hexiaoyi95 / Partition-aware

Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC
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Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC

This repository releases the test code for our paper

Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC (TMM 2020)

Weiyao Lin, Xiaoyi He, Xintong Han, Dong Liu, John See, Junni Zou, Hongkai Xiong, Feng Wu

Preparation

  1. Clone this repository and install the necessary python packages
    
    git clone https://github.com/hexiaoyi95/Partition-aware

cd Partition-aware

visit the requirements.txt for the necessary packages for python2.7

pip install -r requirements.txt


2. Prepare test sequences. We provide an example on [One Drive](https://1drv.ms/u/s!AhjTb_4JKsIDiS5mwv5HTU36-k-F?e=x6OeAd). The original yuv sequences and compressed sequences are put into two different directories. If the original yuv sequence is named as *seq.yuv*, please name the compressed sequence at QP=37 as *seq_QP37.yuv*.

## Deploy a pre-trained model

- for yuv input post-processing:
```Shell
usage: inference.py [-h] [--QP QP] [--checkpoint CHECKPOINT]
                    [--test_num TEST_NUM] [--info INFO]
                    [--recYuv_path RECYUV_PATH] [--origYuv_path ORIGYUV_PATH]
                    [--patch_size PATCH_SIZE] [--Yonly]

optional arguments:
  -h, --help            show this help message and exit
  --QP QP, -q QP        test QP value
  --checkpoint CHECKPOINT, -c CHECKPOINT
                        checkpoint to be evaluted
  --test_num TEST_NUM, -n TEST_NUM
                        test frames number, default is 32
  --info INFO           output json filename
  --recYuv_path RECYUV_PATH
                        reconstructed yuv dir
  --origYuv_path ORIGYUV_PATH
                        original yuv dir
  --patch_size PATCH_SIZE
                        patch_size, default is 64
  --Yonly               only test Y channel if specified

Released model

We released models for our partition-aware network and VRCNN+partition(i.e., 2-in+MM+AF and VRCNN+MM+AF in our paper) trained at QP=37 on One Drive

Citation

If you think this work is helpful for your own research, please consider add following bibtex config in your latex file

@article{lin2020partition,
  title={Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC},
  author={Lin, Weiyao and He, Xiaoyi and Han, Xintong and Liu, Dong and John, See and Zou, Junni and Xiong, Hongkai and Wu, Feng},
  journal={IEEE Transaction on Multimedia},
  doi={10.1109/TMM.2019.2962310},
  year={2020},
  organization={IEEE}
}