| /* |
| * Copyright (c) 2010 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 <assert.h> |
| #include <float.h> |
| #include <limits.h> |
| #include <math.h> |
| |
| #include "./aom_scale_rtcd.h" |
| |
| #include "aom_dsp/psnr.h" |
| #include "aom_dsp/aom_dsp_common.h" |
| #include "aom_mem/aom_mem.h" |
| #include "aom_ports/mem.h" |
| |
| #include "av1/common/onyxc_int.h" |
| #include "av1/common/quant_common.h" |
| |
| #include "av1/encoder/encoder.h" |
| #include "av1/encoder/picklpf.h" |
| #include "av1/encoder/pickrst.h" |
| #include "av1/encoder/quantize.h" |
| |
| static int64_t try_restoration_frame(const YV12_BUFFER_CONFIG *sd, |
| AV1_COMP *const cpi, RestorationInfo *rsi, |
| int partial_frame) { |
| AV1_COMMON *const cm = &cpi->common; |
| int64_t filt_err; |
| av1_loop_restoration_frame(cm->frame_to_show, cm, rsi, 1, partial_frame); |
| #if CONFIG_AOM_HIGHBITDEPTH |
| if (cm->use_highbitdepth) { |
| filt_err = aom_highbd_get_y_sse(sd, cm->frame_to_show); |
| } else { |
| filt_err = aom_get_y_sse(sd, cm->frame_to_show); |
| } |
| #else |
| filt_err = aom_get_y_sse(sd, cm->frame_to_show); |
| #endif // CONFIG_AOM_HIGHBITDEPTH |
| |
| // Re-instate the unfiltered frame |
| aom_yv12_copy_y(&cpi->last_frame_db, cm->frame_to_show); |
| return filt_err; |
| } |
| |
| static int search_bilateral_level(const YV12_BUFFER_CONFIG *sd, AV1_COMP *cpi, |
| int filter_level, int partial_frame, |
| int *bilateral_level, double *best_cost_ret) { |
| AV1_COMMON *const cm = &cpi->common; |
| int i, j, tile_idx; |
| int64_t err; |
| int bits; |
| double cost, best_cost, cost_norestore, cost_bilateral; |
| const int bilateral_level_bits = av1_bilateral_level_bits(&cpi->common); |
| const int bilateral_levels = 1 << bilateral_level_bits; |
| MACROBLOCK *x = &cpi->td.mb; |
| RestorationInfo rsi; |
| const int ntiles = |
| av1_get_restoration_ntiles(BILATERAL_TILESIZE, cm->width, cm->height); |
| |
| // Make a copy of the unfiltered / processed recon buffer |
| aom_yv12_copy_y(cm->frame_to_show, &cpi->last_frame_uf); |
| av1_loop_filter_frame(cm->frame_to_show, cm, &cpi->td.mb.e_mbd, filter_level, |
| 1, partial_frame); |
| aom_yv12_copy_y(cm->frame_to_show, &cpi->last_frame_db); |
| |
| // RD cost associated with no restoration |
| rsi.restoration_type = RESTORE_NONE; |
| err = try_restoration_frame(sd, cpi, &rsi, partial_frame); |
| bits = 0; |
| cost_norestore = |
| RDCOST_DBL(x->rdmult, x->rddiv, (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| best_cost = cost_norestore; |
| |
| // RD cost associated with bilateral filtering |
| rsi.restoration_type = RESTORE_BILATERAL; |
| rsi.bilateral_level = |
| (int *)aom_malloc(sizeof(*rsi.bilateral_level) * ntiles); |
| assert(rsi.bilateral_level != NULL); |
| |
| for (j = 0; j < ntiles; ++j) bilateral_level[j] = -1; |
| |
| // Find best filter for each tile |
| for (tile_idx = 0; tile_idx < ntiles; ++tile_idx) { |
| for (j = 0; j < ntiles; ++j) rsi.bilateral_level[j] = -1; |
| best_cost = cost_norestore; |
| for (i = 0; i < bilateral_levels; ++i) { |
| rsi.bilateral_level[tile_idx] = i; |
| err = try_restoration_frame(sd, cpi, &rsi, partial_frame); |
| bits = bilateral_level_bits + 1; |
| // Normally the rate is rate in bits * 256 and dist is sum sq err * 64 |
| // when RDCOST is used. However below we just scale both in the correct |
| // ratios appropriately but not exactly by these values. |
| cost = RDCOST_DBL(x->rdmult, x->rddiv, |
| (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| if (cost < best_cost) { |
