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FFmpeg/libavfilter/vf_vaguedenoiser.c

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/*
* Copyright (c) 2003 LeFunGus, lefungus@altern.org
*
* This file is part of FFmpeg
*
* FFmpeg is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* FFmpeg is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along
* with FFmpeg; if not, write to the Free Software Foundation, Inc.,
* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
*/
#include <float.h>
#include "libavutil/imgutils.h"
#include "libavutil/attributes.h"
#include "libavutil/common.h"
#include "libavutil/pixdesc.h"
#include "libavutil/intreadwrite.h"
#include "libavutil/opt.h"
#include "avfilter.h"
#include "formats.h"
#include "internal.h"
#include "video.h"
typedef struct VagueDenoiserContext {
const AVClass *class;
float threshold;
float percent;
int method;
int type;
int nsteps;
int planes;
int depth;
int bpc;
int peak;
int nb_planes;
int planeheight[4];
int planewidth[4];
float *block;
float *in;
float *out;
float *tmp;
int hlowsize[4][32];
int hhighsize[4][32];
int vlowsize[4][32];
int vhighsize[4][32];
void (*thresholding)(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent);
} VagueDenoiserContext;
#define OFFSET(x) offsetof(VagueDenoiserContext, x)
#define FLAGS AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_FILTERING_PARAM
static const AVOption vaguedenoiser_options[] = {
{ "threshold", "set filtering strength", OFFSET(threshold), AV_OPT_TYPE_FLOAT, {.dbl=2.}, 0,DBL_MAX, FLAGS },
{ "method", "set filtering method", OFFSET(method), AV_OPT_TYPE_INT, {.i64=2 }, 0, 2, FLAGS, "method" },
{ "hard", "hard thresholding", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "method" },
{ "soft", "soft thresholding", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "method" },
{ "garrote", "garrote thresholding", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "method" },
{ "nsteps", "set number of steps", OFFSET(nsteps), AV_OPT_TYPE_INT, {.i64=6 }, 1, 32, FLAGS },
{ "percent", "set percent of full denoising", OFFSET(percent),AV_OPT_TYPE_FLOAT, {.dbl=85}, 0,100, FLAGS },
{ "planes", "set planes to filter", OFFSET(planes), AV_OPT_TYPE_INT, {.i64=15 }, 0, 15, FLAGS },
{ "type", "set threshold type", OFFSET(type), AV_OPT_TYPE_INT, {.i64=0 }, 0, 1, FLAGS, "type" },
{ "universal", "universal (VisuShrink)", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "type" },
{ "bayes", "bayes (BayesShrink)", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "type" },
{ NULL }
};
AVFILTER_DEFINE_CLASS(vaguedenoiser);
#define NPAD 10
static const float analysis_low[9] = {
0.037828455506995f, -0.023849465019380f, -0.110624404418423f, 0.377402855612654f,
0.852698679009403f, 0.377402855612654f, -0.110624404418423f, -0.023849465019380f, 0.037828455506995f
};
static const float analysis_high[7] = {
-0.064538882628938f, 0.040689417609558f, 0.418092273222212f, -0.788485616405664f,
0.418092273222212f, 0.040689417609558f, -0.064538882628938f
};
static const float synthesis_low[7] = {
-0.064538882628938f, -0.040689417609558f, 0.418092273222212f, 0.788485616405664f,
0.418092273222212f, -0.040689417609558f, -0.064538882628938f
};
static const float synthesis_high[9] = {
-0.037828455506995f, -0.023849465019380f, 0.110624404418423f, 0.377402855612654f,
-0.852698679009403f, 0.377402855612654f, 0.110624404418423f, -0.023849465019380f, -0.037828455506995f
};
static int query_formats(AVFilterContext *ctx)
