ref: becab42eee06e945392d841ef49296396a501fb3
parent: 2eddfb46a999badf6fa9561606f62b73bf4eccb3
author: Jim Bankoski <[email protected]>
date: Mon Nov 13 01:44:17 EST 2017
add 10 and 12 bit to tiny_ssim Change-Id: I92e4dba2d1682a0d77ad9a214ec4312b1cf4d42e
--- a/tools.mk
+++ b/tools.mk
@@ -13,6 +13,8 @@
tiny_ssim.SRCS += vpx/vpx_integer.h y4minput.c y4minput.h \
vpx/vpx_codec.h vpx/src/vpx_image.c
tiny_ssim.SRCS += vpx_mem/vpx_mem.c vpx_mem/vpx_mem.h
+tiny_ssim.SRCS += vpx_dsp/ssim.h vpx_scale/yv12config.h
+tiny_ssim.SRCS += vpx_ports/mem.h vpx_ports/mem.h
tiny_ssim.SRCS += vpx_mem/include/vpx_mem_intrnl.h
tiny_ssim.GUID = 3afa9b05-940b-4d68-b5aa-55157d8ed7b4
tiny_ssim.DESCRIPTION = Generate SSIM/PSNR from raw .yuv files
--- a/tools/tiny_ssim.c
+++ b/tools/tiny_ssim.c
@@ -8,6 +8,7 @@
* be found in the AUTHORS file in the root of the source tree.
*/
+#include <assert.h>
#include <errno.h>
#include <math.h>
#include <stdio.h>
@@ -16,72 +17,36 @@
#include "vpx/vpx_codec.h"
#include "vpx/vpx_integer.h"
#include "./y4minput.h"
+#include "vpx_dsp/ssim.h"
+#include "vpx_ports/mem.h"
-static void ssim_parms_8x8(unsigned char *s, int sp, unsigned char *r, int rp,
- uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s,
- uint32_t *sum_sq_r, uint32_t *sum_sxr) {- int i, j;
- for (i = 0; i < 8; i++, s += sp, r += rp) {- for (j = 0; j < 8; j++) {- *sum_s += s[j];
- *sum_r += r[j];
- *sum_sq_s += s[j] * s[j];
- *sum_sq_r += r[j] * r[j];
- *sum_sxr += s[j] * r[j];
- }
- }
-}
+static const int64_t cc1 = 26634; // (64^2*(.01*255)^2
+static const int64_t cc2 = 239708; // (64^2*(.03*255)^2
+static const int64_t cc1_10 = 428658; // (64^2*(.01*1023)^2
+static const int64_t cc2_10 = 3857925; // (64^2*(.03*1023)^2
+static const int64_t cc1_12 = 6868593; // (64^2*(.01*4095)^2
+static const int64_t cc2_12 = 61817334; // (64^2*(.03*4095)^2
-static const int64_t cc1 = 26634; // (64^2*(.01*255)^2
-static const int64_t cc2 = 239708; // (64^2*(.03*255)^2
+#if CONFIG_VP9_HIGHBITDEPTH
+static uint64_t calc_plane_error16(uint16_t *orig, int orig_stride,
+ uint16_t *recon, int recon_stride,
+ unsigned int cols, unsigned int rows) {+ unsigned int row, col;
+ uint64_t total_sse = 0;
+ int diff;
-static double similarity(uint32_t sum_s, uint32_t sum_r, uint32_t sum_sq_s,
- uint32_t sum_sq_r, uint32_t sum_sxr, int count) {- int64_t ssim_n, ssim_d;
- int64_t c1, c2;
-
- // scale the constants by number of pixels
- c1 = (cc1 * count * count) >> 12;
- c2 = (cc2 * count * count) >> 12;
-
- ssim_n = (2 * sum_s * sum_r + c1) *
- ((int64_t)2 * count * sum_sxr - (int64_t)2 * sum_s * sum_r + c2);
-
- ssim_d = (sum_s * sum_s + sum_r * sum_r + c1) *
- ((int64_t)count * sum_sq_s - (int64_t)sum_s * sum_s +
- (int64_t)count * sum_sq_r - (int64_t)sum_r * sum_r + c2);
-
- return ssim_n * 1.0 / ssim_d;
-}
-
-static double ssim_8x8(unsigned char *s, int sp, unsigned char *r, int rp) {- uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0;
- ssim_parms_8x8(s, sp, r, rp, &sum_s, &sum_r, &sum_sq_s, &sum_sq_r, &sum_sxr);
- return similarity(sum_s, sum_r, sum_sq_s, sum_sq_r, sum_sxr, 64);
-}
-
-// We are using a 8x8 moving window with starting location of each 8x8 window
-// on the 4x4 pixel grid. Such arrangement allows the windows to overlap
-// block boundaries to penalize blocking artifacts.
