[FFmpeg-devel,V6] Add a filter implementing HDR image generation from a single exposure using deep CNNs

Submitted by Guo, Yejun on Nov. 16, 2018, 2:30 p.m.

Details

Message ID 1542378605-24091-1-git-send-email-yejun.guo@intel.com
State Superseded
Headers show

Commit Message

Guo, Yejun Nov. 16, 2018, 2:30 p.m.
see the algorithm's paper and code below.

the filter's parameter looks like:
sdr2hdr=model_filename=/path_to_tensorflow_graph.pb:out_fmt=gbrp10le

The input of the deep CNN model is RGB24 while the output is float
for each color channel. This is the filter's default behavior to
output format with gbrpf32le. And gbrp10le is also supported as the
output, so we can see the rendering result in a player, as a reference.

To generate the model file, we need modify the original script a little.
- set name='y' for y_final within script at
https://github.com/gabrieleilertsen/hdrcnn/blob/master/network.py
- add the following code to the script at
https://github.com/gabrieleilertsen/hdrcnn/blob/master/hdrcnn_predict.py

graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ["y"])
tf.train.write_graph(graph, '.', 'graph.pb', as_text=False)

And I also uploaded the model file under https://drive.google.com/drive/folders/1URsRY5g-VdE-kHlP5vQoLoimMIZ-SX00?usp=sharing.

The filter only works when tensorflow C api is supported in the system,
native backend is not supported since there are some different types of
layers in the deep CNN model, besides CONV and DEPTH_TO_SPACE.

https://arxiv.org/pdf/1710.07480.pdf:
  author       = "Eilertsen, Gabriel and Kronander, Joel, and Denes, Gyorgy and Mantiuk, Rafał and Unger, Jonas",
  title        = "HDR image reconstruction from a single exposure using deep CNNs",
  journal      = "ACM Transactions on Graphics (TOG)",
  number       = "6",
  volume       = "36",
  articleno    = "178",
  year         = "2017"

https://github.com/gabrieleilertsen/hdrcnn

btw, as a whole solution, metadata should also be generated from
the sdr video, so to be encoded as a HDR video. Not supported yet.
This patch just focuses on this paper.

This filter accepts 8bit frame (RGB24) and outputs 10bit/float frame,
and there's no reference image, so it is not feasible to use criteria such as PNSR, SSIM.

I choose the same method described in the paper to demo the filter effect,
that means the frames before/after the filter are reduced by 3 stops.

The native video (test.native.mp4) is created from 7 png files at
https://github.com/gabrieleilertsen/hdrcnn/tree/master/data (the size of the
image is enlarged to 1920*1080 with extra area filled with white) with command line:
ffmpeg -f image2 -i ./img_%03d.png -c:v libx264 -preset veryslow -crf 1  test.native.mp4.

And two rgb24 videos are generated before/after the filter with -3 stops
by modifying the code a little, see in the video folder at the google drive
(the same place as where the model file locates).

For your convenient, I also dump png files from generated videos and combine
the before/after pngs into one file, see in png folder at the google drive.

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
---
 configure                |   1 +
 doc/filters.texi         |  38 +++++++
 libavfilter/Makefile     |   1 +
 libavfilter/allfilters.c |   1 +
 libavfilter/vf_sdr2hdr.c | 270 +++++++++++++++++++++++++++++++++++++++++++++++
 5 files changed, 311 insertions(+)
 create mode 100644 libavfilter/vf_sdr2hdr.c

Comments

Guo, Yejun Nov. 28, 2018, 7:35 a.m.
thanks for the reviews, let me summarize the unfixed issues and my plan.

- more resolutions support besides 1080p. (comment from Vittorio Giovara, Liu Steven, Li Zhong)
I've sent an issue to tensorflow to explain the issue and provide a possible solution, 
see https://github.com/tensorflow/tensorflow/issues/2118#issuecomment-441146241.
Before it is finally fixed by tensorflow, as a workaround, I'll prepare more model files
for typical resolutions, one model file for one resolution. And the user need to choose
the correct model file for the given resolution.

- native mode support.   (comment from Pedro Arthur, Liu Steven)
There are 16 ops not supported now,  I plan to add them one by one. And there is 
another thing to add a tool to write the native model file directly or convert
from TF model file.

- metadata for HDR video encoding. (comment from Vittorio Giovara, Li Zhong)
will figure out a method for it.


> -----Original Message-----

> From: ffmpeg-devel [mailto:ffmpeg-devel-bounces@ffmpeg.org] On Behalf

> Of Guo, Yejun

> Sent: Friday, November 16, 2018 10:30 PM

> To: ffmpeg-devel@ffmpeg.org

> Subject: [FFmpeg-devel] [PATCH V6] Add a filter implementing HDR image

> generation from a single exposure using deep CNNs

> 

> see the algorithm's paper and code below.