| bilateral_level[tile_idx] = i; |
| best_cost = cost; |
| } |
| } |
| } |
| // Find cost for combined configuration |
| bits = 0; |
| for (j = 0; j < ntiles; ++j) { |
| rsi.bilateral_level[j] = bilateral_level[j]; |
| if (rsi.bilateral_level[j] >= 0) { |
| bits += (bilateral_level_bits + 1); |
| } else { |
| bits += 1; |
| } |
| } |
| err = try_restoration_frame(sd, cpi, &rsi, partial_frame); |
| cost_bilateral = |
| RDCOST_DBL(x->rdmult, x->rddiv, (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| |
| aom_free(rsi.bilateral_level); |
| |
| aom_yv12_copy_y(&cpi->last_frame_uf, cm->frame_to_show); |
| if (cost_bilateral < cost_norestore) { |
| if (best_cost_ret) *best_cost_ret = cost_bilateral; |
| return 1; |
| } else { |
| if (best_cost_ret) *best_cost_ret = cost_norestore; |
| return 0; |
| } |
| } |
| |
| static int search_filter_bilateral_level(const YV12_BUFFER_CONFIG *sd, |
| AV1_COMP *cpi, int partial_frame, |
| int *filter_best, int *bilateral_level, |
| double *best_cost_ret) { |
| const AV1_COMMON *const cm = &cpi->common; |
| const struct loopfilter *const lf = &cm->lf; |
| const int min_filter_level = 0; |
| const int max_filter_level = av1_get_max_filter_level(cpi); |
| int filt_direction = 0; |
| int filt_best; |
| double best_err; |
| int i, j; |
| int *tmp_level; |
| int bilateral_success[MAX_LOOP_FILTER + 1]; |
| |
| const int ntiles = |
| av1_get_restoration_ntiles(BILATERAL_TILESIZE, cm->width, cm->height); |
| |
| // Start the search at the previous frame filter level unless it is now out of |
| // range. |
| int filt_mid = clamp(lf->filter_level, min_filter_level, max_filter_level); |
| int filter_step = filt_mid < 16 ? 4 : filt_mid / 4; |
| double ss_err[MAX_LOOP_FILTER + 1]; |
| // Set each entry to -1 |
| for (i = 0; i <= MAX_LOOP_FILTER; ++i) ss_err[i] = -1.0; |
| |
| tmp_level = (int *)aom_malloc(sizeof(*tmp_level) * ntiles); |
| |
| bilateral_success[filt_mid] = search_bilateral_level( |
| sd, cpi, filt_mid, partial_frame, tmp_level, &best_err); |
| filt_best = filt_mid; |
| ss_err[filt_mid] = best_err; |
| for (j = 0; j < ntiles; ++j) { |
| bilateral_level[j] = tmp_level[j]; |
| } |
| |
| while (filter_step > 0) { |
| const int filt_high = AOMMIN(filt_mid + filter_step, max_filter_level); |
| const int filt_low = AOMMAX(filt_mid - filter_step, min_filter_level); |
| |
| // Bias against raising loop filter in favor of lowering it. |
| double bias = (best_err / (1 << (15 - (filt_mid / 8)))) * filter_step; |
| |
| if ((cpi->oxcf.pass == 2) && (cpi->twopass.section_intra_rating < 20)) |
| bias = (bias * cpi->twopass.section_intra_rating) / 20; |
| |
| // yx, bias less for large block size |
| if (cm->tx_mode != ONLY_4X4) bias /= 2; |
| |
| if (filt_direction <= 0 && filt_low != filt_mid) { |
| // Get Low filter error score |
| if (ss_err[filt_low] < 0) { |
| bilateral_success[filt_low] = search_bilateral_level( |
| sd, cpi, filt_low, partial_frame, tmp_level, &ss_err[filt_low]); |
| } |
| // If value is close to the best so far then bias towards a lower loop |
| // filter value. |
| if (ss_err[filt_low] < (best_err + bias)) { |
| // Was it actually better than the previous best? |
| if (ss_err[filt_low] < best_err) { |
| best_err = ss_err[filt_low]; |
| } |
| filt_best = filt_low; |
| for (j = 0; j < ntiles; ++j) { |
| bilateral_level[j] = tmp_level[j]; |
| } |
| } |
| } |
| |
| // Now look at filt_high |
| if (filt_direction >= 0 && filt_high != filt_mid) { |
| if (ss_err[filt_high] < 0) { |