-static double ssim2(unsigned char *img1, unsigned char *img2, int stride_img1,
- int stride_img2, int width, int height) {- int i, j;
- int samples = 0;
- double ssim_total = 0;
-
- // sample point start with each 4x4 location
- for (i = 0; i <= height - 8;
- i += 4, img1 += stride_img1 * 4, img2 += stride_img2 * 4) {- for (j = 0; j <= width - 8; j += 4) {- double v = ssim_8x8(img1 + j, stride_img1, img2 + j, stride_img2);
- ssim_total += v;
- samples++;
+ for (row = 0; row < rows; row++) {+ for (col = 0; col < cols; col++) {+ diff = orig[col] - recon[col];
+ total_sse += diff * diff;
}
+
+ orig += orig_stride;
+ recon += recon_stride;
}
- ssim_total /= samples;
- return ssim_total;
+ return total_sse;
}
-
+#endif
static uint64_t calc_plane_error(uint8_t *orig, int orig_stride, uint8_t *recon,
int recon_stride, unsigned int cols,
unsigned int rows) {@@ -125,11 +90,12 @@
vpx_image_t img;
int w;
int h;
+ int bit_depth;
} input_file_t;
// Open a file and determine if its y4m or raw. If y4m get the header.
static int open_input_file(const char *file_name, input_file_t *input, int w,
- int h) {+ int h, int bit_depth) {char y4m_buf[4];
size_t r1;
input->type = RAW_YUV;
@@ -144,6 +110,7 @@
y4m_input_open(&input->y4m, input->file, y4m_buf, 4, 0);
input->w = input->y4m.pic_w;
input->h = input->y4m.pic_h;
+ input->bit_depth = input->y4m.bit_depth;
// Y4M alloc's its own buf. Init this to avoid problems if we never
// read frames.
memset(&input->img, 0, sizeof(input->img));
@@ -152,7 +119,10 @@
fseek(input->file, 0, SEEK_SET);
input->w = w;
input->h = h;
- input->buf = malloc(w * h * 3 / 2);
+ if (bit_depth < 9)
+ input->buf = malloc(w * h * 3 / 2);
+ else
+ input->buf = malloc(w * h * 3);
break;
}
}
@@ -169,7 +139,7 @@
}
static size_t read_input_file(input_file_t *in, unsigned char **y,
- unsigned char **u, unsigned char **v) {+ unsigned char **u, unsigned char **v, int bd) {size_t r1 = 0;
switch (in->type) {case Y4M:
@@ -179,10 +149,17 @@
*v = in->img.planes[2];
break;
case RAW_YUV:
- r1 = fread(in->buf, in->w * in->h * 3 / 2, 1, in->file);
- *y = in->buf;
- *u = in->buf + in->w * in->h;
- *v = in->buf + 5 * in->w * in->h / 4;
+ if (bd < 9) {+ r1 = fread(in->buf, in->w * in->h * 3 / 2, 1, in->file);
+ *y = in->buf;
+ *u = in->buf + in->w * in->h;
+ *v = in->buf + 5 * in->w * in->h / 4;
+ } else {+ r1 = fread(in->buf, in->w * in->h * 3, 1, in->file);
+ *y = in->buf;
+ *u = in->buf + in->w * in->h / 2;
+ *v = *u + in->w * in->h / 2;
+ }
break;
}
@@ -189,8 +166,412 @@
return r1;
}
+void ssim_parms_16x16(const uint8_t *s, int sp, const uint8_t *r, int rp,
+ uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s,
+ uint32_t *sum_sq_r, uint32_t *sum_sxr) {+ int i, j;
