Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter In-Loop Filtering

doi:10.1109/TCSVT.2024.3473543

	Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter In-Loop Filtering
	Li, Mingxuan 1,2; Ji, Wen 1,3
	2025-02-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
卷号	35 期号:2 页码:1870-1883
摘要	Many studies have focused on utilizing convolutional neural networks (CNNs) to enhance loop filter performance in video encoding. However, existing methods primarily concentrate on improving the natural sequence quality rather than addressing the specific needs of screen content sequences, which have gained increased attention due to the growing demands of remote desktops and online meetings. This paper proposed to understand machine behavior from the machine's point of view, and adopts the machine intelligence to screen content coding. It presents a novel loop filter specifically tailored for screen content coding (SCC), referred to as video coding-SCC (VC-SCC). It employs a multiscale feature extraction structure and introduces two innovative non-local models to address distortions in different frame types across various coding setups. Specifically, considering regions of text and graphic textures in screen content, three types of prior maps, including screen content maps, coding configuration maps, and traditional filtering maps, are designed as auxiliary information in the model, promoting distortion pattern learning under different configurations. Two novel non-local models are proposed to enhance the model's ability to capture global features in intra- and inter-frames while keeping low computational complexity. Finally, the VC-SCC is proposed for parallel implementation with the standard in-loop filter, and the optimal results are selected in each patch. Experimental results demonstrate significant performance improvements, with average BD-rate savings of 9.93%, 11.05%, and 10.73% for the all-intra(AI), low-delay(LD), and random-access(RA) configurations, respectively, outperforming other state-of-the-art approaches.
关键词	Encoding Feature extraction Computational modeling Adaptation models Visualization Nonlinear distortion Information filters Deep learning Standards Image color analysis Video coding in-loop filtering screen content deep learning high-efficiency video coding (HEVC)
DOI	10.1109/TCSVT.2024.3473543
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62072440] ; Beijing Natural Science Foundation[L221004] ; National Key Research and Development Program of China[2023YFB4502805]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001422045800046
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40739
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ji, Wen
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 3.LonganPi Intelligent Informat Technol Co Ltd, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Li, Mingxuan,Ji, Wen. Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter In-Loop Filtering[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2025,35(2):1870-1883.
APA	Li, Mingxuan,&Ji, Wen.(2025).Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter In-Loop Filtering.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,35(2),1870-1883.
MLA	Li, Mingxuan,et al."Screen Content-Aware Video Coding Through Non-Local Model Embedded With Intra-Inter In-Loop Filtering".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35.2(2025):1870-1883.