Commit 7947a33d by Deb Mukherjee Committed by Gerrit Code Review

### Improving model rd with variance and quant step

```Improves the rd modeling function and implements them using interpolation
from a table which is a little faster. Also uses sse as input to the
modeling function rather than var - since there is no dc prediction
used and as a result the sse works a little better.

derfraw300: +0.05%
Speedup: ~1%

Change-Id: I151353c6451e0e8fe3ae18ab9842f8f67e5151ff```
parent 9f2a1ae2
 ... ... @@ -1800,18 +1800,133 @@ static YV12_BUFFER_CONFIG *get_scaled_ref_frame(VP9_COMP *cpi, int ref_frame) { return scaled_ref_frame; } static void model_rd_from_var_lapndz(int var, int n, int qstep, int *rate, int *dist) { // This function models the rate and distortion for a Laplacian static double linear_interpolate(double x, int ntab, double step, const double *tab) { double y = x / step; int d = (int) y; double a = y - d; if (d >= ntab - 1) return tab[ntab - 1]; else return tab[d] * (1 - a) + tab[d + 1] * a; } static double model_rate_norm(double x) { // Normalized rate // This function models the rate for a Laplacian source // source with given variance when quantized with a uniform quantizer // with given stepsize. The closed form expressions are in: // Hang and Chen, "Source Model for transform video coder and its // application - Part I: Fundamental Theory", IEEE Trans. Circ. // Sys. for Video Tech., April 1997. // The function is implemented as piecewise approximation to the // exact computation. // TODO(debargha): Implement the functions by interpolating from a // look-up table static const double rate_tab_step = 0.125; static const double rate_tab[] = { 256.0000, 4.944453, 3.949276, 3.371593, 2.965771, 2.654550, 2.403348, 2.193612, 2.014208, 1.857921, 1.719813, 1.596364, 1.484979, 1.383702, 1.291025, 1.205767, 1.126990, 1.053937, 0.985991, 0.922644, 0.863472, 0.808114, 0.756265, 0.707661, 0.662070, 0.619287, 0.579129, 0.541431, 0.506043, 0.472828, 0.441656, 0.412411, 0.384980, 0.359260, 0.335152, 0.312563, 0.291407, 0.271600, 0.253064, 0.235723, 0.219508, 0.204351, 0.190189, 0.176961, 0.164611, 0.153083, 0.142329, 0.132298, 0.122945, 0.114228, 0.106106, 0.098541, 0.091496, 0.084937, 0.078833, 0.073154, 0.067872, 0.062959, 0.058392, 0.054147, 0.050202, 0.046537, 0.043133, 0.039971, 0.037036, 0.034312, 0.031783, 0.029436, 0.027259, 0.025240, 0.023367, 0.021631, 0.020021, 0.018528, 0.017145, 0.015863, 0.014676, 0.013575, 0.012556, 0.011612, 0.010738, 0.009929, 0.009180, 0.008487, 0.007845, 0.007251, 0.006701, 0.006193, 0.005722, 0.005287, 0.004884, 0.004512, 0.004168, 0.003850, 0.003556, 0.003284, 0.003032, 0.002800, 0.002585, 0.002386, 0.002203, 0.002034, 0.001877, 0.001732, 0.001599, 0.001476, 0.001362, 0.001256, 0.001159, 0.001069, 0.000987, 0.000910, 0.000840, 0.000774, 0.000714, 0.000659, 0.000608, 0.000560, 0.000517, 0.000476, 0.000439, 0.000405, 0.000373, 0.000344, 0.000317, 0.000292, 0.000270, 0.000248, 0.000229, 0.000211, 0.000195, 0.000179, 0.000165, 0.000152, 0.000140, 0.000129, 0.000119, 0.000110, 0.000101, 0.000093, 0.000086, 0.000079, 0.000073, 0.000067, 0.000062, 0.000057, 0.000052, 0.000048, 0.000044, 0.000041, 0.000038, 0.000035, 0.000032, 0.000029, 0.000027, 0.000025, 0.000023, 0.000021, 0.000019, 0.000018, 0.000016, 0.000015, 0.000014, 0.000013, 0.000012, 0.000011, 0.000010, 0.000009, 0.000008, 0.000008, 0.000007, 0.000007, 0.000006, 0.000006, 0.000005, 0.000005, 0.000004, 0.000004, 0.000004, 0.000003, 0.000003, 0.000003, 0.000003, 0.000002, 0.000002, 0.000002, 0.000002, 0.000002, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000000, 0.000000, }; const int rate_tab_num = sizeof(rate_tab)/sizeof(rate_tab[0]); assert(x >= 0.0); return linear_interpolate(x, rate_tab_num, rate_tab_step, rate_tab); } static double model_dist_norm(double