Speed up global motion determination
When global-motion is enabled, a considerable amount of encoder time is spent in the functions in corner_match.c. This patch optimizes those functions to be 3.5-4x as fast, leading to an end-to-end encoder speed improvement (on 20 frames of tempete_cif.y4m) of: 200kbps: ~26% faster 800kbps: ~19% faster 2800kbps: ~12% faster Change-Id: I04d3f87484c36c41eb5a1e86e814f2accbe86297
This commit is contained in:
David Barker
Debargha Mukherjee
Родитель
24f1a90441
Коммит
15338d5f40
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@ -24,11 +24,13 @@
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#define THRESHOLD_NCC 0.75
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static double compute_variance(unsigned char *im, int stride, int x, int y,
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double *mean) {
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double sum = 0.0;
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double sumsq = 0.0;
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double var;
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/* Compute var(im) * MATCH_SZ_SQ over a MATCH_SZ by MATCH_SZ window of im,
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centered at (x, y).
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*/
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static double compute_variance(unsigned char *im, int stride, int x, int y) {
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int sum = 0.0;
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int sumsq = 0.0;
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int var;
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int i, j;
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for (i = 0; i < MATCH_SZ; ++i)
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for (j = 0; j < MATCH_SZ; ++j) {
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@ -36,39 +38,45 @@ static double compute_variance(unsigned char *im, int stride, int x, int y,
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sumsq += im[(i + y - MATCH_SZ_BY2) * stride + (j + x - MATCH_SZ_BY2)] *
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im[(i + y - MATCH_SZ_BY2) * stride + (j + x - MATCH_SZ_BY2)];
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}
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var = (sumsq * MATCH_SZ_SQ - sum * sum) / (MATCH_SZ_SQ * MATCH_SZ_SQ);
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if (mean) *mean = sum / MATCH_SZ_SQ;
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return var;
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var = sumsq * MATCH_SZ_SQ - sum * sum;
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return (double)var;
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}
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/* Compute corr(im1, im2) * MATCH_SZ * stddev(im1), where the
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correlation/standard deviation are taken over MATCH_SZ by MATCH_SZ windows
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of each image, centered at (x1, y1) and (x2, y2) respectively.
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*/
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static double compute_cross_correlation(unsigned char *im1, int stride1, int x1,
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int y1, unsigned char *im2, int stride2,
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int x2, int y2) {
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double sum1 = 0;
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double sum2 = 0;
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double cross = 0;
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double corr;
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int v1, v2;
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int sum1 = 0;
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int sum2 = 0;
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int sumsq2 = 0;
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int cross = 0;
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int var2, cov;
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int i, j;
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for (i = 0; i < MATCH_SZ; ++i)
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for (j = 0; j < MATCH_SZ; ++j) {
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sum1 += im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)];
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sum2 += im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)];
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cross +=
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im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)] *
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im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)];
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v1 = im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)];
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v2 = im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)];
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sum1 += v1;
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sum2 += v2;
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sumsq2 += v2 * v2;
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cross += v1 * v2;
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}
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corr = (cross * MATCH_SZ_SQ - sum1 * sum2) / (MATCH_SZ_SQ * MATCH_SZ_SQ);
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return corr;
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var2 = sumsq2 * MATCH_SZ_SQ - sum2 * sum2;
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cov = cross * MATCH_SZ_SQ - sum1 * sum2;
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return cov / sqrt((double)var2);
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}
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static int is_eligible_point(double pointx, double pointy, int width,
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int height) {
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static int is_eligible_point(int pointx, int pointy, int width, int height) {
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return (pointx >= MATCH_SZ_BY2 && pointy >= MATCH_SZ_BY2 &&
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pointx + MATCH_SZ_BY2 < width && pointy + MATCH_SZ_BY2 < height);
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}
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static int is_eligible_distance(double point1x, double point1y, double point2x,
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double point2y, int width, int height) {
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static int is_eligible_distance(int point1x, int point1y, int point2x,
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int point2y, int width, int height) {
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const int thresh = (width < height ? height : width) >> 4;
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return ((point1x - point2x) * (point1x - point2x) +
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(point1y - point2y) * (point1y - point2y)) <= thresh * thresh;
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@ -81,32 +89,21 @@ static void improve_correspondence(unsigned char *frm, unsigned char *ref,
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int num_correspondences) {
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int i;
