CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

doi:10.1109/TCSVT.2021.3123621

	CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement
	Zhao, Hengrun 1; Zheng, Bolun 1; Yuan, Shanxin 2; Zhang, Hua 3; Yan, Chenggang 1; Li, Liang 4; Slabaugh, Gregory 5
	2022-07-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
卷号	32 期号:7 页码:4138-4149
摘要	Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.
关键词	Image coding Quantization (signal) Streaming media Bit rate Image restoration Transform coding Video recording Quality enhancement CBR compressed video dual-domain restoration
DOI	10.1109/TCSVT.2021.3123621
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFB1406604] ; National Nature Science Foundation of China[62001146] ; National Nature Science Foundation of China[61931008] ; National Nature Science Foundation of China[61671196] ; National Nature Science Foundation of China[61701149] ; National Nature Science Foundation of China[61801157] ; National Nature Science Foundation of China[61971268] ; National Nature Science Foundation of China[61901145] ; National Nature Science Foundation of China[61901150] ; National Nature Science Foundation of China[61972123] ; National Natural Science Major Foundation of Research Instrumentation of PR China[61427808] ; Zhejiang Province Nature Science Foundation of China[LR17F030006] ; Zhejiang Province Nature Science Foundation of China[Q19F010030] ; 111 Project[D17019]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000819817700006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19521
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zheng, Bolun; Zhang, Hua
作者单位	1.Hangzhou Dianzi Univ, Sch Automat, Hangzhou 311305, Peoples R China 2.Huawei Technol, Noahs Ark Lab, London N1C 4AG, England 3.Hangzhou Dianzi Univ, Sch Comp Sci, Hangzhou 311305, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100049, Peoples R China 5.Queen Mary Univ London, Digital Environm Res Inst DERI, London E1 4NS, England
推荐引用方式 GB/T 7714	Zhao, Hengrun,Zheng, Bolun,Yuan, Shanxin,et al. CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(7):4138-4149.
APA	Zhao, Hengrun.,Zheng, Bolun.,Yuan, Shanxin.,Zhang, Hua.,Yan, Chenggang.,...&Slabaugh, Gregory.(2022).CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(7),4138-4149.
MLA	Zhao, Hengrun,et al."CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.7(2022):4138-4149.