ref: e307e924b5af8555625f6742b2251c316ffc0f34
dir: /vp9/encoder/vp9_segmentation.c/
/*
* 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);
// Set the appropriate feature bit
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);
// Clear the appropriate feature bit
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.mb_rows * cpi->common.mb_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) {
int count1, count2;
// Total count for all segments
count1 = segcounts[0] + segcounts[1];
count2 = segcounts[2] + segcounts[3];
// Work out probabilities of each segment
segment_tree_probs[0] = get_binary_prob(count1, count2);
segment_tree_probs[1] = get_prob(segcounts[0], count1);
segment_tree_probs[2] = get_prob(segcounts[2], count2);
}
// Based on set of segment counts and probabilities calculate a cost estimate
static int cost_segmap(MACROBLOCKD *xd,
int *segcounts,
vp9_prob *probs) {
int cost;
int count1, count2;
// Cost the top node of the tree
count1 = segcounts[0] + segcounts[1];
count2 = segcounts[2] + segcounts[3];
cost = count1 * vp9_cost_zero(probs[0]) +
count2 * vp9_cost_one(probs[0]);
// Now add the cost of each individual segment branch
if (count1 > 0)
cost += segcounts[0] * vp9_cost_zero(probs[1]) +
segcounts[1] * vp9_cost_one(probs[1]);
if (count2 > 0)
cost += segcounts[2] * vp9_cost_zero(probs[2]) +
segcounts[3] * vp9_cost_one(probs[2]);
return cost;
}
// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs_pred(MACROBLOCKD *xd,
int (*segcounts)[MAX_MB_SEGMENTS],
vp9_prob *segment_tree_probs) {
int count[4];
assert(!segcounts[0][0] && !segcounts[1][1] &&
!segcounts[2][2] && !segcounts[3][3]);
// Total count for all segments
count[0] = segcounts[3][0] + segcounts[1][0] + segcounts[2][0];
count[1] = segcounts[2][1] + segcounts[0][1] + segcounts[3][1];
count[2] = segcounts[0][2] + segcounts[3][2] + segcounts[1][2];
count[3] = segcounts[1][3] + segcounts[2][3] + segcounts[0][3];
// Work out probabilities of each segment
segment_tree_probs[0] = get_binary_prob(count[0] + count[1],
count[2] + count[3]);
segment_tree_probs[1] = get_binary_prob(count[0], count[1]);
segment_tree_probs[2] = get_binary_prob(count[2], count[3]);
}
// Based on set of segment counts and probabilities calculate a cost estimate
static int cost_segmap_pred(MACROBLOCKD *xd,
int (*segcounts)[MAX_MB_SEGMENTS],
vp9_prob *probs) {
int pred_seg, cost = 0;
for (pred_seg = 0; pred_seg < MAX_MB_SEGMENTS; pred_seg++) {
int count1, count2;
// Cost the top node of the tree
count1 = segcounts[pred_seg][0] + segcounts[pred_seg][1];
count2 = segcounts[pred_seg][2] + segcounts[pred_seg][3];
cost += count1 * vp9_cost_zero(probs[0]) +
count2 * vp9_cost_one(probs[0]);
// Now add the cost of each individual segment branch
if (pred_seg >= 2 && count1) {
cost += segcounts[pred_seg][0] * vp9_cost_zero(probs[1]) +
segcounts[pred_seg][1] * vp9_cost_one(probs[1]);
} else if (pred_seg < 2 && count2 > 0) {
cost += segcounts[pred_seg][2] * vp9_cost_zero(probs[2]) +
segcounts[pred_seg][3] * vp9_cost_one(probs[2]);
}
}
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)[MAX_MB_SEGMENTS],
int bw, int bh, int mb_row, int mb_col) {
VP9_COMMON *const cm = &cpi->common;
MACROBLOCKD *const xd = &cpi->mb.e_mbd;
const int segment_id = mi->mbmi.segment_id;
xd->mode_info_context = mi;
set_mb_row(cm, xd, mb_row, bh);
set_mb_col(cm, xd, mb_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_seg_id = vp9_get_pred_mb_segid(cm, mi->mbmi.sb_type,
mb_row, mb_col);
const int seg_predicted = (segment_id == pred_seg_id);
// Get the segment id prediction context