> 

> the filter's parameter looks like:

> sdr2hdr=model_filename=/path_to_tensorflow_graph.pb:out_fmt=gbrp10l

> e

> 

> The input of the deep CNN model is RGB24 while the output is float for each

> color channel. This is the filter's default behavior to output format with

> gbrpf32le. And gbrp10le is also supported as the output, so we can see the

> rendering result in a player, as a reference.

> 

> To generate the model file, we need modify the original script a little.

> - set name='y' for y_final within script at

> https://github.com/gabrieleilertsen/hdrcnn/blob/master/network.py

> - add the following code to the script at

> https://github.com/gabrieleilertsen/hdrcnn/blob/master/hdrcnn_predict.py

> 

> graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def,

> ["y"]) tf.train.write_graph(graph, '.', 'graph.pb', as_text=False)

> 

> And I also uploaded the model file under

> https://drive.google.com/drive/folders/1URsRY5g-VdE-kHlP5vQoLoimMIZ-

> SX00?usp=sharing.

> 

> The filter only works when tensorflow C api is supported in the system,

> native backend is not supported since there are some different types of

> layers in the deep CNN model, besides CONV and DEPTH_TO_SPACE.

> 

> https://arxiv.org/pdf/1710.07480.pdf:

>   author       = "Eilertsen, Gabriel and Kronander, Joel, and Denes, Gyorgy and

> Mantiuk, Rafał and Unger, Jonas",

>   title        = "HDR image reconstruction from a single exposure using deep

> CNNs",

>   journal      = "ACM Transactions on Graphics (TOG)",

>   number       = "6",

>   volume       = "36",

>   articleno    = "178",

>   year         = "2017"

> 

> https://github.com/gabrieleilertsen/hdrcnn

> 

> btw, as a whole solution, metadata should also be generated from the sdr

> video, so to be encoded as a HDR video. Not supported yet.

> This patch just focuses on this paper.

> 

> This filter accepts 8bit frame (RGB24) and outputs 10bit/float frame, and

> there's no reference image, so it is not feasible to use criteria such as PNSR,

> SSIM.

> 

> I choose the same method described in the paper to demo the filter effect,

> that means the frames before/after the filter are reduced by 3 stops.

> 

> The native video (test.native.mp4) is created from 7 png files at

> https://github.com/gabrieleilertsen/hdrcnn/tree/master/data (the size of

> the image is enlarged to 1920*1080 with extra area filled with white) with

> command line:

> ffmpeg -f image2 -i ./img_%03d.png -c:v libx264 -preset veryslow -crf 1

> test.native.mp4.

> 

> And two rgb24 videos are generated before/after the filter with -3 stops by

> modifying the code a little, see in the video folder at the google drive (the

> same place as where the model file locates).

> 

> For your convenient, I also dump png files from generated videos and

> combine the before/after pngs into one file, see in png folder at the google

> drive.

> 

> Signed-off-by: Guo, Yejun <yejun.guo@intel.com>

> ---

>  configure                |   1 +

>  doc/filters.texi         |  38 +++++++

>  libavfilter/Makefile     |   1 +

>  libavfilter/allfilters.c |   1 +

>  libavfilter/vf_sdr2hdr.c | 270

> +++++++++++++++++++++++++++++++++++++++++++++++

>  5 files changed, 311 insertions(+)

>  create mode 100644 libavfilter/vf_sdr2hdr.c

> 

> diff --git a/configure b/configure

> index 9bc4cf3..08db4eb 100755

> --- a/configure

> +++ b/configure

> @@ -3447,6 +3447,7 @@ sab_filter_deps="gpl swscale"

>  scale2ref_filter_deps="swscale"

>  scale_filter_deps="swscale"

>  scale_qsv_filter_deps="libmfx"

> +sdr2hdr_filter_deps="libtensorflow"

>  select_filter_select="scene_sad"

>  sharpness_vaapi_filter_deps="vaapi"

>  showcqt_filter_deps="avcodec avformat swscale"

> diff --git a/doc/filters.texi b/doc/filters.texi index ab58e53..86432d9 100644

> --- a/doc/filters.texi

> +++ b/doc/filters.texi

> @@ -14872,6 +14872,44 @@ Scale a subtitle stream (b) to match the main

> video (a) in size before overlayin  @end example  @end itemize

> 

> +@section sdr2hdr

> +

> +HDR image generation from a single exposure using deep CNNs with

> TensorFlow C library.