{
static const enum AVPixelFormat pix_fmts[] = {
AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10,
AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
AV_PIX_FMT_YUVJ420P, AV_PIX_FMT_YUVJ422P,
AV_PIX_FMT_YUVJ440P, AV_PIX_FMT_YUVJ444P,
AV_PIX_FMT_YUVJ411P,
AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
AV_PIX_FMT_YUV440P10,
AV_PIX_FMT_YUV444P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV420P12,
AV_PIX_FMT_YUV440P12,
AV_PIX_FMT_YUV444P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV420P14,
AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10,
AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
AV_PIX_FMT_GBRAP, AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
AV_PIX_FMT_NONE
};
AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
if (!fmts_list)
return AVERROR(ENOMEM);
return ff_set_common_formats(ctx, fmts_list);
}
static int config_input(AVFilterLink *inlink)
{
VagueDenoiserContext *s = inlink->dst->priv;
const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
int p, i, nsteps_width, nsteps_height, nsteps_max;
s->depth = desc->comp[0].depth;
s->bpc = (s->depth + 7) / 8;
s->nb_planes = desc->nb_components;
s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
s->planeheight[0] = s->planeheight[3] = inlink->h;
s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
s->planewidth[0] = s->planewidth[3] = inlink->w;
s->block = av_malloc_array(inlink->w * inlink->h, sizeof(*s->block));
s->in = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->in));
s->out = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->out));
s->tmp = av_malloc_array(32 + FFMAX(inlink->w, inlink->h), sizeof(*s->tmp));
if (!s->block || !s->in || !s->out || !s->tmp)
return AVERROR(ENOMEM);
s->threshold *= 1 << (s->depth - 8);
s->peak = (1 << s->depth) - 1;
nsteps_width = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planewidth[1] : s->planewidth[0];
nsteps_height = ((s->planes & 2 || s->planes & 4) && s->nb_planes > 1) ? s->planeheight[1] : s->planeheight[0];
for (nsteps_max = 1; nsteps_max < 15; nsteps_max++) {
if (pow(2, nsteps_max) >= nsteps_width || pow(2, nsteps_max) >= nsteps_height)
break;
}
s->nsteps = FFMIN(s->nsteps, nsteps_max - 2);
for (p = 0; p < 4; p++) {
s->hlowsize[p][0] = (s->planewidth[p] + 1) >> 1;
s->hhighsize[p][0] = s->planewidth[p] >> 1;
s->vlowsize[p][0] = (s->planeheight[p] + 1) >> 1;
s->vhighsize[p][0] = s->planeheight[p] >> 1;
for (i = 1; i < s->nsteps; i++) {
s->hlowsize[p][i] = (s->hlowsize[p][i - 1] + 1) >> 1;
s->hhighsize[p][i] = s->hlowsize[p][i - 1] >> 1;
s->vlowsize[p][i] = (s->vlowsize[p][i - 1] + 1) >> 1;
s->vhighsize[p][i] = s->vlowsize[p][i - 1] >> 1;
}
}
return 0;
}
static inline void copy(const float *p1, float *p2, const int length)
{
memcpy(p2, p1, length * sizeof(float));
}
static inline void copyv(const float *p1, const int stride1, float *p2, const int length)
{
int i;
for (i = 0; i < length; i++) {
p2[i] = *p1;
p1 += stride1;
}
}
static inline void copyh(const float *p1, float *p2, const int stride2, const int length)
{
int i;
for (i = 0; i < length; i++) {
*p2 = p1[i];
p2 += stride2;
}
}
// Do symmetric extension of data using prescribed symmetries
// Original values are in output[npad] through output[npad+size-1]
// New values will be placed in output[0] through output[npad] and in output[npad+size] through output[2*npad+size-1] (note: end values may not be filled in)
// extension at left bdry is ... 3 2 1 0 | 0 1 2 3 ...