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, seg_predicted);
temporal_predictor_count[pred_context][seg_predicted]++;
if (!seg_predicted)
// Update the "unpredicted" segment count
t_unpred_seg_counts[pred_seg_id][segment_id]++;
}
}
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, mb_row, mb_col;
int temporal_predictor_count[PREDICTION_PROBS][2];
int no_pred_segcounts[MAX_MB_SEGMENTS];
int t_unpred_seg_counts[MAX_MB_SEGMENTS][MAX_MB_SEGMENTS];
vp9_prob no_pred_tree[MB_FEATURE_TREE_PROBS];
vp9_prob t_pred_tree[MB_FEATURE_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_mb_col_start;
for (mb_row = 0; mb_row < cm->mb_rows; mb_row += 4, mi_ptr += 4 * mis) {
mi = mi_ptr;
for (mb_col = cm->cur_tile_mb_col_start;
mb_col < cm->cur_tile_mb_col_end; mb_col += 4, mi += 4) {
if (mi->mbmi.sb_type == BLOCK_SIZE_SB64X64) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 4, 4, mb_row, mb_col);
#if CONFIG_SBSEGMENT
} else if (mi->mbmi.sb_type == BLOCK_SIZE_SB64X32) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 4, 2, mb_row, mb_col);
if (mb_row + 2 != cm->mb_rows)
count_segs(cpi, mi + 2 * mis, no_pred_segcounts,
temporal_predictor_count,
t_unpred_seg_counts, 4, 2, mb_row + 2, mb_col);
} else if (mi->mbmi.sb_type == BLOCK_SIZE_SB32X64) {
count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 2, 4, mb_row, mb_col);
if (mb_col + 2 != cm->mb_cols)
count_segs(cpi, mi + 2, no_pred_segcounts, temporal_predictor_count,
t_unpred_seg_counts, 2, 4, mb_row, mb_col + 2);
#endif
} else {
for (i = 0; i < 4; i++) {
int x_idx = (i & 1) << 1, y_idx = i & 2;
MODE_INFO *sb_mi = mi + y_idx * mis + x_idx;
if (mb_col + x_idx >= cm->mb_cols ||
mb_row + y_idx >= cm->mb_rows) {
continue;
}
if (sb_mi->mbmi.sb_type == BLOCK_SIZE_SB32X32) {
count_segs(cpi, sb_mi, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts, 2, 2,
mb_row + y_idx, mb_col + x_idx);
#if CONFIG_SBSEGMENT
} else if (sb_mi->mbmi.sb_type == BLOCK_SIZE_SB32X16) {
count_segs(cpi, sb_mi, no_pred_segcounts,
temporal_predictor_count,
t_unpred_seg_counts, 2, 1,
mb_row + y_idx, mb_col + x_idx);
if (mb_row + y_idx + 1 != cm->mb_rows)
count_segs(cpi, sb_mi + mis, no_pred_segcounts,
temporal_predictor_count,
t_unpred_seg_counts, 2, 1,
mb_row + y_idx + 1, mb_col + x_idx);
} else if (sb_mi->mbmi.sb_type == BLOCK_SIZE_SB16X32) {
count_segs(cpi, sb_mi, no_pred_segcounts,
temporal_predictor_count,
t_unpred_seg_counts, 1, 2,
mb_row + y_idx, mb_col + x_idx);
if (mb_col + x_idx + 1 != cm->mb_cols)
count_segs(cpi, sb_mi + 1, no_pred_segcounts,
temporal_predictor_count,
t_unpred_seg_counts, 1, 2,
mb_row + y_idx, mb_col + x_idx + 1);
#endif
} else {
int j;
for (j = 0; j < 4; j++) {
const int x_idx_mb = x_idx + (j & 1);
const int y_idx_mb = y_idx + (j >> 1);
MODE_INFO *mb_mi = mi + x_idx_mb + y_idx_mb * mis;
if (mb_col + x_idx_mb >= cm->mb_cols ||
mb_row + y_idx_mb >= cm->mb_rows) {
continue;
}
assert(mb_mi->mbmi.sb_type == BLOCK_SIZE_MB16X16);
count_segs(cpi, mb_mi, no_pred_segcounts,
temporal_predictor_count, t_unpred_seg_counts,
1, 1, mb_row + y_idx_mb, mb_col + x_idx_mb);
}
}
}
}
}
}
}
// 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_pred(xd, t_unpred_seg_counts, t_pred_tree);
t_pred_cost = cost_segmap_pred(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++) {
t_nopred_prob[i] = get_binary_prob(temporal_predictor_count[i][0],
temporal_predictor_count[i][1]);
// Add in the predictor signaling cost
t_pred_cost += (temporal_predictor_count[i][0] *
vp9_cost_zero(t_nopred_prob[i])) +
(temporal_predictor_count[i][1] *
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));
}
}