> +The input format of the filter is RGB24, and now only supports

> +resolution with 1920*1080, there's no meta data generated for HDR video

> yet.

> +

> +@itemize

> +@item

> +paper:  see @url{https://arxiv.org/pdf/1710.07480.pdf}

> +

> +@item

> +code with model and trained parameters: see

> +@url{https://github.com/gabrieleilertsen/hdrcnn}

> +@end itemize

> +

> +The filter accepts the following options:

> +

> +@table @option

> +

> +@item model_filename

> +Set path to model file specifying network architecture and its

> +parameters, can download from

> +@url{https://drive.google.com/drive/folders/1URsRY5g-VdE-

> kHlP5vQoLoimMI

> +Z-SX00?usp=sharing}

> +

> +@item out_fmt

> +the data format of the filter's output.

> +

> +It accepts the following values:

> +@table @samp

> +@item gbrpf32le

> +force gbrpf32le output

> +

> +@item gbrp10le

> +force gbrp10le output

> +@end table

> +

> +Default value is @samp{gbrpf32le}.

> +

> +@end table

> +

>  @anchor{selectivecolor}

>  @section selectivecolor

> 

> diff --git a/libavfilter/Makefile b/libavfilter/Makefile index a7ebd02..7ad8250

> 100644

> --- a/libavfilter/Makefile

> +++ b/libavfilter/Makefile

> @@ -366,6 +366,7 @@ OBJS-$(CONFIG_SOBEL_OPENCL_FILTER)           +=

> vf_convolution_opencl.o opencl.o

>  OBJS-$(CONFIG_SPLIT_FILTER)                  += split.o

>  OBJS-$(CONFIG_SPP_FILTER)                    += vf_spp.o

>  OBJS-$(CONFIG_SR_FILTER)                     += vf_sr.o

> +OBJS-$(CONFIG_SDR2HDR_FILTER)                += vf_sdr2hdr.o

>  OBJS-$(CONFIG_SSIM_FILTER)                   += vf_ssim.o framesync.o

>  OBJS-$(CONFIG_STEREO3D_FILTER)               += vf_stereo3d.o

>  OBJS-$(CONFIG_STREAMSELECT_FILTER)           += f_streamselect.o

> framesync.o

> diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c index 484b080..622f9f3

> 100644

> --- a/libavfilter/allfilters.c

> +++ b/libavfilter/allfilters.c

> @@ -322,6 +322,7 @@ extern AVFilter ff_vf_scale_npp;  extern AVFilter

> ff_vf_scale_qsv;  extern AVFilter ff_vf_scale_vaapi;  extern AVFilter

> ff_vf_scale2ref;

> +extern AVFilter ff_vf_sdr2hdr;

>  extern AVFilter ff_vf_select;

>  extern AVFilter ff_vf_selectivecolor;

>  extern AVFilter ff_vf_sendcmd;

> diff --git a/libavfilter/vf_sdr2hdr.c b/libavfilter/vf_sdr2hdr.c new file mode

> 100644 index 0000000..fcee404

> --- /dev/null

> +++ b/libavfilter/vf_sdr2hdr.c

> @@ -0,0 +1,270 @@

> +/*

> + * Copyright (c) 2018 Guo Yejun

> + *

> + * This file is part of FFmpeg.

> + *

> + * FFmpeg is free software; you can redistribute it and/or

> + * modify it under the terms of the GNU Lesser General Public

> + * License as published by the Free Software Foundation; either

> + * version 2.1 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

> + * Lesser General Public License for more details.

> + *

> + * You should have received a copy of the GNU Lesser 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  */

> +

> +/**

> + * @file

> + * Filter implementing HDR image generation from a single exposure using

> deep CNNs.

> + * https://arxiv.org/pdf/1710.07480.pdf

> + */

> +

> +#include "avfilter.h"

> +#include "formats.h"

> +#include "internal.h"

> +#include "libavutil/opt.h"

> +#include "libavutil/qsort.h"

> +#include "libavformat/avio.h"

> +#include "libswscale/swscale.h"

> +#include "dnn_interface.h"

> +#include <math.h>

> +

> +typedef struct SDR2HDRContext {

> +    const AVClass *class;

> +

> +    char* model_filename;

> +    enum AVPixelFormat out_fmt;

> +    DNNModule* dnn_module;

> +    DNNModel* model;

> +    DNNData input, output;

> +} SDR2HDRContext;

> +

> +#define OFFSET(x) offsetof(SDR2HDRContext, x) #define FLAGS

> +AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM static

> const

> +AVOption sdr2hdr_options[] = {

> +    { "model_filename", "path to model file specifying network architecture

> and its parameters", OFFSET(model_filename), AV_OPT_TYPE_STRING,

> {.str=NULL}, 0, 0, FLAGS },

> +    { "out_fmt", "the data format of the filter's output, it could be gbrpf32le