// same for right boundary
// if right_ext=1 then ... 3 2 1 0 | 1 2 3
static void symmetric_extension(float *output, const int size, const int left_ext, const int right_ext)
{
int first = NPAD;
int last = NPAD - 1 + size;
const int originalLast = last;
int i, nextend, idx;
if (left_ext == 2)
output[--first] = output[NPAD];
if (right_ext == 2)
output[++last] = output[originalLast];
// extend left end
nextend = first;
for (i = 0; i < nextend; i++)
output[--first] = output[NPAD + 1 + i];
idx = NPAD + NPAD - 1 + size;
// extend right end
nextend = idx - last;
for (i = 0; i < nextend; i++)
output[++last] = output[originalLast - 1 - i];
}
static void transform_step(float *input, float *output, const int size, const int low_size, VagueDenoiserContext *s)
{
int i;
symmetric_extension(input, size, 1, 1);
for (i = NPAD; i < NPAD + low_size; i++) {
const float a = input[2 * i - 14] * analysis_low[0];
const float b = input[2 * i - 13] * analysis_low[1];
const float c = input[2 * i - 12] * analysis_low[2];
const float d = input[2 * i - 11] * analysis_low[3];
const float e = input[2 * i - 10] * analysis_low[4];
const float f = input[2 * i - 9] * analysis_low[3];
const float g = input[2 * i - 8] * analysis_low[2];
const float h = input[2 * i - 7] * analysis_low[1];
const float k = input[2 * i - 6] * analysis_low[0];
output[i] = a + b + c + d + e + f + g + h + k;
}
for (i = NPAD; i < NPAD + low_size; i++) {
const float a = input[2 * i - 12] * analysis_high[0];
const float b = input[2 * i - 11] * analysis_high[1];
const float c = input[2 * i - 10] * analysis_high[2];
const float d = input[2 * i - 9] * analysis_high[3];
const float e = input[2 * i - 8] * analysis_high[2];
const float f = input[2 * i - 7] * analysis_high[1];
const float g = input[2 * i - 6] * analysis_high[0];
output[i + low_size] = a + b + c + d + e + f + g;
}
}
static void invert_step(const float *input, float *output, float *temp, const int size, VagueDenoiserContext *s)
{
const int low_size = (size + 1) >> 1;
const int high_size = size >> 1;
int left_ext = 1, right_ext, i;
int findex;
memcpy(temp + NPAD, input + NPAD, low_size * sizeof(float));
right_ext = (size % 2 == 0) ? 2 : 1;
symmetric_extension(temp, low_size, left_ext, right_ext);
memset(output, 0, (NPAD + NPAD + size) * sizeof(float));
findex = (size + 2) >> 1;
for (i = 9; i < findex + 11; i++) {
const float a = temp[i] * synthesis_low[0];
const float b = temp[i] * synthesis_low[1];
const float c = temp[i] * synthesis_low[2];
const float d = temp[i] * synthesis_low[3];
output[2 * i - 13] += a;
output[2 * i - 12] += b;
output[2 * i - 11] += c;
output[2 * i - 10] += d;
output[2 * i - 9] += c;
output[2 * i - 8] += b;
output[2 * i - 7] += a;
}
memcpy(temp + NPAD, input + NPAD + low_size, high_size * sizeof(float));
left_ext = 2;
right_ext = (size % 2 == 0) ? 1 : 2;
symmetric_extension(temp, high_size, left_ext, right_ext);
for (i = 8; i < findex + 11; i++) {
const float a = temp[i] * synthesis_high[0];
const float b = temp[i] * synthesis_high[1];
const float c = temp[i] * synthesis_high[2];
const float d = temp[i] * synthesis_high[3];
const float e = temp[i] * synthesis_high[4];
output[2 * i - 13] += a;
output[2 * i - 12] += b;
output[2 * i - 11] += c;
output[2 * i - 10] += d;
output[2 * i - 9] += e;
output[2 * i - 8] += d;
output[2 * i - 7] += c;
output[2 * i - 6] += b;
output[2 * i - 5] += a;
}
}
static void hard_thresholding(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent)
