wangp-blog/EDCNN

Efficient In-loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

The in-loop filtering in HEVC

We propose an efficient in-loop filtering algorithm based on the enhanced deep convolutional neural networks (EDCNN) for significantly improving the performance of in-loop filtering in HEVC. the EDCNN is proposed for efficiently eliminating the artifacts, which adopts three solutions, including a weighted normalization method, a feature information fusion block, and a precise loss function.

Our proposed EDCNN

1. The structure of proposed feature information fusion block

2. The architecture of proposed EDCNN

3. The detailed network parameters

Experimental Results

1. The PSNR standard deviations of Low-Delay coding structure

2. The PSNR standard deviations of Random-Access coding structure

3. Video subjective quality comparison

Test instruction using pre-trained model

We have listed our pre-trained model of EDCNN module, if you want to compare our method, you can substitute the trained EDCNN model in your method. The pre-trained model is placed in weights/edcnn.

python3 predict.py --model [pretrained model] --dir_demo [demo images directory] --save_name [directory to save] --pre_train [weightfile]

Arguments

n_threads: number of threads for data loading
cpu: use cpu only
dir_demo: demo image directory
model: model name
pre_train: pretrained model directory
save_name: directory to save

Citation

Z. Pan, X. Yi, Y. Zhang, B. Jeon and S. Kwong, "Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC," in IEEE Transactions on Image Processing, vol. 29, pp. 5352-5366, 2020

wangp-blog / EDCNN