+ for (i = 0; i < 16; i++, s += sp, r += rp) {+ for (j = 0; j < 16; j++) {+ *sum_s += s[j];
+ *sum_r += r[j];
+ *sum_sq_s += s[j] * s[j];
+ *sum_sq_r += r[j] * r[j];
+ *sum_sxr += s[j] * r[j];
+ }
+ }
+}
+void ssim_parms_8x8(const uint8_t *s, int sp, const uint8_t *r, int rp,
+ uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s,
+ uint32_t *sum_sq_r, uint32_t *sum_sxr) {+ int i, j;
+ for (i = 0; i < 8; i++, s += sp, r += rp) {+ for (j = 0; j < 8; j++) {+ *sum_s += s[j];
+ *sum_r += r[j];
+ *sum_sq_s += s[j] * s[j];
+ *sum_sq_r += r[j] * r[j];
+ *sum_sxr += s[j] * r[j];
+ }
+ }
+}
+
+void highbd_ssim_parms_8x8(const uint16_t *s, int sp, const uint16_t *r, int rp,
+ uint32_t *sum_s, uint32_t *sum_r, uint32_t *sum_sq_s,
+ uint32_t *sum_sq_r, uint32_t *sum_sxr) {+ int i, j;
+ for (i = 0; i < 8; i++, s += sp, r += rp) {+ for (j = 0; j < 8; j++) {+ *sum_s += s[j];
+ *sum_r += r[j];
+ *sum_sq_s += s[j] * s[j];
+ *sum_sq_r += r[j] * r[j];
+ *sum_sxr += s[j] * r[j];
+ }
+ }
+}
+
+static double similarity(uint32_t sum_s, uint32_t sum_r, uint32_t sum_sq_s,
+ uint32_t sum_sq_r, uint32_t sum_sxr, int count,
+ uint32_t bd) {+ int64_t ssim_n, ssim_d;
+ int64_t c1 = 0, c2 = 0;
+ if (bd == 8) {+ // scale the constants by number of pixels
+ c1 = (cc1 * count * count) >> 12;
+ c2 = (cc2 * count * count) >> 12;
+ } else if (bd == 10) {+ c1 = (cc1_10 * count * count) >> 12;
+ c2 = (cc2_10 * count * count) >> 12;
+ } else if (bd == 12) {+ c1 = (cc1_12 * count * count) >> 12;
+ c2 = (cc2_12 * count * count) >> 12;
+ } else {+ assert(0);
+ }
+
+ ssim_n = (2 * sum_s * sum_r + c1) *
+ ((int64_t)2 * count * sum_sxr - (int64_t)2 * sum_s * sum_r + c2);
+
+ ssim_d = (sum_s * sum_s + sum_r * sum_r + c1) *
+ ((int64_t)count * sum_sq_s - (int64_t)sum_s * sum_s +
+ (int64_t)count * sum_sq_r - (int64_t)sum_r * sum_r + c2);
+
+ return ssim_n * 1.0 / ssim_d;
+}
+
+static double ssim_8x8(const uint8_t *s, int sp, const uint8_t *r, int rp) {+ uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0;
+ ssim_parms_8x8(s, sp, r, rp, &sum_s, &sum_r, &sum_sq_s, &sum_sq_r, &sum_sxr);
+ return similarity(sum_s, sum_r, sum_sq_s, sum_sq_r, sum_sxr, 64, 8);
+}
+
+static double highbd_ssim_8x8(const uint16_t *s, int sp, const uint16_t *r,
+ int rp, uint32_t bd, uint32_t shift) {+ uint32_t sum_s = 0, sum_r = 0, sum_sq_s = 0, sum_sq_r = 0, sum_sxr = 0;
+ highbd_ssim_parms_8x8(s, sp, r, rp, &sum_s, &sum_r, &sum_sq_s, &sum_sq_r,
+ &sum_sxr);
+ return similarity(sum_s >> shift, sum_r >> shift, sum_sq_s >> (2 * shift),
+ sum_sq_r >> (2 * shift), sum_sxr >> (2 * shift), 64, bd);
+}
+
+// We are using a 8x8 moving window with starting location of each 8x8 window
+// on the 4x4 pixel grid. Such arrangement allows the windows to overlap
+// block boundaries to penalize blocking artifacts.