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for (i = 0; i < num_correspondences; ++i) {
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double template_norm =
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compute_variance(frm, frm_stride, (int)correspondences[i].x,
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(int)correspondences[i].y, NULL);
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int x, y, best_x = 0, best_y = 0;
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double best_match_ncc = 0.0;
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for (y = -SEARCH_SZ_BY2; y <= SEARCH_SZ_BY2; ++y) {
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for (x = -SEARCH_SZ_BY2; x <= SEARCH_SZ_BY2; ++x) {
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double match_ncc;
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double subimage_norm;
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if (!is_eligible_point((int)correspondences[i].rx + x,
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(int)correspondences[i].ry + y, width, height))
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if (!is_eligible_point(correspondences[i].rx + x,
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correspondences[i].ry + y, width, height))
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continue;
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if (!is_eligible_distance(
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(int)correspondences[i].x, (int)correspondences[i].y,
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(int)correspondences[i].rx + x, (int)correspondences[i].ry + y,
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width, height))
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if (!is_eligible_distance(correspondences[i].x, correspondences[i].y,
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correspondences[i].rx + x,
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correspondences[i].ry + y, width, height))
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continue;
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subimage_norm =
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compute_variance(ref, ref_stride, (int)correspondences[i].rx + x,
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(int)correspondences[i].ry + y, NULL);
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match_ncc = compute_cross_correlation(
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frm, frm_stride, (int)correspondences[i].x,
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(int)correspondences[i].y, ref, ref_stride,
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(int)correspondences[i].rx + x,
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(int)correspondences[i].ry + y) /
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sqrt(template_norm * subimage_norm);
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frm, frm_stride, correspondences[i].x, correspondences[i].y, ref,
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ref_stride, correspondences[i].rx + x, correspondences[i].ry + y);
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if (match_ncc > best_match_ncc) {
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best_match_ncc = match_ncc;
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best_y = y;
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@ -114,36 +111,25 @@ static void improve_correspondence(unsigned char *frm, unsigned char *ref,
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}
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}
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}
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correspondences[i].rx += (double)best_x;
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correspondences[i].ry += (double)best_y;
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correspondences[i].rx += best_x;
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correspondences[i].ry += best_y;
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}
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for (i = 0; i < num_correspondences; ++i) {
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double template_norm =
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compute_variance(ref, ref_stride, (int)correspondences[i].rx,
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(int)correspondences[i].ry, NULL);
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int x, y, best_x = 0, best_y = 0;
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double best_match_ncc = 0.0;
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for (y = -SEARCH_SZ_BY2; y <= SEARCH_SZ_BY2; ++y)
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for (x = -SEARCH_SZ_BY2; x <= SEARCH_SZ_BY2; ++x) {
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double match_ncc;
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double subimage_norm;
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if (!is_eligible_point((int)correspondences[i].x + x,
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(int)correspondences[i].y + y, width, height))
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if (!is_eligible_point(correspondences[i].x + x,
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correspondences[i].y + y, width, height))
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continue;
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if (!is_eligible_distance((int)correspondences[i].x + x,
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(int)correspondences[i].y + y,
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(int)correspondences[i].rx,
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(int)correspondences[i].ry, width, height))
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if (!is_eligible_distance(
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correspondences[i].x + x, correspondences[i].y + y,
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correspondences[i].rx, correspondences[i].ry, width, height))
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continue;
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subimage_norm =
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compute_variance(frm, frm_stride, (int)correspondences[i].x + x,
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(int)correspondences[i].y + y, NULL);
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match_ncc =
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compute_cross_correlation(
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frm, frm_stride, (int)correspondences[i].x + x,
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(int)correspondences[i].y + y, ref, ref_stride,
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(int)correspondences[i].rx, (int)correspondences[i].ry) /
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sqrt(template_norm * subimage_norm);
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match_ncc = compute_cross_correlation(
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ref, ref_stride, correspondences[i].rx, correspondences[i].ry, frm,
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frm_stride, correspondences[i].x + x, correspondences[i].y + y);
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if (match_ncc > best_match_ncc) {
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best_match_ncc = match_ncc;
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best_y = y;
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@ -159,7 +145,7 @@ int determine_correspondence(unsigned char *frm, int *frm_corners,
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int num_frm_corners, unsigned char *ref,
