@inproceedings{9e80e183bec44b1eb47e8b0aafc2b27f,
title = "ViSTRA3: Video coding with deep parameter adaptation and post processing",
abstract = "This paper presents a deep learning-based video compression framework (ViSTRA3), which has been employed to generate compression results for the ISCAS 2022 Grand Challenge on Neural Network-based Video Coding. The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the decoder to restore their original format and enhance reconstruction quality. ViSTRA3 has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under the Joint Video Exploration Team Common Test Conditions. Bj{\o}negaard Delta (BD) measurement results show that the proposed framework consistently outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7% based on the assessment of PSNR and VMAF.",
keywords = "video coding, three-dimensional displays, circuits and systems, video compression, feature extraction, encoding, decoding",
author = "Chen Feng and Duolikun Danier and Charlie Tan and Fan Zhang and David Bull",
year = "2022",
month = nov,
day = "11",
doi = "10.48550/arXiv.2111.15536",
language = "English",
isbn = "9781665484862",
series = "Proceedings of the IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "824--828",
booktitle = "2022 IEEE International Symposium on Circuits and Systems",
address = "United States",
note = "2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 ; Conference date: 28-05-2022 Through 01-06-2022",
}