> [default] or gbrp10le", OFFSET(out_fmt), AV_OPT_TYPE_PIXEL_FMT,

> {.i64=AV_PIX_FMT_GBRPF32LE}, AV_PIX_FMT_NONE, AV_PIX_FMT_NB - 1,

> FLAGS },

> +    { NULL }

> +};

> +

> +AVFILTER_DEFINE_CLASS(sdr2hdr);

> +

> +static av_cold int init(AVFilterContext* context) {

> +    SDR2HDRContext* ctx = context->priv;

> +

> +    if (ctx->out_fmt != AV_PIX_FMT_GBRPF32LE && ctx->out_fmt !=

> AV_PIX_FMT_GBRP10LE) {

> +        av_log(context, AV_LOG_ERROR, "could not support the output

> format\n");

> +        return AVERROR(ENOSYS);

> +    }

> +

> +    ctx->dnn_module = ff_get_dnn_module(DNN_TF);

> +    if (!ctx->dnn_module){

> +        av_log(context, AV_LOG_ERROR, "could not create DNN module for

> tensorflow backend\n");

> +        return AVERROR(ENOMEM);

> +    }

> +    if (!ctx->model_filename){

> +        av_log(context, AV_LOG_ERROR, "model file for network was not

> specified\n");

> +        return AVERROR(EIO);

> +    }

> +    if (!ctx->dnn_module->load_model) {

> +        av_log(context, AV_LOG_ERROR, "load_model for network was not

> specified\n");

> +        return AVERROR(EIO);

> +    }

> +    ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename);

> +    if (!ctx->model){

> +        av_log(context, AV_LOG_ERROR, "could not load DNN model\n");

> +        return AVERROR(EIO);

> +    }

> +    return 0;

> +}

> +

> +static int query_formats(AVFilterContext* context) {

> +    const enum AVPixelFormat in_formats[] = {AV_PIX_FMT_RGB24,

> +                                             AV_PIX_FMT_NONE};

> +    enum AVPixelFormat out_formats[2];

> +    SDR2HDRContext* ctx = context->priv;

> +    AVFilterFormats* formats_list;

> +    int ret = 0;

> +

> +    formats_list = ff_make_format_list(in_formats);

> +    if ((ret = ff_formats_ref(formats_list, &context->inputs[0]->out_formats))

> < 0)

> +        return ret;

> +

> +    out_formats[0] = ctx->out_fmt;

> +    out_formats[1] = AV_PIX_FMT_NONE;

> +    formats_list = ff_make_format_list(out_formats);

> +    if ((ret = ff_formats_ref(formats_list, &context->outputs[0]->in_formats))

> < 0)

> +        return ret;

> +

> +    return 0;

> +}

> +

> +static int config_props(AVFilterLink* inlink) {

> +    AVFilterContext* context = inlink->dst;

> +    SDR2HDRContext* ctx = context->priv;

> +    AVFilterLink* outlink = context->outputs[0];

> +    DNNReturnType result;

> +

> +    // the dnn model is tied with resolution due to deconv layer of tensorflow

> +    // now just support 1920*1080 and so the magic numbers within this file

> +    if (inlink->w != 1920 || inlink->h != 1080) {

> +        av_log(context, AV_LOG_ERROR, "only support frame size with

> 1920*1080\n");

> +        return AVERROR(ENOSYS);

> +     }

> +

> +    ctx->input.width = inlink->w;

> +    ctx->input.height = FFALIGN(inlink->h, 32);  //the model requires height is

> a multiple of 32,

> +    ctx->input.channels = 3;

> +

> +    result = (ctx->model->set_input_output)(ctx->model->model, &ctx-

> >input, &ctx->output);

> +    if (result != DNN_SUCCESS){

> +        av_log(context, AV_LOG_ERROR, "could not set input and output for

> the model\n");

> +        return AVERROR(EIO);

> +    }

> +

> +    memset(ctx->input.data, 0, ctx->input.channels * ctx->input.width * ctx-

> >input.height * sizeof(float));

> +    outlink->h = inlink->h;

> +    outlink->w = inlink->w;

> +    return 0;

> +}

> +

> +static float qsort_comparison_function_float(const void *a, const void

> +*b) {

> +    return *(const float *)a - *(const float *)b; }

> +

> +static int filter_frame(AVFilterLink* inlink, AVFrame* in) {

> +    DNNReturnType dnn_result = DNN_SUCCESS;

> +    AVFilterContext* context = inlink->dst;