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int *ref_corners, int num_ref_corners, int width,
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int height, int frm_stride, int ref_stride,
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double *correspondence_pts) {
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int *correspondence_pts) {
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// TODO(sarahparker) Improve this to include 2-way match
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int i, j;
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Correspondence *correspondences = (Correspondence *)correspondence_pts;
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@ -171,11 +157,8 @@ int determine_correspondence(unsigned char *frm, int *frm_corners,
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if (!is_eligible_point(frm_corners[2 * i], frm_corners[2 * i + 1], width,
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height))
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continue;
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template_norm = compute_variance(frm, frm_stride, frm_corners[2 * i],
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frm_corners[2 * i + 1], NULL);
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for (j = 0; j < num_ref_corners; ++j) {
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double match_ncc;
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double subimage_norm;
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if (!is_eligible_point(ref_corners[2 * j], ref_corners[2 * j + 1], width,
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height))
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continue;
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@ -183,25 +166,24 @@ int determine_correspondence(unsigned char *frm, int *frm_corners,
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ref_corners[2 * j], ref_corners[2 * j + 1],
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width, height))
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continue;
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subimage_norm = compute_variance(ref, ref_stride, ref_corners[2 * j],
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ref_corners[2 * j + 1], NULL);
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match_ncc = compute_cross_correlation(frm, frm_stride, frm_corners[2 * i],
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frm_corners[2 * i + 1], ref,
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ref_stride, ref_corners[2 * j],
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ref_corners[2 * j + 1]) /
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sqrt(template_norm * subimage_norm);
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match_ncc = compute_cross_correlation(
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frm, frm_stride, frm_corners[2 * i], frm_corners[2 * i + 1], ref,
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ref_stride, ref_corners[2 * j], ref_corners[2 * j + 1]);
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if (match_ncc > best_match_ncc) {
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best_match_ncc = match_ncc;
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best_match_j = j;
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}
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}
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if (best_match_ncc > THRESHOLD_NCC) {
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correspondences[num_correspondences].x = (double)frm_corners[2 * i];
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correspondences[num_correspondences].y = (double)frm_corners[2 * i + 1];
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correspondences[num_correspondences].rx =
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(double)ref_corners[2 * best_match_j];
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// Note: We want to test if the best correlation is >= THRESHOLD_NCC,
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// but need to account for the normalization in compute_cross_correlation.
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template_norm = compute_variance(frm, frm_stride, frm_corners[2 * i],
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frm_corners[2 * i + 1]);
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if (best_match_ncc > THRESHOLD_NCC * sqrt(template_norm)) {
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correspondences[num_correspondences].x = frm_corners[2 * i];
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correspondences[num_correspondences].y = frm_corners[2 * i + 1];
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correspondences[num_correspondences].rx = ref_corners[2 * best_match_j];
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correspondences[num_correspondences].ry =
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(double)ref_corners[2 * best_match_j + 1];
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ref_corners[2 * best_match_j + 1];
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num_correspondences++;
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}
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}
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@ -16,14 +16,14 @@
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#include <memory.h>
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typedef struct {
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double x, y;
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double rx, ry;
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int x, y;
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int rx, ry;
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} Correspondence;
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int determine_correspondence(unsigned char *frm, int *frm_corners,
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int num_frm_corners, unsigned char *ref,
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int *ref_corners, int num_ref_corners, int width,
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int height, int frm_stride, int ref_stride,
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double *correspondence_pts);
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int *correspondence_pts);
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#endif // AV1_ENCODER_CORNER_MATCH_H_
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@ -218,7 +218,7 @@ static INLINE RansacFunc get_ransac_type(TransformationType type) {
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// computes global motion parameters by fitting a model using RANSAC
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static int compute_global_motion_params(TransformationType type,
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double *correspondences,
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int *correspondences,
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int num_correspondences, double *params,
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int *inlier_map) {
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int result;
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@ -259,7 +259,7 @@ int compute_global_motion_feature_based(TransformationType type,
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double *params) {
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int num_frm_corners, num_ref_corners;
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int num_correspondences;