| bilateral_success[filt_high] = search_bilateral_level( |
| sd, cpi, filt_high, partial_frame, tmp_level, &ss_err[filt_high]); |
| } |
| // If value is significantly better than previous best, bias added against |
| // raising filter value |
| if (ss_err[filt_high] < (best_err - bias)) { |
| best_err = ss_err[filt_high]; |
| filt_best = filt_high; |
| for (j = 0; j < ntiles; ++j) { |
| bilateral_level[j] = tmp_level[j]; |
| } |
| } |
| } |
| |
| // Half the step distance if the best filter value was the same as last time |
| if (filt_best == filt_mid) { |
| filter_step /= 2; |
| filt_direction = 0; |
| } else { |
| filt_direction = (filt_best < filt_mid) ? -1 : 1; |
| filt_mid = filt_best; |
| } |
| } |
| |
| aom_free(tmp_level); |
| |
| // Update best error |
| best_err = ss_err[filt_best]; |
| |
| if (best_cost_ret) *best_cost_ret = best_err; |
| if (filter_best) *filter_best = filt_best; |
| |
| return bilateral_success[filt_best]; |
| } |
| |
| static double find_average(uint8_t *src, int h_start, int h_end, int v_start, |
| int v_end, int stride) { |
| uint64_t sum = 0; |
| double avg = 0; |
| int i, j; |
| for (i = v_start; i < v_end; i++) |
| for (j = h_start; j < h_end; j++) sum += src[i * stride + j]; |
| avg = (double)sum / ((v_end - v_start) * (h_end - h_start)); |
| return avg; |
| } |
| |
| static void compute_stats(uint8_t *dgd, uint8_t *src, int h_start, int h_end, |
| int v_start, int v_end, int dgd_stride, |
| int src_stride, double *M, double *H) { |
| int i, j, k, l; |
| double Y[RESTORATION_WIN2]; |
| const double avg = |
| find_average(dgd, h_start, h_end, v_start, v_end, dgd_stride); |
| |
| memset(M, 0, sizeof(*M) * RESTORATION_WIN2); |
| memset(H, 0, sizeof(*H) * RESTORATION_WIN2 * RESTORATION_WIN2); |
| for (i = v_start; i < v_end; i++) { |
| for (j = h_start; j < h_end; j++) { |
| const double X = (double)src[i * src_stride + j] - avg; |
| int idx = 0; |
| for (k = -RESTORATION_HALFWIN; k <= RESTORATION_HALFWIN; k++) { |
| for (l = -RESTORATION_HALFWIN; l <= RESTORATION_HALFWIN; l++) { |
| Y[idx] = (double)dgd[(i + l) * dgd_stride + (j + k)] - avg; |
| idx++; |
| } |
| } |
| for (k = 0; k < RESTORATION_WIN2; ++k) { |
| M[k] += Y[k] * X; |
| H[k * RESTORATION_WIN2 + k] += Y[k] * Y[k]; |
| for (l = k + 1; l < RESTORATION_WIN2; ++l) { |
| double value = Y[k] * Y[l]; |
| H[k * RESTORATION_WIN2 + l] += value; |
| H[l * RESTORATION_WIN2 + k] += value; |
| } |
| } |
| } |
| } |
| } |
| |
| #if CONFIG_AOM_HIGHBITDEPTH |
| static double find_average_highbd(uint16_t *src, int h_start, int h_end, |
| int v_start, int v_end, int stride) { |
| uint64_t sum = 0; |
| double avg = 0; |
| int i, j; |
| for (i = v_start; i < v_end; i++) |
| for (j = h_start; j < h_end; j++) sum += src[i * stride + j]; |
| avg = (double)sum / ((v_end - v_start) * (h_end - h_start)); |
| return avg; |
| } |
| |
| static void compute_stats_highbd(uint8_t *dgd8, uint8_t *src8, int h_start, |
| int h_end, int v_start, int v_end, |
| int dgd_stride, int src_stride, double *M, |
| double *H) { |
| int i, j, k, l; |
| double Y[RESTORATION_WIN2]; |
| uint16_t *src = CONVERT_TO_SHORTPTR(src8); |
| uint16_t *dgd = CONVERT_TO_SHORTPTR(dgd8); |
| const double avg = |
| find_average_highbd(dgd, h_start, h_end, v_start, v_end, dgd_stride); |
| |
| memset(M, 0, sizeof(*M) * RESTORATION_WIN2); |
| memset(H, 0, sizeof(*H) * RESTORATION_WIN2 * RESTORATION_WIN2); |
| for (i = v_start; i < v_end; i++) { |
| for (j = h_start; j < h_end; j++) { |
| const double X = (double)src[i * src_stride + j] - avg; |
| int idx = 0; |
