Down-Sampling Based Video Coding with Degradation-aware Restoration-Reconstruction Deep Neural Network (TIP'2021 - MMM'2020)
Man M. Ho, Gang He, Zheng Wang, and Jinjia Zhou.
| Date | News |
|---|---|
| 2021/01/10 | Our extended work has been accepted to and published in TIP'2021 (Open Access) |
| 2020/07/05 | We have updated the evaluation code for RR-DnCNN and RR-DnCNN v2.0 |
| 2020/01/12 | Our RR-DnCNN will be published together with its version 2.0 at once |
| 2020/01/07 | We won Best Paper Runner-up Award at MMM'2020 |
| 2019/09/24 | Our work has been accepted to MMM'2020 (Oral) |
Down-Sampling Based Video Coding with Degradation-aware Restoration-Reconstruction Deep Neural Network
Minh-Man Ho, Gang He, Zheng Wang, and Jinjia Zhou
In International Conference on Multimedia Modeling (MMM), 2020.
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding
(Open Access)
Man M. Ho, Jinjia Zhou, and Gang He
In IEEE Transactions on Image Processing (TIP), 2021.
- Ubuntu 16.04
- OpenCV
- PyTorch >= 1.1.0
- Numpy
Let's reproduce our result.
git clone https://github.com/minhmanho/rrdncnn.git
cd rrdncnn
Since the files are large, URLs here need your confirmation; otherwise, just run the command lines with recursion '-r'.
+ Johnny 1280x720 in Class E (65 MB) compressed using Low Delay P (recommended).
wget --no-check-certificate -r 'https://docs.google.com/uc?export=download&id=1PZWTdTkMxLpaSTJ4A7Xxw2QK6tmYy-d9' -O ./data/class_e.zip
unzip -o ./data/class_e.zip -d ./data/
or
./data/download_class_e.sh
+ BasketballDrill 832x480 in Class C (384 MB) compressed using All Intra (optional).
wget --no-check-certificate -r 'https://docs.google.com/uc?export=download&id=1GsnzTPGEVS8v-aMfQyrKExqKx-jZP7uP' -O ./data/class_c.zip
unzip -o ./data/class_c.zip -d ./data/
or
./data/download_class_c.sh
The expected data structure (for Johnny 1280x720 compressed by HEVC 16.20 with Low Delay P - LDP) is as:
./data/class_e
│ DHR_LDP.csv (HEVC on HR as <video_name>,<bir-rate kbps>, <Y-PSNR>, <U-PSNR>, <V-PSNR>, <YUV-PSNR>)
│ DLR_LDP.csv (HEVC on LR as <video_name>,<bir-rate kbps>, <Y-PSNR>, <U-PSNR>, <V-PSNR>, <YUV-PSNR>)
│ DHR_LDP_QP[32-47].txt (HEVC running logs)
| DLR_LDP_QP[32-47].txt (HEVC running logs)
|
└───DLR_LDP_QP[32-47] (Decoded Low-Resolution)
│ │ Johnny_1280x720_50.yuv
|
└───HR (Uncompressed High-Resolution)
│ │ Johnny_1280x720_50.yuv
|
└───LR (Uncompressed Low-Resolution down-sampled x2 by bicubic)
│ │ Johnny_1280x720_50.yuv
CUDA_VISIBLE_DEVICES=0 python eval.py --vin <path/to/class/folder> --ckpt <path/to/model> --size <video_size HxW> --decoder_folder <folder/contains/videos> --log_dir ./logs/
For example:
+ Run with RR-DnCNN.
CUDA_VISIBLE_DEVICES=0 python eval.py --vin ./data/class_e/ --ckpt ./models/rrdncnn.pth.tar --size 360x640 --decoder_folder DLR_LDP_QP32
+ Run with RR-DnCNN v2.0.
CUDA_VISIBLE_DEVICES=0 python eval.py --vin ./data/class_e/ --ckpt ./models/rrdncnn_v2.pth.tar --size 360x640 --decoder_folder DLR_LDP_QP32
+ Run with our prepared script.
./run.sh
After running, you will get the results as follows:
+ <path/to/class/folder> or <args.out>/<model>_SDLR_<configuration>_<QP> (Folder contains reconstructed videos)
+ <args.log_dir>/<configuration>_<model>.csv (CSV file contains quantitative results)
Beside printing, we also export the results in CSV to log_dir for copying/pasting, where the columns (left-to-right) in the CSV file represent:
video_name_QP, HR-Y-PSNR, HR-Y-SSIM, LR-Y-PSNR, LR-Y-SSIM
E.g.,
rrdncnn_Johnny_1280x720_50_DLR_LDP_QP32,35.0897,0.9118,38.2423,0.9594
Finally, we calculate the BD-rates based on the results of HEVC (attached in the class package) and our methods. The expected output on Johnny (LDP) and BasketballDrill (AI) should be:
| Sequence_QP | HEVC 16.20 | Low Bit-rate (kbps) | RR-DnCNN | RR-DnCNN v2.0 | |||
|---|---|---|---|---|---|---|---|
| Bit-rate (kbps) | PSNR (dB) | PSNR | BD-BR (%) | PSNR (dB) | BD-BR (%) | ||
| Johnny_1280x720_50.yuv_32 | 87.7059 | 38.9999 | 34.4353 | 35.0897 | -12.9307 | 35.4468 | -15.8121 |
| Johnny_1280x720_50.yuv_37 | 46.9529 | 36.7226 | 18.9812 | 33.4981 | 33.6468 | ||
| Johnny_1280x720_50.yuv_42 | 25.5059 | 34.1018 | 11.4706 | 31.2232 | 31.3090 | ||
| Johnny_1280x720_50.yuv_47 | 14.9129 | 31.5612 | 7.0847 | 28.7739 | 28.8354 | ||
| BasketballDrill_832x480_50.yuv_32 | 1833.6642 | 35.4191 | 685.0295 | 31.0428 | -10.9197 | 31.0627 | -12.4616 |
| BasketballDrill_832x480_50.yuv_37 | 1018.0177 | 32.7758 | 392.1459 | 29.2932 | 29.365 | ||
| BasketballDrill_832x480_50.yuv_42 | 559.657 | 30.1624 | 208.8544 | 26.9593 | 27.0212 | ||
| BasketballDrill_832x480_50.yuv_47 | 277.0484 | 27.5161 | 97.052 | 24.7342 | 24.7411 | ||
Let's check our pre-run version (26 KB) for more details.
wget 'https://docs.google.com/uc?export=download&id=1VkiMalZp_UxejxA3ScoE7fzDDZoWACFc' -O ./logs/result_sample.zip
unzip -o ./logs/result_sample.zip -d ./logs/
or
./logs/download_result_sample.sh
Please cite this work if you find it useful.
@article{ho2021rr,
title={RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling-Based Video Coding},
author={Ho, Man M and Zhou, Jinjia and He, Gang},
journal={IEEE Transactions on Image Processing},
volume={30},
pages={1702--1715},
year={2021},
publisher={IEEE}
}
This repository (as well as its materials) is for non-commercial uses and research purposes only.
Thank G. Bjontegaard and S. Pateux for ETRO's Bjontegaard Metric Implementation.
G. Bjontegaard, Calculation of average PSNR differences between RD-curves (VCEG-M33)
S. Pateux, J. Jung, An excel add-in for computing Bjontegaard metric and its evolution
This work is supported by JST, PRESTO Grant Number JPMJPR1757 Japan.
If you have any questions, or the use of material violates your copyright / license, please contact me [email protected].
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