth noting here that a strategy to check L and L+1 for coefficients that were truncated down to L might work. (a and b get trellis quant to basically match the exponential search on all mid/low rate encodings on cif set, without a, b, trellis quant can hurt the psnr by 0.2 to .3db at 200kbps for some cif clips) (c gets trellis quant to match the exponential search to match at Q0 encoding, without c, trellis quant can be 1.5 to 2db lower for encodings with fixed Q at 0 on most derf cif clips) Change-Id: Ib1a043b665d75fbf00cb0257b7c18e90eebab95e
2010-07-02 23:35:53 +02:00
}
}
/* Now pick the best path through the whole trellis. */
band = get_coef_band(scan, tx_size, i + 1);
pt = combine_entropy_contexts(*a, *l);
rate0 = tokens[next][0].rate;
rate1 = tokens[next][1].rate;
error0 = tokens[next][0].error;
error1 = tokens[next][1].error;
t0 = tokens[next][0].token;
t1 = tokens[next][1].token;
rate0 += mb->token_costs[tx_size][type][ref][band][pt][t0];
rate1 += mb->token_costs[tx_size][type][ref][band][pt][t1];
UPDATE_RD_COST();
best = rd_cost1 < rd_cost0;
#if CONFIG_CODE_NONZEROCOUNT
final_nzc_exp = (best ? nzc1 : nzc0);
#endif
final_eob = i0 - 1;
for (i = next; i < eob; i = next) {
x = tokens[i][best].qc;
if (x) {
final_eob = i;
#if CONFIG_CODE_NONZEROCOUNT
++final_nzc;
#endif
}
rc = scan[i];
qcoeff_ptr[rc] = x;
dqcoeff_ptr[rc] = (x * dequant_ptr[rc != 0]) / mul;
next = tokens[i][best].next;
best = best_index[i][best];
}
final_eob++;
xd->plane[pb_idx.plane].eobs[pb_idx.block] = final_eob;
*a = *l = (final_eob > 0);
#if CONFIG_CODE_NONZEROCOUNT
assert(final_nzc == final_nzc_exp);
xd->nzcs[ib] = final_nzc;
#endif
}
void vp9_optimize_sby_32x32(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) - 1, bw = 1 << bwl;
const int bh = 1 << (mb_height_log2(bsize) - 1);
ENTROPY_CONTEXT ta[2], tl[2];
int n;
for (n = 0; n < bw; n++) {
ENTROPY_CONTEXT *a =
(ENTROPY_CONTEXT *) (x->e_mbd.above_context + n * 2 + 0);
ENTROPY_CONTEXT *a1 =
(ENTROPY_CONTEXT *) (x->e_mbd.above_context + n * 2 + 1);
ta[n] = (a[0] + a[1] + a[2] + a[3] + a1[0] + a1[1] + a1[2] + a1[3]) != 0;
}
for (n = 0; n < bh; n++) {
ENTROPY_CONTEXT *l =
(ENTROPY_CONTEXT *) (x->e_mbd.left_context + n * 2);
ENTROPY_CONTEXT *l1 =
(ENTROPY_CONTEXT *) (x->e_mbd.left_context + n * 2 + 1);
tl[n] = (l[0] + l[1] + l[2] + l[3] + l1[0] + l1[1] + l1[2] + l1[3]) != 0;
}
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
optimize_b(cm, x, n * 64, PLANE_TYPE_Y_WITH_DC, x->e_mbd.block[0].dequant,
ta + x_idx, tl + y_idx, TX_32X32, 64 * bw * bh);
}
}
void vp9_optimize_sby_16x16(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize), bw = 1 << bwl;
const int bh = 1 << mb_height_log2(bsize);
ENTROPY_CONTEXT ta[4], tl[4];
int n;
for (n = 0; n < bw; n++) {
ENTROPY_CONTEXT *a = (ENTROPY_CONTEXT *) (x->e_mbd.above_context + n);
ta[n] = (a[0] + a[1] + a[2] + a[3]) != 0;
}
for (n = 0; n < bh; n++) {
ENTROPY_CONTEXT *l = (ENTROPY_CONTEXT *) (x->e_mbd.left_context + n);