| for (k = -RESTORATION_HALFWIN; k <= RESTORATION_HALFWIN; k++) { |
| for (l = -RESTORATION_HALFWIN; l <= RESTORATION_HALFWIN; l++) { |
| Y[idx] = (double)dgd[(i + l) * dgd_stride + (j + k)] - avg; |
| idx++; |
| } |
| } |
| for (k = 0; k < RESTORATION_WIN2; ++k) { |
| M[k] += Y[k] * X; |
| H[k * RESTORATION_WIN2 + k] += Y[k] * Y[k]; |
| for (l = k + 1; l < RESTORATION_WIN2; ++l) { |
| double value = Y[k] * Y[l]; |
| H[k * RESTORATION_WIN2 + l] += value; |
| H[l * RESTORATION_WIN2 + k] += value; |
| } |
| } |
| } |
| } |
| } |
| #endif // CONFIG_AOM_HIGHBITDEPTH |
| |
| // Solves Ax = b, where x and b are column vectors |
| static int linsolve(int n, double *A, int stride, double *b, double *x) { |
| int i, j, k; |
| double c; |
| // Partial pivoting |
| for (i = n - 1; i > 0; i--) { |
| if (A[(i - 1) * stride] < A[i * stride]) { |
| for (j = 0; j < n; j++) { |
| c = A[i * stride + j]; |
| A[i * stride + j] = A[(i - 1) * stride + j]; |
| A[(i - 1) * stride + j] = c; |
| } |
| c = b[i]; |
| b[i] = b[i - 1]; |
| b[i - 1] = c; |
| } |
| } |
| // Forward elimination |
| for (k = 0; k < n - 1; k++) { |
| for (i = k; i < n - 1; i++) { |
| c = A[(i + 1) * stride + k] / A[k * stride + k]; |
| for (j = 0; j < n; j++) A[(i + 1) * stride + j] -= c * A[k * stride + j]; |
| b[i + 1] -= c * b[k]; |
| } |
| } |
| // Backward substitution |
| for (i = n - 1; i >= 0; i--) { |
| if (fabs(A[i * stride + i]) < 1e-10) return 0; |
| c = 0; |
| for (j = i + 1; j <= n - 1; j++) c += A[i * stride + j] * x[j]; |
| x[i] = (b[i] - c) / A[i * stride + i]; |
| } |
| return 1; |
| } |
| |
| static INLINE int wrap_index(int i) { |
| return (i >= RESTORATION_HALFWIN1 ? RESTORATION_WIN - 1 - i : i); |
| } |
| |
| // Fix vector b, update vector a |
| static void update_a_sep_sym(double **Mc, double **Hc, double *a, double *b) { |
| int i, j; |
| double S[RESTORATION_WIN]; |
| double A[RESTORATION_WIN], B[RESTORATION_WIN2]; |
| int w, w2; |
| memset(A, 0, sizeof(A)); |
| memset(B, 0, sizeof(B)); |
| for (i = 0; i < RESTORATION_WIN; i++) { |
| int j; |
| for (j = 0; j < RESTORATION_WIN; ++j) { |
| const int jj = wrap_index(j); |
| A[jj] += Mc[i][j] * b[i]; |
| } |
| } |
| for (i = 0; i < RESTORATION_WIN; i++) { |
| for (j = 0; j < RESTORATION_WIN; j++) { |
| int k, l; |
| for (k = 0; k < RESTORATION_WIN; ++k) |
| for (l = 0; l < RESTORATION_WIN; ++l) { |
| const int kk = wrap_index(k); |
| const int ll = wrap_index(l); |
| B[ll * RESTORATION_HALFWIN1 + kk] += |
| Hc[j * RESTORATION_WIN + i][k * RESTORATION_WIN2 + l] * b[i] * |
| b[j]; |
| } |
| } |
| } |
| // Normalization enforcement in the system of equations itself |
| w = RESTORATION_WIN; |
| w2 = (w >> 1) + 1; |
| for (i = 0; i < w2 - 1; ++i) |
| A[i] -= |
| A[w2 - 1] * 2 + B[i * w2 + w2 - 1] - 2 * B[(w2 - 1) * w2 + (w2 - 1)]; |
| for (i = 0; i < w2 - 1; ++i) |
| for (j = 0; j < w2 - 1; ++j) |
| B[i * w2 + j] -= 2 * (B[i * w2 + (w2 - 1)] + B[(w2 - 1) * w2 + j] - |
| 2 * B[(w2 - 1) * w2 + (w2 - 1)]); |
| if (linsolve(w2 - 1, B, w2, A, S)) { |
| S[w2 - 1] = 1.0; |
| for (i = w2; i < w; ++i) { |
| S[i] = S[w - 1 - i]; |
| S[w2 - 1] -= 2 * S[i]; |
| } |
| memcpy(a, S, w * sizeof(*a)); |
| } |
| } |
| |
| // Fix vector a, update vector b |
| static void update_b_sep_sym(double **Mc, double **Hc, double *a, double *b) { |
| int i, j; |
| double S[RESTORATION_WIN]; |
| double A[RESTORATION_WIN], B[RESTORATION_WIN2]; |
| int w, w2; |
| memset(A, 0, sizeof(A)); |
| memset(B, 0, sizeof(B)); |
| for (i = 0; i < RESTORATION_WIN; i++) { |