+static double ssim2(const uint8_t *img1, const uint8_t *img2, int stride_img1,
+ int stride_img2, int width, int height) {+ int i, j;
+ int samples = 0;
+ double ssim_total = 0;
+
+ // sample point start with each 4x4 location
+ for (i = 0; i <= height - 8;
+ i += 4, img1 += stride_img1 * 4, img2 += stride_img2 * 4) {+ for (j = 0; j <= width - 8; j += 4) {+ double v = ssim_8x8(img1 + j, stride_img1, img2 + j, stride_img2);
+ ssim_total += v;
+ samples++;
+ }
+ }
+ ssim_total /= samples;
+ return ssim_total;
+}
+
+static double highbd_ssim2(const uint8_t *img1, const uint8_t *img2,
+ int stride_img1, int stride_img2, int width,
+ int height, uint32_t bd, uint32_t shift) {+ int i, j;
+ int samples = 0;
+ double ssim_total = 0;
+
+ // sample point start with each 4x4 location
+ for (i = 0; i <= height - 8;
+ i += 4, img1 += stride_img1 * 4, img2 += stride_img2 * 4) {+ for (j = 0; j <= width - 8; j += 4) {+ double v = highbd_ssim_8x8(CONVERT_TO_SHORTPTR(img1 + j), stride_img1,
+ CONVERT_TO_SHORTPTR(img2 + j), stride_img2, bd,
+ shift);
+ ssim_total += v;
+ samples++;
+ }
+ }
+ ssim_total /= samples;
+ return ssim_total;
+}
+
+// traditional ssim as per: http://en.wikipedia.org/wiki/Structural_similarity
+//
+// Re working out the math ->
+//
+// ssim(x,y) = (2*mean(x)*mean(y) + c1)*(2*cov(x,y)+c2) /
+// ((mean(x)^2+mean(y)^2+c1)*(var(x)+var(y)+c2))
+//
+// mean(x) = sum(x) / n
+//
+// cov(x,y) = (n*sum(xi*yi)-sum(x)*sum(y))/(n*n)
+//
+// var(x) = (n*sum(xi*xi)-sum(xi)*sum(xi))/(n*n)
+//
+// ssim(x,y) =
+// (2*sum(x)*sum(y)/(n*n) + c1)*(2*(n*sum(xi*yi)-sum(x)*sum(y))/(n*n)+c2) /
+// (((sum(x)*sum(x)+sum(y)*sum(y))/(n*n) +c1) *
+// ((n*sum(xi*xi) - sum(xi)*sum(xi))/(n*n)+
+// (n*sum(yi*yi) - sum(yi)*sum(yi))/(n*n)+c2)))
+//
+// factoring out n*n
+//
+// ssim(x,y) =
+// (2*sum(x)*sum(y) + n*n*c1)*(2*(n*sum(xi*yi)-sum(x)*sum(y))+n*n*c2) /
+// (((sum(x)*sum(x)+sum(y)*sum(y)) + n*n*c1) *
+// (n*sum(xi*xi)-sum(xi)*sum(xi)+n*sum(yi*yi)-sum(yi)*sum(yi)+n*n*c2))
+//
+// Replace c1 with n*n * c1 for the final step that leads to this code:
+// The final step scales by 12 bits so we don't lose precision in the constants.
+
+static double ssimv_similarity(const Ssimv *sv, int64_t n) {+ // Scale the constants by number of pixels.
+ const int64_t c1 = (cc1 * n * n) >> 12;
+ const int64_t c2 = (cc2 * n * n) >> 12;
+
+ const double l = 1.0 * (2 * sv->sum_s * sv->sum_r + c1) /
+ (sv->sum_s * sv->sum_s + sv->sum_r * sv->sum_r + c1);
+
+ // Since these variables are unsigned sums, convert to double so
+ // math is done in double arithmetic.