tl[n] = (l[0] + l[1] + l[2] + l[3]) != 0;
}
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
optimize_b(cm, x, n * 16, PLANE_TYPE_Y_WITH_DC, x->e_mbd.block[0].dequant,
ta + x_idx, tl + y_idx, TX_16X16, 16 * bw * bh);
}
}
void vp9_optimize_sby_8x8(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 1, bw = 1 << bwl;
const int bh = 2 << mb_height_log2(bsize);
ENTROPY_CONTEXT ta[8], tl[8];
int n;
for (n = 0; n < bw; n += 2) {
ENTROPY_CONTEXT *a =
(ENTROPY_CONTEXT *) (x->e_mbd.above_context + (n >> 1));
ta[n + 0] = (a[0] + a[1]) != 0;
ta[n + 1] = (a[2] + a[3]) != 0;
}
for (n = 0; n < bh; n += 2) {
ENTROPY_CONTEXT *l =
(ENTROPY_CONTEXT *) (x->e_mbd.left_context + (n >> 1));
tl[n + 0] = (l[0] + l[1]) != 0;
tl[n + 1] = (l[2] + l[3]) != 0;
}
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
optimize_b(cm, x, n * 4, PLANE_TYPE_Y_WITH_DC, x->e_mbd.block[0].dequant,
ta + x_idx, tl + y_idx, TX_8X8, 4 * bw * bh);
}
}
void vp9_optimize_sby_4x4(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
int bwl = mb_width_log2(bsize), bw = 1 << bwl;
int bh = 1 << mb_height_log2(bsize);
ENTROPY_CONTEXT ta[16], tl[16];
int n;
for (n = 0; n < bw; n++)
vpx_memcpy(&ta[n * 4], x->e_mbd.above_context + n,
sizeof(ENTROPY_CONTEXT) * 4);
for (n = 0; n < bh; n++)
vpx_memcpy(&tl[n * 4], x->e_mbd.left_context + n,
sizeof(ENTROPY_CONTEXT) * 4);
bw *= 4;
bh *= 4;
bwl += 2;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> bwl;
optimize_b(cm, x, n, PLANE_TYPE_Y_WITH_DC, x->e_mbd.block[0].dequant,
ta + x_idx, tl + y_idx, TX_4X4, bh * bw);
}
}
void vp9_optimize_sbuv_32x32(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
ENTROPY_CONTEXT *ta = (ENTROPY_CONTEXT *) x->e_mbd.above_context;
ENTROPY_CONTEXT *tl = (ENTROPY_CONTEXT *) x->e_mbd.left_context;
ENTROPY_CONTEXT *a, *l, *a1, *l1, *a2, *l2, *a3, *l3, a_ec, l_ec;
int b;
assert(bsize == BLOCK_SIZE_SB64X64);
for (b = 256; b < 384; b += 64) {
const int cidx = b >= 320 ? 20 : 16;
a = ta + vp9_block2above_sb64[TX_32X32][b];
l = tl + vp9_block2left_sb64[TX_32X32][b];
a1 = a + sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
l1 = l + sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
a2 = a + 2 * sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
l2 = l + 2 * sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
a3 = a + 3 * sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
l3 = l + 3 * sizeof(ENTROPY_CONTEXT_PLANES) / sizeof(ENTROPY_CONTEXT);
a_ec = (a[0] + a[1] + a1[0] + a1[1] + a2[0] + a2[1] + a3[0] + a3[1]) != 0;
l_ec = (l[0] + l[1] + l1[0] + l1[1] + l2[0] + l2[1] + l3[0] + l3[1]) != 0;
optimize_b(cm, x, b, PLANE_TYPE_UV, x->e_mbd.block[cidx].dequant,
&a_ec, &l_ec, TX_32X32, 256);
}
}
void vp9_optimize_sbuv_16x16(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize), bhl = mb_height_log2(bsize);
const int bw = 1 << (bwl - 1);
const int bh = 1 << (bhl - 1);
int uvoff = 16 << (bwl + bhl);
ENTROPY_CONTEXT ta[2][2], tl[2][2];
int plane, n;