| int j; |
| const int ii = wrap_index(i); |
| for (j = 0; j < RESTORATION_WIN; j++) A[ii] += Mc[i][j] * a[j]; |
| } |
| |
| for (i = 0; i < RESTORATION_WIN; i++) { |
| for (j = 0; j < RESTORATION_WIN; j++) { |
| const int ii = wrap_index(i); |
| const int jj = wrap_index(j); |
| int k, l; |
| for (k = 0; k < RESTORATION_WIN; ++k) |
| for (l = 0; l < RESTORATION_WIN; ++l) |
| B[jj * RESTORATION_HALFWIN1 + ii] += |
| Hc[i * RESTORATION_WIN + j][k * RESTORATION_WIN2 + l] * a[k] * |
| a[l]; |
| } |
| } |
| // Normalization enforcement in the system of equations itself |
| w = RESTORATION_WIN; |
| w2 = RESTORATION_HALFWIN1; |
| for (i = 0; i < w2 - 1; ++i) |
| A[i] -= |
| A[w2 - 1] * 2 + B[i * w2 + w2 - 1] - 2 * B[(w2 - 1) * w2 + (w2 - 1)]; |
| for (i = 0; i < w2 - 1; ++i) |
| for (j = 0; j < w2 - 1; ++j) |
| B[i * w2 + j] -= 2 * (B[i * w2 + (w2 - 1)] + B[(w2 - 1) * w2 + j] - |
| 2 * B[(w2 - 1) * w2 + (w2 - 1)]); |
| if (linsolve(w2 - 1, B, w2, A, S)) { |
| S[w2 - 1] = 1.0; |
| for (i = w2; i < w; ++i) { |
| S[i] = S[w - 1 - i]; |
| S[w2 - 1] -= 2 * S[i]; |
| } |
| memcpy(b, S, w * sizeof(*b)); |
| } |
| } |
| |
| static int wiener_decompose_sep_sym(double *M, double *H, double *a, |
| double *b) { |
| static const double init_filt[RESTORATION_WIN] = { |
| 0.035623, -0.127154, 0.211436, 0.760190, 0.211436, -0.127154, 0.035623, |
| }; |
| int i, j, iter; |
| double *Hc[RESTORATION_WIN2]; |
| double *Mc[RESTORATION_WIN]; |
| for (i = 0; i < RESTORATION_WIN; i++) { |
| Mc[i] = M + i * RESTORATION_WIN; |
| for (j = 0; j < RESTORATION_WIN; j++) { |
| Hc[i * RESTORATION_WIN + j] = |
| H + i * RESTORATION_WIN * RESTORATION_WIN2 + j * RESTORATION_WIN; |
| } |
| } |
| memcpy(a, init_filt, sizeof(*a) * RESTORATION_WIN); |
| memcpy(b, init_filt, sizeof(*b) * RESTORATION_WIN); |
| |
| iter = 1; |
| while (iter < 10) { |
| update_a_sep_sym(Mc, Hc, a, b); |
| update_b_sep_sym(Mc, Hc, a, b); |
| iter++; |
| } |
| return 1; |
| } |
| |
| // Computes the function x'*A*x - x'*b for the learned filters, and compares |
| // against identity filters; Final score is defined as the difference between |
| // the function values |
| static double compute_score(double *M, double *H, int *vfilt, int *hfilt) { |
| double ab[RESTORATION_WIN * RESTORATION_WIN]; |
| int i, k, l; |
| double P = 0, Q = 0; |
| double iP = 0, iQ = 0; |
| double Score, iScore; |
| int w; |
| double a[RESTORATION_WIN], b[RESTORATION_WIN]; |
| w = RESTORATION_WIN; |
| a[RESTORATION_HALFWIN] = b[RESTORATION_HALFWIN] = 1.0; |
| for (i = 0; i < RESTORATION_HALFWIN; ++i) { |
| a[i] = a[RESTORATION_WIN - i - 1] = |
| (double)vfilt[i] / RESTORATION_FILT_STEP; |
| b[i] = b[RESTORATION_WIN - i - 1] = |
| (double)hfilt[i] / RESTORATION_FILT_STEP; |
| a[RESTORATION_HALFWIN] -= 2 * a[i]; |
| b[RESTORATION_HALFWIN] -= 2 * b[i]; |
| } |
| for (k = 0; k < w; ++k) { |
| for (l = 0; l < w; ++l) { |
| ab[k * w + l] = a[l] * b[k]; |
| } |
| } |
| for (k = 0; k < w * w; ++k) { |
| P += ab[k] * M[k]; |
| for (l = 0; l < w * w; ++l) Q += ab[k] * H[k * w * w + l] * ab[l]; |
| } |
| Score = Q - 2 * P; |
| |
| iP = M[(w * w) >> 1]; |
| iQ = H[((w * w) >> 1) * w * w + ((w * w) >> 1)]; |
| iScore = iQ - 2 * iP; |
| |
| return Score - iScore; |
| } |
| |
| #define CLIP(x, lo, hi) ((x) < (lo) ? (lo) : (x) > (hi) ? (hi) : (x)) |
| #define RINT(x) ((x) < 0 ? (int)((x)-0.5) : (int)((x) + 0.5)) |
| |
| static void quantize_sym_filter(double *f, int *fi) { |
| int i; |
| for (i = 0; i < RESTORATION_HALFWIN; ++i) { |
| fi[i] = RINT(f[i] * RESTORATION_FILT_STEP); |
