The paper proposes a novel frame-wise filtering method based on Convolutional Neural Networks (CNNs) for enhancing the quality of HEVC decoded videos. A novel deep neural network architecture is proposed for post-filtering the entire intra-coded videos. A novel scheme utilizing frame-size patches is employed for training the network. The proposed method filters the luminance channel separately from the pair of chrominance channels. A novel patch generation paradigm is proposed where, for each color channel, the corresponding mode map is generated based on the HEVC intra-prediction mode index and block segmentation. The proposed CNN-based filtering method is an alternative to the traditional HEVC built-in in-loop filtering module for intra-coded frames. Experimental results on standard test sequences show that the proposed method outperforms the HEVC standard with average BD-rate savings of 11.1% and an average BD-PSNR improvement of 0.602 dB. The average relative improvement in ?PSNR is around 105% at QP=42 and around 85% at QP=32 compared with state-of-the-art machine-learning-based methods.
Huang, H, Schiopu, I & Munteanu, A 2020, ' Frame-wise CNN-based Filtering for Intra-Frame Quality Enhancement of HEVC Videos ', IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 6, 9171884, pp. 2100-2113.
Huang, H., Schiopu, I. , & Munteanu, A. (2020). Frame-wise CNN-based Filtering for Intra-Frame Quality Enhancement of HEVC Videos . IEEE Transactions on Circuits and Systems for Video Technology , 31 (6), 2100-2113. [9171884].
@article{57bdb887be6c4c8ca010bfe14e7760bf,
title = " Frame-wise CNN-based Filtering for Intra-Frame Quality Enhancement of HEVC Videos " ,
abstract = " The paper proposes a novel frame-wise filtering method based on Convolutional Neural Networks (CNNs) for enhancing the quality of HEVC decoded videos. A novel deep neural network architecture is proposed for post-filtering the entire intra-coded videos. A novel scheme utilizing frame-size patches is employed for training the network. The proposed method filters the luminance channel separately from the pair of chrominance channels. A novel patch generation paradigm is proposed where, for each color channel, the corresponding mode map is generated based on the HEVC intra-prediction mode index and block segmentation. The proposed CNN-based filtering method is an alternative to the traditional HEVC built-in in-loop filtering module for intra-coded frames. Experimental results on standard test sequences show that the proposed method outperforms the HEVC standard with average BD-rate savings of 11.1% and an average BD-PSNR improvement of 0.602 dB. The average relative improvement in ?PSNR is around 105% at QP=42 and around 85% at QP=32 compared with state-of-the-art machine-learning-based methods. " ,
keywords = " Deep-learning, Quality Enhancement, Video Coding " ,
author = " Hongyue Huang and Ionut Schiopu and Adrian Munteanu " ,
year = " 2020 " ,
month = aug,
day = " 20 " ,
doi = " 10.1109/TCSVT.2020.3018230 " ,
language = " English " ,
volume = " 31 " ,
pages = " 21002113 " ,
journal = " IEEE Transactions on Circuits and Systems for Video Technology " ,
issn = " 1051-8215 " ,
publisher = " Institute of Electrical and Electronics Engineers Inc. " ,
number = " 6 " ,
}