for (n = 0; n < bw; n++) {
ENTROPY_CONTEXT_PLANES *a = x->e_mbd.above_context + n * 2;
ENTROPY_CONTEXT_PLANES *a1 = x->e_mbd.above_context + n * 2 + 1;
ta[0][n] = (a->u[0] + a->u[1] + a1->u[0] + a1->u[1]) != 0;
ta[1][n] = (a->v[0] + a->v[1] + a1->v[0] + a1->v[1]) != 0;
}
for (n = 0; n < bh; n++) {
ENTROPY_CONTEXT_PLANES *l = (x->e_mbd.left_context + n * 2);
ENTROPY_CONTEXT_PLANES *l1 = (x->e_mbd.left_context + n * 2 + 1);
tl[0][n] = (l->u[0] + l->u[1] + l1->u[0] + l1->u[1]) != 0;
tl[1][n] = (l->v[0] + l->v[1] + l1->v[0] + l1->v[1]) != 0;
}
for (plane = 0; plane < 2; plane++) {
const int cidx = 16 + plane * 4;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
optimize_b(cm, x, uvoff + n * 16, PLANE_TYPE_UV,
x->e_mbd.block[cidx].dequant,
&ta[plane][x_idx], &tl[plane][y_idx],
TX_16X16, bh * bw * 64);
}
uvoff = (uvoff * 5) >> 2; // switch u -> v
}
}
void vp9_optimize_sbuv_8x8(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 1, bhl = mb_height_log2(bsize) + 1;
const int bw = 1 << (bwl - 1);
const int bh = 1 << (bhl - 1);
int uvoff = 4 << (bwl + bhl);
ENTROPY_CONTEXT ta[2][4], tl[2][4];
int plane, n;
for (n = 0; n < bw; n++) {
ENTROPY_CONTEXT_PLANES *a = x->e_mbd.above_context + n;
ta[0][n] = (a->u[0] + a->u[1]) != 0;
ta[1][n] = (a->v[0] + a->v[1]) != 0;
}
for (n = 0; n < bh; n++) {
ENTROPY_CONTEXT_PLANES *l = x->e_mbd.left_context + n;
tl[0][n] = (l->u[0] + l->u[1]) != 0;
tl[1][n] = (l->v[0] + l->v[1]) != 0;
}
for (plane = 0; plane < 2; plane++) {
const int cidx = 16 + plane * 4;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
optimize_b(cm, x, uvoff + n * 4, PLANE_TYPE_UV,
x->e_mbd.block[cidx].dequant,
&ta[plane][x_idx], &tl[plane][y_idx],
TX_8X8, bh * bw * 16);
}
uvoff = (uvoff * 5) >> 2; // switch u -> v
}
}
void vp9_optimize_sbuv_4x4(VP9_COMMON *const cm, MACROBLOCK *x,
BLOCK_SIZE_TYPE bsize) {
const int bwl = mb_width_log2(bsize) + 2, bhl = mb_height_log2(bsize) + 2;
const int bw = 1 << (bwl - 1);
const int bh = 1 << (bhl - 1);
int uvoff = 1 << (bwl + bhl);
ENTROPY_CONTEXT ta[2][8], tl[2][8];
int plane, n;
for (n = 0; n < bw; n += 2) {
ENTROPY_CONTEXT_PLANES *a = x->e_mbd.above_context + (n >> 1);
ta[0][n + 0] = (a->u[0]) != 0;
ta[0][n + 1] = (a->u[1]) != 0;
ta[1][n + 0] = (a->v[0]) != 0;
ta[1][n + 1] = (a->v[1]) != 0;
}
for (n = 0; n < bh; n += 2) {
ENTROPY_CONTEXT_PLANES *l = x->e_mbd.left_context + (n >> 1);
tl[0][n + 0] = (l->u[0]) != 0;
tl[0][n + 1] = (l->u[1]) != 0;
tl[1][n + 0] = (l->v[0]) != 0;
tl[1][n + 1] = (l->v[1]) != 0;
}
for (plane = 0; plane < 2; plane++) {
const int cidx = 16 + plane * 4;
for (n = 0; n < bw * bh; n++) {
const int x_idx = n & (bw - 1), y_idx = n >> (bwl - 1);
optimize_b(cm, x, uvoff + n, PLANE_TYPE_UV,
x->e_mbd.block[cidx].dequant,
&ta[plane][x_idx], &tl[plane][y_idx],
TX_4X4, bh * bw * 4);
}
uvoff = (uvoff * 5) >> 2; // switch u -> v
}
}
void vp9_fidct_mb(VP9_COMMON *const cm, MACROBLOCK *x) {
MACROBLOCKD *const xd = &x->e_mbd;