| } |
| // Specialize for 7-tap filter |
| fi[0] = CLIP(fi[0], WIENER_FILT_TAP0_MINV, WIENER_FILT_TAP0_MAXV); |
| fi[1] = CLIP(fi[1], WIENER_FILT_TAP1_MINV, WIENER_FILT_TAP1_MAXV); |
| fi[2] = CLIP(fi[2], WIENER_FILT_TAP2_MINV, WIENER_FILT_TAP2_MAXV); |
| } |
| |
| static int search_wiener_filter(const YV12_BUFFER_CONFIG *src, AV1_COMP *cpi, |
| int filter_level, int partial_frame, |
| int (*vfilter)[RESTORATION_HALFWIN], |
| int (*hfilter)[RESTORATION_HALFWIN], |
| int *process_tile, double *best_cost_ret) { |
| AV1_COMMON *const cm = &cpi->common; |
| RestorationInfo rsi; |
| int64_t err; |
| int bits; |
| double cost_wiener, cost_norestore; |
| MACROBLOCK *x = &cpi->td.mb; |
| double M[RESTORATION_WIN2]; |
| double H[RESTORATION_WIN2 * RESTORATION_WIN2]; |
| double vfilterd[RESTORATION_WIN], hfilterd[RESTORATION_WIN]; |
| const YV12_BUFFER_CONFIG *dgd = cm->frame_to_show; |
| const int width = cm->width; |
| const int height = cm->height; |
| const int src_stride = src->y_stride; |
| const int dgd_stride = dgd->y_stride; |
| double score; |
| int tile_idx, htile_idx, vtile_idx, tile_width, tile_height, nhtiles, nvtiles; |
| int h_start, h_end, v_start, v_end; |
| int i, j; |
| |
| const int tilesize = WIENER_TILESIZE; |
| const int ntiles = av1_get_restoration_ntiles(tilesize, width, height); |
| |
| assert(width == dgd->y_crop_width); |
| assert(height == dgd->y_crop_height); |
| assert(width == src->y_crop_width); |
| assert(height == src->y_crop_height); |
| |
| av1_get_restoration_tile_size(tilesize, width, height, &tile_width, |
| &tile_height, &nhtiles, &nvtiles); |
| |
| // Make a copy of the unfiltered / processed recon buffer |
| aom_yv12_copy_y(cm->frame_to_show, &cpi->last_frame_uf); |
| av1_loop_filter_frame(cm->frame_to_show, cm, &cpi->td.mb.e_mbd, filter_level, |
| 1, partial_frame); |
| aom_yv12_copy_y(cm->frame_to_show, &cpi->last_frame_db); |
| |
| rsi.restoration_type = RESTORE_NONE; |
| err = try_restoration_frame(src, cpi, &rsi, partial_frame); |
| bits = 0; |
| cost_norestore = |
| RDCOST_DBL(x->rdmult, x->rddiv, (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| |
| rsi.restoration_type = RESTORE_WIENER; |
| rsi.vfilter = |
| (int(*)[RESTORATION_HALFWIN])aom_malloc(sizeof(*rsi.vfilter) * ntiles); |
| assert(rsi.vfilter != NULL); |
| rsi.hfilter = |
| (int(*)[RESTORATION_HALFWIN])aom_malloc(sizeof(*rsi.hfilter) * ntiles); |
| assert(rsi.hfilter != NULL); |
| rsi.wiener_level = (int *)aom_malloc(sizeof(*rsi.wiener_level) * ntiles); |
| assert(rsi.wiener_level != NULL); |
| |
| // Compute best Wiener filters for each tile |
| for (tile_idx = 0; tile_idx < ntiles; ++tile_idx) { |
| htile_idx = tile_idx % nhtiles; |
| vtile_idx = tile_idx / nhtiles; |
| h_start = |
| htile_idx * tile_width + ((htile_idx > 0) ? 0 : RESTORATION_HALFWIN); |
| h_end = (htile_idx < nhtiles - 1) ? ((htile_idx + 1) * tile_width) |
| : (width - RESTORATION_HALFWIN); |
| v_start = |
| vtile_idx * tile_height + ((vtile_idx > 0) ? 0 : RESTORATION_HALFWIN); |
| v_end = (vtile_idx < nvtiles - 1) ? ((vtile_idx + 1) * tile_height) |
| : (height - RESTORATION_HALFWIN); |
| |
| #if CONFIG_AOM_HIGHBITDEPTH |
| if (cm->use_highbitdepth) |
| compute_stats_highbd(dgd->y_buffer, src->y_buffer, h_start, h_end, |
| v_start, v_end, dgd_stride, src_stride, M, H); |
| else |
| #endif // CONFIG_AOM_HIGHBITDEPTH |
| compute_stats(dgd->y_buffer, src->y_buffer, h_start, h_end, v_start, |
| v_end, dgd_stride, src_stride, M, H); |
| |
| if (!wiener_decompose_sep_sym(M, H, vfilterd, hfilterd)) { |
