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 ΔPSNR 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. https://doi.org/10.1109/TCSVT.2020.3018230
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. Article 9171884. https://doi.org/10.1109/TCSVT.2020.3018230
@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 ΔPSNR 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 = "2100--2113",
journal = "IEEE Transactions on Circuits and Systems for Video Technology",
issn = "1051-8215",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",
}