+ const double v = (2.0 * n * sv->sum_sxr - 2 * sv->sum_s * sv->sum_r + c2) /
+ (n * sv->sum_sq_s - sv->sum_s * sv->sum_s +
+ n * sv->sum_sq_r - sv->sum_r * sv->sum_r + c2);
+
+ return l * v;
+}
+
+// The first term of the ssim metric is a luminance factor.
+//
+// (2*mean(x)*mean(y) + c1)/ (mean(x)^2+mean(y)^2+c1)
+//
+// This luminance factor is super sensitive to the dark side of luminance
+// values and completely insensitive on the white side. check out 2 sets
+// (1,3) and (250,252) the term gives ( 2*1*3/(1+9) = .60
+// 2*250*252/ (250^2+252^2) => .99999997
+//
+// As a result in this tweaked version of the calculation in which the
+// luminance is taken as percentage off from peak possible.
+//
+// 255 * 255 - (sum_s - sum_r) / count * (sum_s - sum_r) / count
+//
+static double ssimv_similarity2(const Ssimv *sv, int64_t n) {+ // Scale the constants by number of pixels.
+ const int64_t c1 = (cc1 * n * n) >> 12;
+ const int64_t c2 = (cc2 * n * n) >> 12;
+
+ const double mean_diff = (1.0 * sv->sum_s - sv->sum_r) / n;
+ const double l = (255 * 255 - mean_diff * mean_diff + c1) / (255 * 255 + c1);
+
+ // Since these variables are unsigned, sums convert to double so
+ // math is done in double arithmetic.
+ const double v = (2.0 * n * sv->sum_sxr - 2 * sv->sum_s * sv->sum_r + c2) /
+ (n * sv->sum_sq_s - sv->sum_s * sv->sum_s +
+ n * sv->sum_sq_r - sv->sum_r * sv->sum_r + c2);
+
+ return l * v;
+}
+static void ssimv_parms(uint8_t *img1, int img1_pitch, uint8_t *img2,
+ int img2_pitch, Ssimv *sv) {+ ssim_parms_8x8(img1, img1_pitch, img2, img2_pitch, &sv->sum_s, &sv->sum_r,
+ &sv->sum_sq_s, &sv->sum_sq_r, &sv->sum_sxr);
+}
+
+double get_ssim_metrics(uint8_t *img1, int img1_pitch, uint8_t *img2,
+ int img2_pitch, int width, int height, Ssimv *sv2,
+ Metrics *m, int do_inconsistency) {+ double dssim_total = 0;
+ double ssim_total = 0;
+ double ssim2_total = 0;
+ double inconsistency_total = 0;
+ int i, j;
+ int c = 0;
+ double norm;
+ double old_ssim_total = 0;
+
+ // We can sample points as frequently as we like start with 1 per 4x4.
+ for (i = 0; i < height;
+ i += 4, img1 += img1_pitch * 4, img2 += img2_pitch * 4) {+ for (j = 0; j < width; j += 4, ++c) {+ Ssimv sv = { 0 };+ double ssim;
+ double ssim2;
+ double dssim;
+ uint32_t var_new;
+ uint32_t var_old;
+ uint32_t mean_new;
+ uint32_t mean_old;
+ double ssim_new;
+ double ssim_old;
+
+ // Not sure there's a great way to handle the edge pixels
+ // in ssim when using a window. Seems biased against edge pixels
+ // however you handle this. This uses only samples that are
+ // fully in the frame.
+ if (j + 8 <= width && i + 8 <= height) {+ ssimv_parms(img1 + j, img1_pitch, img2 + j, img2_pitch, &sv);
+ }
+
+ ssim = ssimv_similarity(&sv, 64);
+ ssim2 = ssimv_similarity2(&sv, 64);
+
+ sv.ssim = ssim2;
+
+ // dssim is calculated to use as an actual error metric and
+ // is scaled up to the same range as sum square error.
