基于质量依赖的VVC互编码的速率分配模型

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Heqiang Wang , Xuekai Wei , Mingliang Zhou , Horace Ho Shing Ip , Sam Kwong
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引用次数: 0

摘要

本文提出了一种用于通用视频编码(VVC)中图像组(GOP)级和帧级速率控制的速率分配方案。首先,通过统计分析周期内gop之间的质量依赖链,研究了gop级质量依赖因子(GQDF)对VVC互编码结构的影响。其次,考虑到帧级的分层和块不一致的质量依赖,我们通过将失真分解为跳过和非跳过组件并分析每个部分的依赖关系来建模帧级质量依赖因子(FQDF)。最后,利用拟议的GQDF和FQDF,将统一的费率分配方案无缝集成到现有的VVC费率控制框架中。该方法在最新的参考软件中实现。实验表明,与现有的方法相比,我们的方法达到了理想的压缩效率和视觉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A rate allocation model for VVC intercoding using a quality dependency
In this paper, we propose a rate allocation scheme for rate control at both the group-of-pictures (GOP) level and the frame level in versatile video coding (VVC). First, by statistically analysing the quality dependency chain among GOPs in an intraperiod, a GOP-level quality dependency factor (GQDF) is investigated for the intercoding structure in VVC. Second, considering the hierarchical and block-inconsistent quality dependency at the frame level, we model a frame-level quality dependency factor (FQDF) by decomposing distortion into the skip and nonskip components and analysing the dependency of each part. Finally, a unified rate allocation scheme, leveraging the proposed GQDF and FQDF, is seamlessly integrated into the existing VVC rate control framework. The proposed method is implemented within the newest reference software. Experiments suggest that our method achieves desirable compression efficiency and visual quality compared with state-of-the-art methods.
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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