x) { // Normalized distortion // This function models the normalized distortion for a Laplacian source // source with given variance when quantized with a uniform quantizer // with given stepsize. The closed form expression is: // Dn(x) = 1 - 1/sqrt(2) * x / sinh(x/sqrt(2)) // where x = qpstep / sqrt(variance) // Note the actual distortion is Dn * variance. static const double dist_tab_step = 0.25; static const double dist_tab[] = { 0.000000, 0.005189, 0.020533, 0.045381, 0.078716, 0.119246, 0.165508, 0.215979, 0.269166, 0.323686, 0.378318, 0.432034, 0.484006, 0.533607, 0.580389, 0.624063, 0.664475, 0.701581, 0.735418, 0.766092, 0.793751, 0.818575, 0.840761, 0.860515, 0.878045, 0.893554, 0.907238, 0.919281, 0.929857, 0.939124, 0.947229, 0.954306, 0.960475, 0.965845, 0.970512, 0.974563, 0.978076, 0.981118, 0.983750, 0.986024, 0.987989, 0.989683, 0.991144, 0.992402, 0.993485, 0.994417, 0.995218, 0.995905, 0.996496, 0.997002, 0.997437, 0.997809, 0.998128, 0.998401, 0.998635, 0.998835, 0.999006, 0.999152, 0.999277, 0.999384, 0.999475, 0.999553, 0.999619, 0.999676, 0.999724, 0.999765, 0.999800, 0.999830, 0.999855, 0.999877, 0.999895, 0.999911, 0.999924, 0.999936, 0.999945, 0.999954, 0.999961, 0.999967, 0.999972, 0.999976, 0.999980, 0.999983, 0.999985, 0.999988, 0.999989, 0.999991, 0.999992, 0.999994, 0.999995, 0.999995, 0.999996, 0.999997, 0.999997, 0.999998, 0.999998, 0.999998, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 1.000000, }; const int dist_tab_num = sizeof(dist_tab)/sizeof(dist_tab[0]); assert(x >= 0.0); return linear_interpolate(x, dist_tab_num, dist_tab_step, dist_tab); } static void model_rd_from_var_lapndz(int var, int n, int qstep, int *rate, int *dist) { // This function models the rate and distortion for a Laplacian // source with given variance when quantized with a uniform quantizer // with given stepsize. The closed form expression is: // Rn(x) = H(sqrt(r)) + sqrt(r)*[1 + H(r)/(1 - r)], // where r = exp(-sqrt(2) * x) and x = qpstep / sqrt(variance) vp9_clear_system_state(); if (var == 0 || n == 0) { *rate = 0; ... ... @@ -1819,29 +1934,18 @@ static void model_rd_from_var_lapndz(int var, int n, int qstep, } else { double D, R; double s2 = (double) var / n; double s = sqrt(s2); double x = qstep / s; if (x > 1.0) { double y = exp(-x / 2); double y2 = y * y; D = 2.069981728764738 * y2 - 2.764286806516079 * y + 1.003956960819275; R = 0.924056758535089 * y2 + 2.738636469814024 * y - 0.005169662030017; } else { double x2 = x * x; D = 0.075303187668830 * x2 + 0.004296954321112 * x - 0.000413209252807; if (x > 0.125) R = 1 / (-0.03459733614226 * x2 + 0.36561675733603 * x + 0.1626989668625); else R = -1.442252874826093 * log(x) + 1.944647760719664; } double x = qstep / sqrt(s2); // TODO(debargha): Make the modeling functions take (qstep^2 / s2) // as argument rather than qstep / sqrt(s2) to obviate the need for // the sqrt() operation. D = model_dist_norm(x); R = model_rate_norm(x); if (R < 0) { *rate = 0; *dist = var; } else { *rate = (n * R * 256 + 0.5); *dist = (n * D * s2 + 0.5); R = 0; D = var; } *rate = (n * R * 256 + 0.5); *dist = (n * D * s2 + 0.5); } vp9_clear_system_state(); } ... ... @@ -1872,14 +1976,15 @@ static void model_rd_for_sb(VP9_COMP *cpi, BLOCK_SIZE_TYPE bsize, int rate, dist; var = cpi->fn_ptr[bs].vf(p->src.buf, p->src.stride, pd->dst.buf, pd->dst.stride, &sse); model_rd_from_var_lapndz(var, bw * bh, pd->dequant[1] >> 3, &rate, &dist); // sse works better than var, since there is no dc prediction used model_rd_from_var_lapndz(sse, bw * bh, pd->dequant[1] >> 3, &rate, &dist); rate_sum += rate; dist_sum += dist; } *out_rate_sum = rate_sum; *out_dist_sum = dist_sum; *out_dist_sum = dist_sum << 4; } static INLINE int get_switchable_rate(VP9_COMMON *cm, MACROBLOCK *x) { ... ...
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!