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double *correspondences;
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int *correspondences;
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int num_inliers;
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int frm_corners[2 * MAX_CORNERS], ref_corners[2 * MAX_CORNERS];
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int *inlier_map = NULL;
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@ -291,7 +291,7 @@ int compute_global_motion_feature_based(TransformationType type,
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// find correspondences between the two images
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correspondences =
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(double *)malloc(num_frm_corners * 4 * sizeof(*correspondences));
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(int *)malloc(num_frm_corners * 4 * sizeof(*correspondences));
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num_correspondences = determine_correspondence(
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frm_buffer, (int *)frm_corners, num_frm_corners, ref_buffer,
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(int *)ref_corners, num_ref_corners, frm->y_width, frm->y_height,
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@ -116,7 +116,7 @@ static int get_rand_indices(int npoints, int minpts, int *indices,
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return 1;
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}
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static int ransac(double *matched_points, int npoints, int *number_of_inliers,
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static int ransac(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_mask, double *best_params, const int minpts,
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IsDegenerateFunc is_degenerate,
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FindTransformationFunc find_transformation,
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@ -305,31 +305,29 @@ static int is_degenerate_homography(double *p) {
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is_collinear3(p, p + 4, p + 6) || is_collinear3(p + 2, p + 4, p + 6);
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}
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int ransac_translation(double *matched_points, int npoints,
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int *number_of_inliers, int *best_inlier_mask,
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double *best_params) {
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int ransac_translation(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_mask, double *best_params) {
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return ransac(matched_points, npoints, number_of_inliers, best_inlier_mask,
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best_params, 3, is_degenerate_translation, find_translation,
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project_points_double_translation);
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}
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int ransac_rotzoom(double *matched_points, int npoints, int *number_of_inliers,
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int ransac_rotzoom(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_mask, double *best_params) {
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return ransac(matched_points, npoints, number_of_inliers, best_inlier_mask,
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best_params, 3, is_degenerate_affine, find_rotzoom,
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project_points_double_rotzoom);
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}
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int ransac_affine(double *matched_points, int npoints, int *number_of_inliers,
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int ransac_affine(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_mask, double *best_params) {
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return ransac(matched_points, npoints, number_of_inliers, best_inlier_mask,
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best_params, 3, is_degenerate_affine, find_affine,
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project_points_double_affine);
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}
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int ransac_homography(double *matched_points, int npoints,
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int *number_of_inliers, int *best_inlier_mask,
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double *best_params) {
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int ransac_homography(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_mask, double *best_params) {
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return ransac(matched_points, npoints, number_of_inliers, best_inlier_mask,
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best_params, 4, is_degenerate_homography, find_homography,
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project_points_double_homography);
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|
|
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@ -19,20 +19,18 @@
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#include "av1/common/warped_motion.h"
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typedef int (*RansacFunc)(double *matched_points, int npoints,
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typedef int (*RansacFunc)(int *matched_points, int npoints,
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int *number_of_inliers, int *best_inlier_mask,
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double *best_params);
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/* Each of these functions fits a motion model from a set of
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corresponding points in 2 frames using RANSAC.*/
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int ransac_homography(double *matched_points, int npoints,
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int *number_of_inliers, int *best_inlier_indices,
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double *best_params);
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int ransac_affine(double *matched_points, int npoints, int *number_of_inliers,
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int ransac_homography(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_indices, double *best_params);
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int ransac_rotzoom(double *matched_points, int npoints, int *number_of_inliers,
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int ransac_affine(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_indices, double *best_params);
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int ransac_rotzoom(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_indices, double *best_params);
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int ransac_translation(int *matched_points, int npoints, int *number_of_inliers,
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int *best_inlier_indices, double *best_params);
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int ransac_translation(double *matched_points, int npoints,
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int *number_of_inliers, int *best_inlier_indices,
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double *best_params);
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#endif // AV1_ENCODER_RANSAC_H_
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