> +    SDR2HDRContext* ctx = context->priv;

> +    AVFilterLink* outlink = context->outputs[0];

> +    AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h);

> +    int total_pixels = in->height * in->width;

> +

> +    if (!out){

> +        av_log(context, AV_LOG_ERROR, "could not allocate memory for

> output frame\n");

> +        av_frame_free(&in);

> +        return AVERROR(ENOMEM);

> +    }

> +

> +    av_frame_copy_props(out, in);

> +

> +    for (int i = 0; i < in->linesize[0] * in->height; ++i) {

> +        ctx->input.data[i] = in->data[0][i] / 255.0f;

> +    }

> +

> +    dnn_result = (ctx->dnn_module->execute_model)(ctx->model);

> +    if (dnn_result != DNN_SUCCESS){

> +        av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");

> +        return AVERROR(EIO);

> +    }

> +

> +    if (ctx->out_fmt == AV_PIX_FMT_GBRPF32LE) {

> +        float* outg = (float*)out->data[0];

> +        float* outb = (float*)out->data[1];

> +        float* outr = (float*)out->data[2];

> +        for (int i = 0; i < total_pixels; ++i) {

> +            float r = ctx->output.data[i*3];

> +            float g = ctx->output.data[i*3+1];

> +            float b = ctx->output.data[i*3+2];

> +            outr[i] = r;

> +            outg[i] = g;

> +            outb[i] = b;

> +        }

> +    } else if (ctx->out_fmt == AV_PIX_FMT_GBRP10LE) {

> +        // here, we just use a rough mapping to the 10bit contents.

> +        // meta data generation for HDR video encoding is not supported yet

> +        float* converted_data = (float*)av_malloc(total_pixels * 3 *

> sizeof(float));

> +        int16_t* outg = (int16_t*)out->data[0];

> +        int16_t* outb = (int16_t*)out->data[1];

> +        int16_t* outr = (int16_t*)out->data[2];

> +

> +        float max = 1.0f;

> +        for (int i = 0; i < total_pixels * 3; ++i) {

> +            float d = ctx->output.data[i];

> +            d = sqrt(d);

> +            converted_data[i] = d;

> +            max = FFMAX(d, max);

> +        }

> +

> +        if (max > 1.0f) {

> +            AV_QSORT(converted_data, total_pixels * 3, float,

> qsort_comparison_function_float);

> +            // 0.5% pixels are clipped

> +            max = converted_data[(int)(total_pixels * 3 * 0.995)];

> +            max = FFMAX(max, 1.0f);

> +

> +            for (int i = 0; i < total_pixels * 3; ++i) {

> +                float d = ctx->output.data[i];

> +                d = sqrt(d);

> +                d = FFMIN(d, max);

> +                converted_data[i] = d;

> +            }

> +        }

> +

> +        for (int i = 0; i < total_pixels; ++i) {

> +            float r = converted_data[i*3];

> +            float g = converted_data[i*3+1];

> +            float b = converted_data[i*3+2];

> +            outr[i] = r / max * 1023;

> +            outg[i] = g / max * 1023;

> +            outb[i] = b / max * 1023;

> +        }

> +

> +        av_free(converted_data);

> +    } else {

> +        assert(!"should not reach here");

> +    }

> +

> +    av_frame_free(&in);

> +    return ff_filter_frame(outlink, out); }

> +

> +static av_cold void uninit(AVFilterContext* context) {

> +    SDR2HDRContext* ctx = context->priv;

> +

> +    if (ctx->dnn_module){

> +        (ctx->dnn_module->free_model)(&ctx->model);

> +        av_freep(&ctx->dnn_module);

> +    }

> +}

> +

> +static const AVFilterPad sdr2hdr_inputs[] = {

> +    {

> +        .name         = "default",

> +        .type         = AVMEDIA_TYPE_VIDEO,

> +        .config_props = config_props,

> +        .filter_frame = filter_frame,

> +    },

> +    { NULL }

> +};

> +

> +static const AVFilterPad sdr2hdr_outputs[] = {

> +    {

> +        .name = "default",

> +        .type = AVMEDIA_TYPE_VIDEO,

> +    },

> +    { NULL }

> +};

> +

> +AVFilter ff_vf_sdr2hdr = {

> +    .name          = "sdr2hdr",

> +    .description   = NULL_IF_CONFIG_SMALL("HDR image generation from a

> single exposure using deep CNNs."),

> +    .priv_size     = sizeof(SDR2HDRContext),

> +    .init          = init,

> +    .uninit        = uninit,

> +    .query_formats = query_formats,

> +    .inputs        = sdr2hdr_inputs,

> +    .outputs       = sdr2hdr_outputs,

> +    .priv_class    = &sdr2hdr_class,

> +    .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,

> +};