{
const float frac = 1.f - percent * 0.01f;
int y, x;
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++) {
if (FFABS(block[x]) <= threshold)
block[x] *= frac;
}
block += stride;
}
}
static void soft_thresholding(float *block, const int width, const int height, const int stride,
const float threshold, const float percent)
{
const float frac = 1.f - percent * 0.01f;
const float shift = threshold * 0.01f * percent;
int y, x;
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++) {
const float temp = FFABS(block[x]);
if (temp <= threshold)
block[x] *= frac;
else
block[x] = (block[x] < 0.f ? -1.f : (block[x] > 0.f ? 1.f : 0.f)) * (temp - shift);
}
block += stride;
}
}
static void qian_thresholding(float *block, const int width, const int height,
const int stride, const float threshold,
const float percent)
{
const float percent01 = percent * 0.01f;
const float tr2 = threshold * threshold * percent01;
const float frac = 1.f - percent01;
int y, x;
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++) {
const float temp = FFABS(block[x]);
if (temp <= threshold) {
block[x] *= frac;
} else {
const float tp2 = temp * temp;
block[x] *= (tp2 - tr2) / tp2;
}
}
block += stride;
}
}
static float bayes_threshold(float *block, const int width, const int height,
const int stride, const float threshold)
{
float mean = 0.f;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
mean += block[x] * block[x];
}
block += stride;
}
mean /= width * height;
return threshold * threshold / (FFMAX(sqrtf(mean - threshold), FLT_EPSILON));
}
static void filter(VagueDenoiserContext *s, AVFrame *in, AVFrame *out)
{
int p, y, x, i, j;
for (p = 0; p < s->nb_planes; p++) {
const int height = s->planeheight[p];
const int width = s->planewidth[p];
const uint8_t *srcp8 = in->data[p];
const uint16_t *srcp16 = (const uint16_t *)in->data[p];
uint8_t *dstp8 = out->data[p];
uint16_t *dstp16 = (uint16_t *)out->data[p];
float *output = s->block;
int h_low_size0 = width;
int v_low_size0 = height;
int nsteps_transform = s->nsteps;
int nsteps_invert = s->nsteps;
const float *input = s->block;
if (!((1 << p) & s->planes)) {
av_image_copy_plane(out->data[p], out->linesize[p], in->data[p], in->linesize[p],
s->planewidth[p] * s->bpc, s->planeheight[p]);
continue;
}
if (s->depth <= 8) {
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++)
output[x] = srcp8[x];
srcp8 += in->linesize[p];
output += width;
}
} else {
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++)
output[x] = srcp16[x];
srcp16 += in->linesize[p] / 2;
output += width;
}
}
while (nsteps_transform--) {
int low_size = (h_low_size0 + 1) >> 1;
float *input = s->block;
for (j = 0; j < v_low_size0; j++) {
copy(input, s->in + NPAD, h_low_size0);
transform_step(s->in, s->out, h_low_size0, low_size, s);
copy(s->out + NPAD, input, h_low_size0);
input += width;
}
low_size = (v_low_size0 + 1) >> 1;
input = s->block;
for (j = 0; j < h_low_size0; j++) {
copyv(input, width, s->in + NPAD, v_low_size0);
transform_step(s->in, s->out, v_low_size0, low_size, s);
copyh(s->out + NPAD, input, width, v_low_size0);
input++;
}
h_low_size0 = (h_low_size0 + 1) >> 1;
v_low_size0 = (v_low_size0 + 1) >> 1;
}
if (s->type == 0) {
s->thresholding(s->block, width, height, width, s->threshold, s->percent);
} else {
for (int n = 0; n < s->nsteps; n++) {
float threshold;
float *block;
if (n == s->nsteps - 1) {