+ // Since we are subsampling every 16th point maybe this should be
+ // *16 ?
+ dssim = 255 * 255 * (1 - ssim2) / 2;
+
+ // Here I introduce a new error metric: consistency-weighted
+ // SSIM-inconsistency. This metric isolates frames where the
+ // SSIM 'suddenly' changes, e.g. if one frame in every 8 is much
+ // sharper or blurrier than the others. Higher values indicate a
+ // temporally inconsistent SSIM. There are two ideas at work:
+ //
+ // 1) 'SSIM-inconsistency': the total inconsistency value
+ // reflects how much SSIM values are changing between this
+ // source / reference frame pair and the previous pair.
+ //
+ // 2) 'consistency-weighted': weights de-emphasize areas in the
+ // frame where the scene content has changed. Changes in scene
+ // content are detected via changes in local variance and local
+ // mean.
+ //
+ // Thus the overall measure reflects how inconsistent the SSIM
+ // values are, over consistent regions of the frame.
+ //
+ // The metric has three terms:
+ //
+ // term 1 -> uses change in scene Variance to weight error score
+ // 2 * var(Fi)*var(Fi-1) / (var(Fi)^2+var(Fi-1)^2)
+ // larger changes from one frame to the next mean we care
+ // less about consistency.
+ //
+ // term 2 -> uses change in local scene luminance to weight error
+ // 2 * avg(Fi)*avg(Fi-1) / (avg(Fi)^2+avg(Fi-1)^2)
+ // larger changes from one frame to the next mean we care
+ // less about consistency.
+ //
+ // term3 -> measures inconsistency in ssim scores between frames
+ // 1 - ( 2 * ssim(Fi)*ssim(Fi-1)/(ssim(Fi)^2+sssim(Fi-1)^2).
+ //
+ // This term compares the ssim score for the same location in 2
+ // subsequent frames.
+ var_new = sv.sum_sq_s - sv.sum_s * sv.sum_s / 64;
+ var_old = sv2[c].sum_sq_s - sv2[c].sum_s * sv2[c].sum_s / 64;
+ mean_new = sv.sum_s;
+ mean_old = sv2[c].sum_s;
+ ssim_new = sv.ssim;
+ ssim_old = sv2[c].ssim;
+
+ if (do_inconsistency) {+ // We do the metric once for every 4x4 block in the image. Since
+ // we are scaling the error to SSE for use in a psnr calculation
+ // 1.0 = 4x4x255x255 the worst error we can possibly have.
+ static const double kScaling = 4. * 4 * 255 * 255;
+
+ // The constants have to be non 0 to avoid potential divide by 0
+ // issues other than that they affect kind of a weighting between
+ // the terms. No testing of what the right terms should be has been
+ // done.
+ static const double c1 = 1, c2 = 1, c3 = 1;
+
+ // This measures how much consistent variance is in two consecutive
+ // source frames. 1.0 means they have exactly the same variance.
+ const double variance_term =
+ (2.0 * var_old * var_new + c1) /
+ (1.0 * var_old * var_old + 1.0 * var_new * var_new + c1);
+
+ // This measures how consistent the local mean are between two
+ // consecutive frames. 1.0 means they have exactly the same mean.
+ const double mean_term =
+ (2.0 * mean_old * mean_new + c2) /
+ (1.0 * mean_old * mean_old + 1.0 * mean_new * mean_new + c2);
+
+ // This measures how consistent the ssims of two
+ // consecutive frames is. 1.0 means they are exactly the same.
+ double ssim_term =
+ pow((2.0 * ssim_old * ssim_new + c3) /
+ (ssim_old * ssim_old + ssim_new * ssim_new + c3),
+ 5);
+
+ double this_inconsistency;
+
+ // Floating point math sometimes makes this > 1 by a tiny bit.
+ // We want the metric to scale between 0 and 1.0 so we can convert
+ // it to an snr scaled value.
+ if (ssim_term > 1) ssim_term = 1;
+
+ // This converts the consistency metric to an inconsistency metric
+ // ( so we can scale it like psnr to something like sum square error.