> --

> 2.7.4

> 

> _______________________________________________

> ffmpeg-devel mailing list

> ffmpeg-devel@ffmpeg.org

> http://ffmpeg.org/mailman/listinfo/ffmpeg-devel

Patch hide | download patch | download mbox

diff --git a/configure b/configure
index 9bc4cf3..08db4eb 100755
--- a/configure
+++ b/configure
@@ -3447,6 +3447,7 @@  sab_filter_deps="gpl swscale"
 scale2ref_filter_deps="swscale"
 scale_filter_deps="swscale"
 scale_qsv_filter_deps="libmfx"
+sdr2hdr_filter_deps="libtensorflow"
 select_filter_select="scene_sad"
 sharpness_vaapi_filter_deps="vaapi"
 showcqt_filter_deps="avcodec avformat swscale"
diff --git a/doc/filters.texi b/doc/filters.texi
index ab58e53..86432d9 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -14872,6 +14872,44 @@  Scale a subtitle stream (b) to match the main video (a) in size before overlayin
 @end example
 @end itemize
 
+@section sdr2hdr
+
+HDR image generation from a single exposure using deep CNNs with TensorFlow C library.
+The input format of the filter is RGB24, and now only supports resolution with 1920*1080,
+there's no meta data generated for HDR video yet.
+
+@itemize
+@item
+paper:  see @url{https://arxiv.org/pdf/1710.07480.pdf}
+
+@item
+code with model and trained parameters: see @url{https://github.com/gabrieleilertsen/hdrcnn}
+@end itemize
+
+The filter accepts the following options:
+
+@table @option
+
+@item model_filename
+Set path to model file specifying network architecture and its parameters, can download
+from @url{https://drive.google.com/drive/folders/1URsRY5g-VdE-kHlP5vQoLoimMIZ-SX00?usp=sharing}
+
+@item out_fmt
+the data format of the filter's output.
+
+It accepts the following values:
+@table @samp
+@item gbrpf32le
+force gbrpf32le output
+
+@item gbrp10le
+force gbrp10le output
+@end table
+
+Default value is @samp{gbrpf32le}.
+
+@end table
+
 @anchor{selectivecolor}
 @section selectivecolor
 
diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index a7ebd02..7ad8250 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -366,6 +366,7 @@  OBJS-$(CONFIG_SOBEL_OPENCL_FILTER)           += vf_convolution_opencl.o opencl.o
 OBJS-$(CONFIG_SPLIT_FILTER)                  += split.o
 OBJS-$(CONFIG_SPP_FILTER)                    += vf_spp.o
 OBJS-$(CONFIG_SR_FILTER)                     += vf_sr.o
+OBJS-$(CONFIG_SDR2HDR_FILTER)                += vf_sdr2hdr.o
 OBJS-$(CONFIG_SSIM_FILTER)                   += vf_ssim.o framesync.o
 OBJS-$(CONFIG_STEREO3D_FILTER)               += vf_stereo3d.o
 OBJS-$(CONFIG_STREAMSELECT_FILTER)           += f_streamselect.o framesync.o
diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
index 484b080..622f9f3 100644
--- a/libavfilter/allfilters.c
+++ b/libavfilter/allfilters.c
@@ -322,6 +322,7 @@  extern AVFilter ff_vf_scale_npp;
 extern AVFilter ff_vf_scale_qsv;
 extern AVFilter ff_vf_scale_vaapi;
 extern AVFilter ff_vf_scale2ref;
+extern AVFilter ff_vf_sdr2hdr;
 extern AVFilter ff_vf_select;
 extern AVFilter ff_vf_selectivecolor;
 extern AVFilter ff_vf_sendcmd;
diff --git a/libavfilter/vf_sdr2hdr.c b/libavfilter/vf_sdr2hdr.c
new file mode 100644
index 0000000..fcee404
--- /dev/null
+++ b/libavfilter/vf_sdr2hdr.c
@@ -0,0 +1,270 @@ 
+/*
+ * Copyright (c) 2018 Guo Yejun
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 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
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser 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
+ */
+
+/**
+ * @file
+ * Filter implementing HDR image generation from a single exposure using deep CNNs.
+ * https://arxiv.org/pdf/1710.07480.pdf
+ */
+
+#include "avfilter.h"
+#include "formats.h"
+#include "internal.h"
+#include "libavutil/opt.h"
+#include "libavutil/qsort.h"
+#include "libavformat/avio.h"
+#include "libswscale/swscale.h"
+#include "dnn_interface.h"
+#include <math.h>
+
+typedef struct SDR2HDRContext {
+    const AVClass *class;
+
+    char* model_filename;
+    enum AVPixelFormat out_fmt;
+    DNNModule* dnn_module;
+    DNNModel* model;
+    DNNData input, output;
+} SDR2HDRContext;
+
+#define OFFSET(x) offsetof(SDR2HDRContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
+static const AVOption sdr2hdr_options[] = {
+    { "model_filename", "path to model file specifying network architecture and its parameters", OFFSET(model_filename), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS },
+    { "out_fmt", "the data format of the filter's output, it could be gbrpf32le [default] or gbrp10le", OFFSET(out_fmt), AV_OPT_TYPE_PIXEL_FMT, {.i64=AV_PIX_FMT_GBRPF32LE}, AV_PIX_FMT_NONE, AV_PIX_FMT_NB - 1, FLAGS },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(sdr2hdr);
+
+static av_cold int init(AVFilterContext* context)
+{
+    SDR2HDRContext* ctx = context->priv;
+
+    if (ctx->out_fmt != AV_PIX_FMT_GBRPF32LE && ctx->out_fmt != AV_PIX_FMT_GBRP10LE) {
+        av_log(context, AV_LOG_ERROR, "could not support the output format\n");
+        return AVERROR(ENOSYS);
+    }
+
+    ctx->dnn_module = ff_get_dnn_module(DNN_TF);
+    if (!ctx->dnn_module){
+        av_log(context, AV_LOG_ERROR, "could not create DNN module for tensorflow backend\n");
+        return AVERROR(ENOMEM);
+    }
+    if (!ctx->model_filename){
+        av_log(context, AV_LOG_ERROR, "model file for network was not specified\n");
+        return AVERROR(EIO);
+    }
+    if (!ctx->dnn_module->load_model) {
+        av_log(context, AV_LOG_ERROR, "load_model for network was not specified\n");
+        return AVERROR(EIO);
+    }
+    ctx->model = (ctx->dnn_module->load_model)(ctx->model_filename);
+    if (!ctx->model){
+        av_log(context, AV_LOG_ERROR, "could not load DNN model\n");
+        return AVERROR(EIO);
+    }
+    return 0;
+}
+
+static int query_formats(AVFilterContext* context)
+{
+    const enum AVPixelFormat in_formats[] = {AV_PIX_FMT_RGB24,
+                                             AV_PIX_FMT_NONE};
+    enum AVPixelFormat out_formats[2];
+    SDR2HDRContext* ctx = context->priv;
+    AVFilterFormats* formats_list;
+    int ret = 0;
+
+    formats_list = ff_make_format_list(in_formats);
+    if ((ret = ff_formats_ref(formats_list, &context->inputs[0]->out_formats)) < 0)
+        return ret;
+
+    out_formats[0] = ctx->out_fmt;
+    out_formats[1] = AV_PIX_FMT_NONE;
+    formats_list = ff_make_format_list(out_formats);
+    if ((ret = ff_formats_ref(formats_list, &context->outputs[0]->in_formats)) < 0)
+        return ret;
+
+    return 0;
+}
+
+static int config_props(AVFilterLink* inlink)
+{
+    AVFilterContext* context = inlink->dst;
+    SDR2HDRContext* ctx = context->priv;
+    AVFilterLink* outlink = context->outputs[0];
+    DNNReturnType result;
+
+    // the dnn model is tied with resolution due to deconv layer of tensorflow
+    // now just support 1920*1080 and so the magic numbers within this file
+    if (inlink->w != 1920 || inlink->h != 1080) {