threshold = bayes_threshold(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, s->threshold);
s->thresholding(s->block, s->hlowsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
}
block = s->block + s->hlowsize[p][n];
threshold = bayes_threshold(block, s->hhighsize[p][n], s->vlowsize[p][n], width, s->threshold);
s->thresholding(block, s->hhighsize[p][n], s->vlowsize[p][n], width, threshold, s->percent);
block = s->block + s->vlowsize[p][n] * width;
threshold = bayes_threshold(block, s->hlowsize[p][n], s->vhighsize[p][n], width, s->threshold);
s->thresholding(block, s->hlowsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
block = s->block + s->hlowsize[p][n] + s->vlowsize[p][n] * width;
threshold = bayes_threshold(block, s->hhighsize[p][n], s->vhighsize[p][n], width, s->threshold);
s->thresholding(block, s->hhighsize[p][n], s->vhighsize[p][n], width, threshold, s->percent);
}
}
while (nsteps_invert--) {
const int idx = s->vlowsize[p][nsteps_invert] + s->vhighsize[p][nsteps_invert];
const int idx2 = s->hlowsize[p][nsteps_invert] + s->hhighsize[p][nsteps_invert];
float * idx3 = s->block;
for (i = 0; i < idx2; i++) {
copyv(idx3, width, s->in + NPAD, idx);
invert_step(s->in, s->out, s->tmp, idx, s);
copyh(s->out + NPAD, idx3, width, idx);
idx3++;
}
idx3 = s->block;
for (i = 0; i < idx; i++) {
copy(idx3, s->in + NPAD, idx2);
invert_step(s->in, s->out, s->tmp, idx2, s);
copy(s->out + NPAD, idx3, idx2);
idx3 += width;
}
}
if (s->depth <= 8) {
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++)
dstp8[x] = av_clip_uint8(input[x] + 0.5f);
input += width;
dstp8 += out->linesize[p];
}
} else {
for (y = 0; y < height; y++) {
for (x = 0; x < width; x++)
dstp16[x] = av_clip(input[x] + 0.5f, 0, s->peak);
input += width;
dstp16 += out->linesize[p] / 2;
}
}
}
}
static int filter_frame(AVFilterLink *inlink, AVFrame *in)
{
AVFilterContext *ctx = inlink->dst;
VagueDenoiserContext *s = ctx->priv;
AVFilterLink *outlink = ctx->outputs[0];
AVFrame *out;
int direct = av_frame_is_writable(in);
if (direct) {
out = in;
} else {
out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
if (!out) {
av_frame_free(&in);
return AVERROR(ENOMEM);
}
av_frame_copy_props(out, in);
}
filter(s, in, out);
if (!direct)
av_frame_free(&in);
return ff_filter_frame(outlink, out);
}
static av_cold int init(AVFilterContext *ctx)
{
VagueDenoiserContext *s = ctx->priv;
switch (s->method) {
case 0:
s->thresholding = hard_thresholding;
break;
case 1:
s->thresholding = soft_thresholding;
break;
case 2:
s->thresholding = qian_thresholding;
break;
}
return 0;
}
static av_cold void uninit(AVFilterContext *ctx)
{
VagueDenoiserContext *s = ctx->priv;
av_freep(&s->block);
av_freep(&s->in);
av_freep(&s->out);
av_freep(&s->tmp);
}
static const AVFilterPad vaguedenoiser_inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_input,
.filter_frame = filter_frame,
},
{ NULL }
};
static const AVFilterPad vaguedenoiser_outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO
},
{ NULL }
};
AVFilter ff_vf_vaguedenoiser = {
.name = "vaguedenoiser",
.description = NULL_IF_CONFIG_SMALL("Apply a Wavelet based Denoiser."),
.priv_size = sizeof(VagueDenoiserContext),
.priv_class = &vaguedenoiser_class,
.init = init,
.uninit = uninit,
.query_formats = query_formats,
.inputs = vaguedenoiser_inputs,
.outputs = vaguedenoiser_outputs,
.flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
};
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