const TX_SIZE tx_size = xd->mode_info_context->mbmi.txfm_size;
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if (tx_size == TX_16X16) {
vp9_transform_sby_16x16(x, BLOCK_SIZE_MB16X16);
vp9_transform_sbuv_8x8(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sby_16x16(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sbuv_8x8(x, BLOCK_SIZE_MB16X16);
if (x->optimize) {
vp9_optimize_sby_16x16(cm, x, BLOCK_SIZE_MB16X16);
vp9_optimize_sbuv_8x8(cm, x, BLOCK_SIZE_MB16X16);
}
vp9_inverse_transform_sby_16x16(xd, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sbuv_8x8(xd, BLOCK_SIZE_MB16X16);
} else if (tx_size == TX_8X8) {
vp9_transform_sby_8x8(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sby_8x8(x, BLOCK_SIZE_MB16X16);
if (x->optimize)
vp9_optimize_sby_8x8(cm, x, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sby_8x8(xd, BLOCK_SIZE_MB16X16);
if (xd->mode_info_context->mbmi.mode == SPLITMV) {
assert(xd->mode_info_context->mbmi.partitioning != PARTITIONING_4X4);
vp9_transform_sbuv_4x4(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sbuv_4x4(x, BLOCK_SIZE_MB16X16);
if (x->optimize)
vp9_optimize_sbuv_4x4(cm, x, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sbuv_4x4(xd, BLOCK_SIZE_MB16X16);
} else {
vp9_transform_sbuv_8x8(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sbuv_8x8(x, BLOCK_SIZE_MB16X16);
if (x->optimize)
vp9_optimize_sbuv_8x8(cm, x, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sbuv_8x8(xd, BLOCK_SIZE_MB16X16);
}
} else {
vp9_transform_sby_4x4(x, BLOCK_SIZE_MB16X16);
vp9_transform_sbuv_4x4(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sby_4x4(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sbuv_4x4(x, BLOCK_SIZE_MB16X16);
if (x->optimize) {
vp9_optimize_sby_4x4(cm, x, BLOCK_SIZE_MB16X16);
vp9_optimize_sbuv_4x4(cm, x, BLOCK_SIZE_MB16X16);
}
vp9_inverse_transform_sby_4x4(xd, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sbuv_4x4(xd, BLOCK_SIZE_MB16X16);
}
}
void vp9_encode_inter16x16(VP9_COMMON *const cm, MACROBLOCK *x,
int mb_row, int mb_col) {
MACROBLOCKD *const xd = &x->e_mbd;
vp9_build_inter_predictors_mb(xd, mb_row, mb_col);
subtract_mb(x);
vp9_fidct_mb(cm, x);
vp9_recon_mb(xd);
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}
/* this function is used by first pass only */
void vp9_encode_inter16x16y(MACROBLOCK *x, int mb_row, int mb_col) {
MACROBLOCKD *xd = &x->e_mbd;
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vp9_build_inter_predictors_sby(xd, xd->dst.y_buffer, xd->dst.y_stride,
mb_row, mb_col, BLOCK_SIZE_MB16X16);
vp9_subtract_sby_s_c(x->src_diff, x->src.y_buffer, x->src.y_stride,
xd->dst.y_buffer, xd->dst.y_stride,
BLOCK_SIZE_MB16X16);
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vp9_transform_sby_4x4(x, BLOCK_SIZE_MB16X16);
vp9_quantize_sby_4x4(x, BLOCK_SIZE_MB16X16);
vp9_inverse_transform_sby_4x4(xd, BLOCK_SIZE_MB16X16);
vp9_recon_mby(xd);
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}