+ // The reason for the variance and mean terms is the assumption that
+ // if there are big changes in the source we shouldn't penalize
+ // inconsistency in ssim scores a bit less as it will be less visible
+ // to the user.
+ this_inconsistency = (1 - ssim_term) * variance_term * mean_term;
+
+ this_inconsistency *= kScaling;
+ inconsistency_total += this_inconsistency;
+ }
+ sv2[c] = sv;
+ ssim_total += ssim;
+ ssim2_total += ssim2;
+ dssim_total += dssim;
+
+ old_ssim_total += ssim_old;
+ }
+ old_ssim_total += 0;
+ }
+
+ norm = 1. / (width / 4) / (height / 4);
+ ssim_total *= norm;
+ ssim2_total *= norm;
+ m->ssim2 = ssim2_total;
+ m->ssim = ssim_total;
+ if (old_ssim_total == 0) inconsistency_total = 0;
+
+ m->ssimc = inconsistency_total;
+
+ m->dssim = dssim_total;
+ return inconsistency_total;
+}
+
+double highbd_calc_ssim(const YV12_BUFFER_CONFIG *source,
+ const YV12_BUFFER_CONFIG *dest, double *weight,
+ uint32_t bd, uint32_t in_bd) {+ double a, b, c;
+ double ssimv;
+ uint32_t shift = 0;
+
+ assert(bd >= in_bd);
+ shift = bd - in_bd;
+
+ a = highbd_ssim2(source->y_buffer, dest->y_buffer, source->y_stride,
+ dest->y_stride, source->y_crop_width, source->y_crop_height,
+ in_bd, shift);
+
+ b = highbd_ssim2(source->u_buffer, dest->u_buffer, source->uv_stride,
+ dest->uv_stride, source->uv_crop_width,
+ source->uv_crop_height, in_bd, shift);
+
+ c = highbd_ssim2(source->v_buffer, dest->v_buffer, source->uv_stride,
+ dest->uv_stride, source->uv_crop_width,
+ source->uv_crop_height, in_bd, shift);
+
+ ssimv = a * .8 + .1 * (b + c);
+
+ *weight = 1;
+
+ return ssimv;
+}
+
int main(int argc, char *argv[]) {FILE *framestats = NULL;
+ int bit_depth = 8;
int w = 0, h = 0, tl_skip = 0, tl_skips_remaining = 0;
double ssimavg = 0, ssimyavg = 0, ssimuavg = 0, ssimvavg = 0;
double psnrglb = 0, psnryglb = 0, psnruglb = 0, psnrvglb = 0;
@@ -200,11 +581,12 @@
size_t i, n_frames = 0, allocated_frames = 0;
int return_value = 0;
input_file_t in[2];
+ double peak = 255.0;
if (argc < 2) {fprintf(stderr,
"Usage: %s file1.{yuv|y4m} file2.{yuv|y4m}"- "[WxH tl_skip={0,1,3}]\n",+ "[WxH tl_skip={0,1,3} frame_stats_file bits]\n",argv[0]);
return_value = 1;
goto clean_up;
@@ -214,7 +596,11 @@
sscanf(argv[3], "%dx%d", &w, &h);
}
- if (open_input_file(argv[1], &in[0], w, h) < 0) {+ if (argc > 6) {+ sscanf(argv[6], "%d", &bit_depth);
+ }
+
+ if (open_input_file(argv[1], &in[0], w, h, bit_depth) < 0) {fprintf(stderr, "File %s can't be opened or parsed!\n", argv[2]);
goto clean_up;
}
@@ -223,9 +609,13 @@
// If a y4m is the first file and w, h is not set grab from first file.
w = in[0].w;
h = in[0].h;
+ bit_depth = in[0].bit_depth;
}
+ if (bit_depth == 10) peak = 1023.0;
- if (open_input_file(argv[2], &in[1], w, h) < 0) {+ if (bit_depth == 12) peak = 4095;
+
+ if (open_input_file(argv[2], &in[1], w, h, bit_depth) < 0) {fprintf(stderr, "File %s can't be opened or parsed!\n", argv[2]);
goto clean_up;
}
@@ -264,7 +654,7 @@
size_t r1, r2;
unsigned char *y[2], *u[2], *v[2];
- r1 = read_input_file(&in[0], &y[0], &u[0], &v[0]);
+ r1 = read_input_file(&in[0], &y[0], &u[0], &v[0], bit_depth);
if (r1) {// Reading parts of file1.yuv that were not used in temporal layer.
