• DVC_P
• • our contibutions
  • environment
  • command
  • train
  • test
  • mindspore performance
  • source
  • citation
  • contributors

DVC_P

《DVC-P: Deep Video Compression with Perceptual Optimizations》

This work is based on OpenDVC (an open source Tensorflow implementation of DVC), but improves it with perceptual optimizations (i.e., a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate, and nearest-neighbor interpolation is used to eliminate checkerboard artifacts).

our contibutions

1.translate from tensorflow to mindspore

2.successfully run forward, backward and parameter update

3.test performance in mindspore version

environment

• mindspore-dev==2.0.0dev20230109
• ubuntu 16.04
• cuda 11.1
• python 3.7

command

please intall compressai via:

cd ./CompressAI_MindSpore
pip install -e .

train

Our train experiments are conducted on Vimeo-90k dataset.

python train.py --alpha 256/512/1024/2048

test

Our test experiments are conducted on UVG test dataset.

python run_test.py

mindspore performance

The results of different bitrate models on UVG test dataset:

lambda	bpp	PSNR	MSSSIM	time_cost	GPU M
256	0.109	33.897	0.924	4883	8370
512	0.136	35.104	0.937	4989	8370
1024	0.175	36.284	0.947	5082	8370
2048	0.254	37.157	0.955	4968	8370

source

translated from tensorflow

link: DVC_P-tensorflow

citation

@article{DVC-P,
  title={DVC-P: Deep Video Compression with Perceptual Optimizations},
  author={Saiping Zhang and Marta Mrak and Luis Herranz and Marc Gorriz Blanch and Shuai Wan and Fuzheng Yang},
  journal={arXiv preprint arXiv:2109.10849},
  year={2021}
}

contributors

name: Kaiyu Zheng, Yongchi Zhang