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《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).
1.translate from tensorflow to mindspore
2.successfully run forward, backward and parameter update
3.test performance in mindspore version
please intall compressai via:
cd ./CompressAI_MindSpore
pip install -e .
Our train experiments are conducted on Vimeo-90k dataset.
python train.py --alpha 256/512/1024/2048
Our test experiments are conducted on UVG test dataset.
python run_test.py
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 |
translated from tensorflow
link: DVC_P-tensorflow
@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}
}
name: Kaiyu Zheng, Yongchi Zhang
Unofficial mindspore implementation of DVC_P
Python Shell
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