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be07485e9a
vpx/vp9/encoder/vp9_segmentation.c
Dmitry Kovalev be07485e9a General cleanup in segmentation-related code. 
Using consistent function and variable names.

Change-Id: I2deb3fded8797453a2081836c9ce2e79ade06eb7
2013-06-26 10:27:28 -07:00

294 lines
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C
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/*
* Copyright (c) 2012 The WebM project authors. All Rights Reserved.
*
* 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.
*/
#include <limits.h>
#include "vpx_mem/vpx_mem.h"
#include "vp9/encoder/vp9_segmentation.h"
#include "vp9/common/vp9_pred_common.h"
#include "vp9/common/vp9_tile_common.h"
void vp9_enable_segmentation(VP9_PTR ptr) {
VP9_COMP *cpi = (VP9_COMP *)ptr;
cpi->mb.e_mbd.segmentation_enabled = 1;
cpi->mb.e_mbd.update_mb_segmentation_map = 1;
cpi->mb.e_mbd.update_mb_segmentation_data = 1;
}
void vp9_disable_segmentation(VP9_PTR ptr) {
VP9_COMP *cpi = (VP9_COMP *)ptr;
cpi->mb.e_mbd.segmentation_enabled = 0;
}
void vp9_set_segmentation_map(VP9_PTR ptr,
unsigned char *segmentation_map) {
VP9_COMP *cpi = (VP9_COMP *)(ptr);
// Copy in the new segmentation map
vpx_memcpy(cpi->segmentation_map, segmentation_map,
(cpi->common.mi_rows * cpi->common.mi_cols));
// Signal that the map should be updated.
cpi->mb.e_mbd.update_mb_segmentation_map = 1;
cpi->mb.e_mbd.update_mb_segmentation_data = 1;
}
void vp9_set_segment_data(VP9_PTR ptr,
signed char *feature_data,
unsigned char abs_delta) {
VP9_COMP *cpi = (VP9_COMP *)(ptr);
cpi->mb.e_mbd.mb_segment_abs_delta = abs_delta;
vpx_memcpy(cpi->mb.e_mbd.segment_feature_data, feature_data,
sizeof(cpi->mb.e_mbd.segment_feature_data));
// TBD ?? Set the feature mask
// vpx_memcpy(cpi->mb.e_mbd.segment_feature_mask, 0,
// sizeof(cpi->mb.e_mbd.segment_feature_mask));
}
// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(MACROBLOCKD *xd, int *segcounts,
vp9_prob *segment_tree_probs) {
// Work out probabilities of each segment
const int c01 = segcounts[0] + segcounts[1];
const int c23 = segcounts[2] + segcounts[3];
const int c45 = segcounts[4] + segcounts[5];
const int c67 = segcounts[6] + segcounts[7];
segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67);
segment_tree_probs[1] = get_binary_prob(c01, c23);
segment_tree_probs[2] = get_binary_prob(c45, c67);
segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
}
// Based on set of segment counts and probabilities calculate a cost estimate
static int cost_segmap(MACROBLOCKD *xd, int *segcounts, vp9_prob *probs) {
const int c01 = segcounts[0] + segcounts[1];
const int c23 = segcounts[2] + segcounts[3];
const int c45 = segcounts[4] + segcounts[5];
const int c67 = segcounts[6] + segcounts[7];
const int c0123 = c01 + c23;
const int c4567 = c45 + c67;
// Cost the top node of the tree
int cost = c0123 * vp9_cost_zero(probs[0]) +
c4567 * vp9_cost_one(probs[0]);
// Cost subsequent levels
if (c0123 > 0) {
cost += c01 * vp9_cost_zero(probs[1]) +
c23 * vp9_cost_one(probs[1]);
if (c01 > 0)
cost += segcounts[0] * vp9_cost_zero(probs[3]) +
segcounts[1] * vp9_cost_one(probs[3]);
if (c23 > 0)
cost += segcounts[2] * vp9_cost_zero(probs[4]) +
segcounts[3] * vp9_cost_one(probs[4]);
}
if (c4567 > 0) {
cost += c45 * vp9_cost_zero(probs[2]) +
c67 * vp9_cost_one(probs[2]);
if (c45 > 0)
cost += segcounts[4] * vp9_cost_zero(probs[5]) +
segcounts[5] * vp9_cost_one(probs[5]);
if (c67 > 0)
cost += segcounts[6] * vp9_cost_zero(probs[6]) +
segcounts[7] * vp9_cost_one(probs[6]);
}
return cost;
}
static void count_segs(VP9_COMP *cpi, MODE_INFO *mi,
int *no_pred_segcounts,
int (*temporal_predictor_count)[2],
int *t_unpred_seg_counts,
int bw, int bh, int mi_row, int mi_col) {
VP9_COMMON *const cm = &cpi->common;
MACROBLOCKD *const xd = &cpi->mb.e_mbd;
int segment_id;
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
return;
segment_id = mi->mbmi.segment_id;
xd->mode_info_context = mi;
set_mi_row_col(cm, xd, mi_row, bh, mi_col, bw);
// Count the number of hits on each segment with no prediction
no_pred_segcounts[segment_id]++;
// Temporal prediction not allowed on key frames
if (cm->frame_type != KEY_FRAME) {
// Test to see if the segment id matches the predicted value.