+        av_log(context, AV_LOG_ERROR, "only support frame size with 1920*1080\n");
+        return AVERROR(ENOSYS);
+     }
+
+    ctx->input.width = inlink->w;
+    ctx->input.height = FFALIGN(inlink->h, 32);  //the model requires height is a multiple of 32,
+    ctx->input.channels = 3;
+
+    result = (ctx->model->set_input_output)(ctx->model->model, &ctx->input, &ctx->output);
+    if (result != DNN_SUCCESS){
+        av_log(context, AV_LOG_ERROR, "could not set input and output for the model\n");
+        return AVERROR(EIO);
+    }
+
+    memset(ctx->input.data, 0, ctx->input.channels * ctx->input.width * ctx->input.height * sizeof(float));
+    outlink->h = inlink->h;
+    outlink->w = inlink->w;
+    return 0;
+}
+
+static float qsort_comparison_function_float(const void *a, const void *b)
+{
+    return *(const float *)a - *(const float *)b;
+}
+
+static int filter_frame(AVFilterLink* inlink, AVFrame* in)
+{
+    DNNReturnType dnn_result = DNN_SUCCESS;
+    AVFilterContext* context = inlink->dst;
+    SDR2HDRContext* ctx = context->priv;
+    AVFilterLink* outlink = context->outputs[0];
+    AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
+    int total_pixels = in->height * in->width;
+
+    if (!out){
+        av_log(context, AV_LOG_ERROR, "could not allocate memory for output frame\n");
+        av_frame_free(&in);
+        return AVERROR(ENOMEM);
+    }
+
+    av_frame_copy_props(out, in);
+
+    for (int i = 0; i < in->linesize[0] * in->height; ++i) {
+        ctx->input.data[i] = in->data[0][i] / 255.0f;
+    }
+
+    dnn_result = (ctx->dnn_module->execute_model)(ctx->model);
+    if (dnn_result != DNN_SUCCESS){
+        av_log(context, AV_LOG_ERROR, "failed to execute loaded model\n");
+        return AVERROR(EIO);
+    }
+
+    if (ctx->out_fmt == AV_PIX_FMT_GBRPF32LE) {
+        float* outg = (float*)out->data[0];
+        float* outb = (float*)out->data[1];
+        float* outr = (float*)out->data[2];
+        for (int i = 0; i < total_pixels; ++i) {
+            float r = ctx->output.data[i*3];
+            float g = ctx->output.data[i*3+1];
+            float b = ctx->output.data[i*3+2];
+            outr[i] = r;
+            outg[i] = g;
+            outb[i] = b;
+        }
+    } else if (ctx->out_fmt == AV_PIX_FMT_GBRP10LE) {
+        // here, we just use a rough mapping to the 10bit contents.
+        // meta data generation for HDR video encoding is not supported yet
+        float* converted_data = (float*)av_malloc(total_pixels * 3 * sizeof(float));
+        int16_t* outg = (int16_t*)out->data[0];
+        int16_t* outb = (int16_t*)out->data[1];
+        int16_t* outr = (int16_t*)out->data[2];
+
+        float max = 1.0f;
+        for (int i = 0; i < total_pixels * 3; ++i) {
+            float d = ctx->output.data[i];
+            d = sqrt(d);
+            converted_data[i] = d;
+            max = FFMAX(d, max);
+        }
+
+        if (max > 1.0f) {
+            AV_QSORT(converted_data, total_pixels * 3, float, qsort_comparison_function_float);
+            // 0.5% pixels are clipped
+            max = converted_data[(int)(total_pixels * 3 * 0.995)];
+            max = FFMAX(max, 1.0f);
+
+            for (int i = 0; i < total_pixels * 3; ++i) {
+                float d = ctx->output.data[i];
+                d = sqrt(d);
+                d = FFMIN(d, max);
+                converted_data[i] = d;
+            }
+        }
+
+        for (int i = 0; i < total_pixels; ++i) {
+            float r = converted_data[i*3];
+            float g = converted_data[i*3+1];
+            float b = converted_data[i*3+2];
+            outr[i] = r / max * 1023;
+            outg[i] = g / max * 1023;
+            outb[i] = b / max * 1023;
+        }
+
+        av_free(converted_data);
+    } else {
+        assert(!"should not reach here");
+    }
+
+    av_frame_free(&in);
+    return ff_filter_frame(outlink, out);
+}
+
+static av_cold void uninit(AVFilterContext* context)
+{
+    SDR2HDRContext* ctx = context->priv;
+
+    if (ctx->dnn_module){
+        (ctx->dnn_module->free_model)(&ctx->model);
+        av_freep(&ctx->dnn_module);
+    }
+}
+
+static const AVFilterPad sdr2hdr_inputs[] = {
+    {
+        .name         = "default",
+        .type         = AVMEDIA_TYPE_VIDEO,
+        .config_props = config_props,
+        .filter_frame = filter_frame,
+    },
+    { NULL }
+};
+
+static const AVFilterPad sdr2hdr_outputs[] = {
+    {
+        .name = "default",
+        .type = AVMEDIA_TYPE_VIDEO,
+    },
+    { NULL }
+};
+
+AVFilter ff_vf_sdr2hdr = {
+    .name          = "sdr2hdr",
+    .description   = NULL_IF_CONFIG_SMALL("HDR image generation from a single exposure using deep CNNs."),
+    .priv_size     = sizeof(SDR2HDRContext),
+    .init          = init,
+    .uninit        = uninit,
+    .query_formats = query_formats,
+    .inputs        = sdr2hdr_inputs,
+    .outputs       = sdr2hdr_outputs,
+    .priv_class    = &sdr2hdr_class,
+    .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
+};