@@ -276,7 +666,7 @@
tl_skips_remaining = tl_skip;
}
- r2 = read_input_file(&in[1], &y[1], &u[1], &v[1]);
+ r2 = read_input_file(&in[1], &y[1], &u[1], &v[1], bit_depth);
if (r1 && r2 && r1 != r2) {fprintf(stderr, "Failed to read data: %s [%d/%d]\n", strerror(errno),
@@ -286,9 +676,22 @@
} else if (r1 == 0 || r2 == 0) {break;
}
+#if CONFIG_VP9_HIGHBITDEPTH
+#define psnr_and_ssim(ssim, psnr, buf0, buf1, w, h) \
+ if (bit_depth < 9) { \+ ssim = ssim2(buf0, buf1, w, w, w, h); \
+ psnr = calc_plane_error(buf0, w, buf1, w, w, h); \
+ } else { \+ ssim = highbd_ssim2(CONVERT_TO_BYTEPTR(buf0), CONVERT_TO_BYTEPTR(buf1), w, \
+ w, w, h, bit_depth, bit_depth - 8); \
+ psnr = calc_plane_error16(CAST_TO_SHORTPTR(buf0), w, \
+ CAST_TO_SHORTPTR(buf1), w, w, h); \
+ }
+#else
#define psnr_and_ssim(ssim, psnr, buf0, buf1, w, h) \
ssim = ssim2(buf0, buf1, w, w, w, h); \
psnr = calc_plane_error(buf0, w, buf1, w, w, h);
+#endif
if (n_frames == allocated_frames) {allocated_frames = allocated_frames == 0 ? 1024 : allocated_frames * 2;
@@ -322,10 +725,10 @@
ssimvavg += ssimv[i];
frame_psnr =
- mse2psnr(w * h * 6 / 4, 255.0, (double)psnry[i] + psnru[i] + psnrv[i]);
- frame_psnry = mse2psnr(w * h * 4 / 4, 255.0, (double)psnry[i]);
- frame_psnru = mse2psnr(w * h * 1 / 4, 255.0, (double)psnru[i]);
- frame_psnrv = mse2psnr(w * h * 1 / 4, 255.0, (double)psnrv[i]);
+ mse2psnr(w * h * 6 / 4, peak, (double)psnry[i] + psnru[i] + psnrv[i]);
+ frame_psnry = mse2psnr(w * h * 4 / 4, peak, (double)psnry[i]);
+ frame_psnru = mse2psnr(w * h * 1 / 4, peak, (double)psnru[i]);
+ frame_psnrv = mse2psnr(w * h * 1 / 4, peak, (double)psnrv[i]);
psnravg += frame_psnr;
psnryavg += frame_psnry;
@@ -367,10 +770,10 @@
puts("");psnrglb = psnryglb + psnruglb + psnrvglb;
- psnrglb = mse2psnr((double)n_frames * w * h * 6 / 4, 255.0, psnrglb);
- psnryglb = mse2psnr((double)n_frames * w * h * 4 / 4, 255.0, psnryglb);
- psnruglb = mse2psnr((double)n_frames * w * h * 1 / 4, 255.0, psnruglb);
- psnrvglb = mse2psnr((double)n_frames * w * h * 1 / 4, 255.0, psnrvglb);
+ psnrglb = mse2psnr((double)n_frames * w * h * 6 / 4, peak, psnrglb);
+ psnryglb = mse2psnr((double)n_frames * w * h * 4 / 4, peak, psnryglb);
+ psnruglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnruglb);
+ psnrvglb = mse2psnr((double)n_frames * w * h * 1 / 4, peak, psnrvglb);
printf("GlbPSNR: %lf\n", psnrglb); printf("GlbPSNR-Y: %lf\n", psnryglb);