const int pred_segment_id = vp9_get_segment_id(cm, cm->last_frame_seg_map,
mi->mbmi.sb_type,
mi_row, mi_col);
const int pred_flag = pred_segment_id == segment_id;
const int pred_context = vp9_get_pred_context(cm, xd, PRED_SEG_ID);
// Store the prediction status for this mb and update counts
// as appropriate
vp9_set_pred_flag(xd, PRED_SEG_ID, pred_flag);
temporal_predictor_count[pred_context][pred_flag]++;
if (!pred_flag)
// Update the "unpredicted" segment count
t_unpred_seg_counts[segment_id]++;
}
}
static void count_segs_sb(VP9_COMP *cpi, MODE_INFO *mi,
int *no_pred_segcounts,
int (*temporal_predictor_count)[2],
int *t_unpred_seg_counts,
int mi_row, int mi_col,
BLOCK_SIZE_TYPE bsize) {
VP9_COMMON *const cm = &cpi->common;
const int mis = cm->mode_info_stride;
int bwl, bhl;
const int bsl = mi_width_log2(bsize), bs = 1 << (bsl - 1);
if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
return;
bwl = mi_width_log2(mi->mbmi.sb_type);
bhl = mi_height_log2(mi->mbmi.sb_type);
if (bwl == bsl && bhl == bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, 1 << bsl, mi_row, mi_col);
} else if (bwl == bsl && bhl < bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, bs, mi_row, mi_col);
count_segs(cpi, mi + bs * mis, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 1 << bsl, bs, mi_row + bs, mi_col);
} else if (bwl < bsl && bhl == bsl) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col);
count_segs(cpi, mi + bs, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col + bs);
} else {
BLOCK_SIZE_TYPE subsize;
int n;
assert(bwl < bsl && bhl < bsl);
if (bsize == BLOCK_SIZE_SB64X64) {
subsize = BLOCK_SIZE_SB32X32;
} else if (bsize == BLOCK_SIZE_SB32X32) {
subsize = BLOCK_SIZE_MB16X16;
} else {
assert(bsize == BLOCK_SIZE_MB16X16);
subsize = BLOCK_SIZE_SB8X8;
}
for (n = 0; n < 4; n++) {
const int y_idx = n >> 1, x_idx = n & 0x01;
count_segs_sb(cpi, mi + y_idx * bs * mis + x_idx * bs,
no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts,
mi_row + y_idx * bs, mi_col + x_idx * bs, subsize);
}
}
}
void vp9_choose_segmap_coding_method(VP9_COMP *cpi) {
VP9_COMMON *const cm = &cpi->common;
MACROBLOCKD *const xd = &cpi->mb.e_mbd;
int no_pred_cost;
int t_pred_cost = INT_MAX;
int i;
int tile_col, mi_row, mi_col;
int temporal_predictor_count[PREDICTION_PROBS][2];
int no_pred_segcounts[MAX_MB_SEGMENTS];
int t_unpred_seg_counts[MAX_MB_SEGMENTS];
vp9_prob no_pred_tree[MB_SEG_TREE_PROBS];
vp9_prob t_pred_tree[MB_SEG_TREE_PROBS];
vp9_prob t_nopred_prob[PREDICTION_PROBS];
const int mis = cm->mode_info_stride;
MODE_INFO *mi_ptr, *mi;
// Set default state for the segment tree probabilities and the
// temporal coding probabilities
vpx_memset(xd->mb_segment_tree_probs, 255, sizeof(xd->mb_segment_tree_probs));
vpx_memset(cm->segment_pred_probs, 255, sizeof(cm->segment_pred_probs));
vpx_memset(no_pred_segcounts, 0, sizeof(no_pred_segcounts));
vpx_memset(t_unpred_seg_counts, 0, sizeof(t_unpred_seg_counts));
vpx_memset(temporal_predictor_count, 0, sizeof(temporal_predictor_count));
// First of all generate stats regarding how well the last segment map
// predicts this one
for (tile_col = 0; tile_col < cm->tile_columns; tile_col++) {
vp9_get_tile_col_offsets(cm, tile_col);
mi_ptr = cm->mi + cm->cur_tile_mi_col_start;
for (mi_row = 0; mi_row < cm->mi_rows;
mi_row += 8, mi_ptr += 8 * mis) {
mi = mi_ptr;
for (mi_col = cm->cur_tile_mi_col_start;
mi_col < cm->cur_tile_mi_col_end;
mi_col += 8, mi += 8) {
count_segs_sb(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, mi_row, mi_col, BLOCK_SIZE_SB64X64);
}
}
}
// Work out probability tree for coding segments without prediction
// and the cost.
calc_segtree_probs(xd, no_pred_segcounts, no_pred_tree);
no_pred_cost = cost_segmap(xd, no_pred_segcounts, no_pred_tree);
// Key frames cannot use temporal prediction
if (cm->frame_type != KEY_FRAME) {
// Work out probability tree for coding those segments not
// predicted using the temporal method and the cost.
calc_segtree_probs(xd, t_unpred_seg_counts, t_pred_tree);
t_pred_cost = cost_segmap(xd, t_unpred_seg_counts, t_pred_tree);
// Add in the cost of the signalling for each prediction context
for (i = 0; i < PREDICTION_PROBS; i++) {
const int count0 = temporal_predictor_count[i][0];
const int count1 = temporal_predictor_count[i][1];
t_nopred_prob[i] = get_binary_prob(count0, count1);
// Add in the predictor signaling cost
t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) +
count1 * vp9_cost_one(t_nopred_prob[i]);
}
}
// Now choose which coding method to use.
if (t_pred_cost < no_pred_cost) {
cm->temporal_update = 1;
vpx_memcpy(xd->mb_segment_tree_probs, t_pred_tree, sizeof(t_pred_tree));
vpx_memcpy(cm->segment_pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
} else {
cm->temporal_update = 0;
vpx_memcpy(xd->mb_segment_tree_probs, no_pred_tree, sizeof(no_pred_tree));
}
}
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