| for (i = 0; i < RESTORATION_HALFWIN; ++i) |
| rsi.vfilter[tile_idx][i] = rsi.hfilter[tile_idx][i] = 0; |
| process_tile[tile_idx] = 0; |
| continue; |
| } |
| quantize_sym_filter(vfilterd, rsi.vfilter[tile_idx]); |
| quantize_sym_filter(hfilterd, rsi.hfilter[tile_idx]); |
| process_tile[tile_idx] = 1; |
| |
| // Filter score computes the value of the function x'*A*x - x'*b for the |
| // learned filter and compares it against identity filer. If there is no |
| // reduction in the function, the filter is reverted back to identity |
| score = compute_score(M, H, rsi.vfilter[tile_idx], rsi.hfilter[tile_idx]); |
| if (score > 0.0) { |
| for (i = 0; i < RESTORATION_HALFWIN; ++i) |
| rsi.vfilter[tile_idx][i] = rsi.hfilter[tile_idx][i] = 0; |
| process_tile[tile_idx] = 0; |
| continue; |
| } |
| |
| for (j = 0; j < ntiles; ++j) rsi.wiener_level[j] = 0; |
| rsi.wiener_level[tile_idx] = 1; |
| |
| err = try_restoration_frame(src, cpi, &rsi, partial_frame); |
| bits = 1 + WIENER_FILT_BITS; |
| cost_wiener = RDCOST_DBL(x->rdmult, x->rddiv, |
| (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| if (cost_wiener >= cost_norestore) process_tile[tile_idx] = 0; |
| } |
| // Cost for Wiener filtering |
| bits = 0; |
| for (tile_idx = 0; tile_idx < ntiles; ++tile_idx) { |
| bits += (process_tile[tile_idx] ? (WIENER_FILT_BITS + 1) : 1); |
| rsi.wiener_level[tile_idx] = process_tile[tile_idx]; |
| } |
| err = try_restoration_frame(src, cpi, &rsi, partial_frame); |
| cost_wiener = |
| RDCOST_DBL(x->rdmult, x->rddiv, (bits << (AV1_PROB_COST_SHIFT - 4)), err); |
| |
| for (tile_idx = 0; tile_idx < ntiles; ++tile_idx) { |
| if (process_tile[tile_idx] == 0) continue; |
| for (i = 0; i < RESTORATION_HALFWIN; ++i) { |
| vfilter[tile_idx][i] = rsi.vfilter[tile_idx][i]; |
| hfilter[tile_idx][i] = rsi.hfilter[tile_idx][i]; |
| } |
| } |
| |
| aom_free(rsi.vfilter); |
| aom_free(rsi.hfilter); |
| aom_free(rsi.wiener_level); |
| |
| aom_yv12_copy_y(&cpi->last_frame_uf, cm->frame_to_show); |
| if (cost_wiener < cost_norestore) { |
| if (best_cost_ret) *best_cost_ret = cost_wiener; |
| return 1; |
| } else { |
| if (best_cost_ret) *best_cost_ret = cost_norestore; |
| return 0; |
| } |
| } |
| |
| void av1_pick_filter_restoration(const YV12_BUFFER_CONFIG *sd, AV1_COMP *cpi, |
| LPF_PICK_METHOD method) { |
| AV1_COMMON *const cm = &cpi->common; |
| struct loopfilter *const lf = &cm->lf; |
| int wiener_success = 0; |
| int bilateral_success = 0; |
| double cost_bilateral = DBL_MAX; |
| double cost_wiener = DBL_MAX; |
| double cost_norestore = DBL_MAX; |
| int ntiles; |
| |
| ntiles = |
| av1_get_restoration_ntiles(BILATERAL_TILESIZE, cm->width, cm->height); |
| cm->rst_info.bilateral_level = |
| (int *)aom_realloc(cm->rst_info.bilateral_level, |
| sizeof(*cm->rst_info.bilateral_level) * ntiles); |
| assert(cm->rst_info.bilateral_level != NULL); |
| |
| ntiles = av1_get_restoration_ntiles(WIENER_TILESIZE, cm->width, cm->height); |
| cm->rst_info.wiener_level = (int *)aom_realloc( |
| cm->rst_info.wiener_level, sizeof(*cm->rst_info.wiener_level) * ntiles); |
| assert(cm->rst_info.wiener_level != NULL); |
| cm->rst_info.vfilter = (int(*)[RESTORATION_HALFWIN])aom_realloc( |
| cm->rst_info.vfilter, sizeof(*cm->rst_info.vfilter) * ntiles); |
| assert(cm->rst_info.vfilter != NULL); |
| cm->rst_info.hfilter = (int(*)[RESTORATION_HALFWIN])aom_realloc( |
| cm->rst_info.hfilter, sizeof(*cm->rst_info.hfilter) * ntiles); |
| assert(cm->rst_info.hfilter != NULL); |
| |
| lf->sharpness_level = cm->frame_type == KEY_FRAME ? 0 : cpi->oxcf.sharpness; |
| |
| if (method == LPF_PICK_MINIMAL_LPF && lf->filter_level) { |
| lf->filter_level = 0; |
| cm->rst_info.restoration_type = RESTORE_NONE; |
| } else if (method >= LPF_PICK_FROM_Q) { |
| const int min_filter_level = 0; |
| const int max_filter_level = av1_get_max_filter_level(cpi); |
| const int q = av1_ac_quant(cm->base_qindex, 0, cm->bit_depth); |
| // These values were determined by linear fitting the result of the |
| // searched level, filt_guess = q * 0.316206 + 3.87252 |
| #if CONFIG_AOM_HIGHBITDEPTH |
| int filt_guess; |
| switch (cm->bit_depth) { |
| case AOM_BITS_8: |
| filt_guess = ROUND_POWER_OF_TWO(q * 20723 + 1015158, 18); |
| break; |
| case AOM_BITS_10: |
| filt_guess = ROUND_POWER_OF_TWO(q * 20723 + 4060632, 20); |
| break; |
| case AOM_BITS_12: |
| filt_guess = ROUND_POWER_OF_TWO(q * 20723 + 16242526, 22); |
| break; |
| default: |
| assert(0 && |
| "bit_depth should be AOM_BITS_8, AOM_BITS_10 " |
| "or AOM_BITS_12"); |
| return; |
| } |
| #else |
| int filt_guess = ROUND_POWER_OF_TWO(q * 20723 + 1015158, 18); |
| #endif // CONFIG_AOM_HIGHBITDEPTH |
| if (cm->frame_type == KEY_FRAME) filt_guess -= 4; |
| lf->filter_level = clamp(filt_guess, min_filter_level, max_filter_level); |
| bilateral_success = search_bilateral_level( |
| sd, cpi, lf->filter_level, method == LPF_PICK_FROM_SUBIMAGE, |
| cm->rst_info.bilateral_level, &cost_bilateral); |
| wiener_success = search_wiener_filter( |
| sd, cpi, lf->filter_level, method == LPF_PICK_FROM_SUBIMAGE, |
| cm->rst_info.vfilter, cm->rst_info.hfilter, cm->rst_info.wiener_level, |
| &cost_wiener); |
| if (cost_bilateral < cost_wiener) { |
| if (bilateral_success) |
| cm->rst_info.restoration_type = RESTORE_BILATERAL; |
| else |
| cm->rst_info.restoration_type = RESTORE_NONE; |
| } else { |
| if (wiener_success) |
| cm->rst_info.restoration_type = RESTORE_WIENER; |
| else |
| cm->rst_info.restoration_type = RESTORE_NONE; |
| } |
| } else { |
| int blf_filter_level = -1; |
| bilateral_success = search_filter_bilateral_level( |
| sd, cpi, method == LPF_PICK_FROM_SUBIMAGE, &blf_filter_level, |
| cm->rst_info.bilateral_level, &cost_bilateral); |
| lf->filter_level = av1_search_filter_level( |
| sd, cpi, method == LPF_PICK_FROM_SUBIMAGE, &cost_norestore); |
| wiener_success = search_wiener_filter( |
| sd, cpi, lf->filter_level, method == LPF_PICK_FROM_SUBIMAGE, |
| cm->rst_info.vfilter, cm->rst_info.hfilter, cm->rst_info.wiener_level, |
| &cost_wiener); |
| if (cost_bilateral < cost_wiener) { |
| lf->filter_level = blf_filter_level; |
| if (bilateral_success) |
| cm->rst_info.restoration_type = RESTORE_BILATERAL; |
| else |
| cm->rst_info.restoration_type = RESTORE_NONE; |
| } else { |
| if (wiener_success) |
| cm->rst_info.restoration_type = RESTORE_WIENER; |
| else |
| cm->rst_info.restoration_type = RESTORE_NONE; |
| } |
| // printf("[%d] Costs %g %g (%d) %g (%d)\n", cm->rst_info.restoration_type, |
| // cost_norestore, cost_bilateral, lf->filter_level, cost_wiener, |
| // wiener_success); |
| } |
| if (cm->rst_info.restoration_type != RESTORE_BILATERAL) { |
| aom_free(cm->rst_info.bilateral_level); |
| cm->rst_info.bilateral_level = NULL; |
| } |
| if (cm->rst_info.restoration_type != RESTORE_WIENER) { |
| aom_free(cm->rst_info.vfilter); |
| cm->rst_info.vfilter = NULL; |
| aom_free(cm->rst_info.hfilter); |
| cm->rst_info.hfilter = NULL; |
| aom_free(cm->rst_info.wiener_level); |
| cm->rst_info.wiener_level = NULL; |
| } |
| } |
