HVASR: Enhancing 360-degree video delivery with viewport-aware super resolution

IF 8.1 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Sciences Pub Date : 2024-11-04 DOI:10.1016/j.ins.2024.121609
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Abstract

In recent years, 360-degree videos have gained significant traction due to their capacity to provide immersive experiences. However, the adoption of 360-degree videos substantially escalates bandwidth demands, necessitating approximately four to ten times more bandwidth than traditional video formats do. This presents a considerable challenge in maintaining high-quality videos in environments characterized by limited bandwidth or unstable networks. A trend has emerged where client-side computational power and deep neural networks are employed to enhance video quality while mitigating bandwidth requirements within contemporary video delivery systems. These approaches segment a video into discrete chunks and apply super resolution (SR) models to each segment, streaming low-resolution (LR) chunks alongside their corresponding SR models to the client. Although these methods enhance both video quality and transmission efficiency for conventional videos, they impose greater computational resource demands when applied to 360-degree content, thereby constraining widespread implementation. This paper introduces an innovative method called HVASR for 360-degree videos that leverages viewport information for more precise segmentation and minimizes model training costs as well as bandwidth requirements. Additionally, HVASR incorporates a viewport-aware training strategy that is aimed at further enhancing performance while reducing computational expenses. The experimental results demonstrate that HVASR achieves an average utility increase ranging from 12.46% to 40.89% across various scenes.
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HVASR:利用视口感知超级分辨率加强 360 度视频传输
近年来,360 度视频因其能够提供身临其境的体验而大受欢迎。然而,360 度视频的采用大大提高了带宽需求,所需的带宽大约是传统视频格式的四到十倍。这给在带宽有限或网络不稳定的环境中保持高质量视频带来了巨大挑战。目前出现了一种趋势,即在当代视频传输系统中,利用客户端计算能力和深度神经网络来提高视频质量,同时降低带宽要求。这些方法将视频分割成离散的片段,并对每个片段应用超分辨率(SR)模型,将低分辨率(LR)片段与其相应的 SR 模型一起流式传输到客户端。虽然这些方法提高了传统视频的视频质量和传输效率,但在应用于 360 度内容时,对计算资源提出了更高的要求,从而限制了其广泛应用。本文介绍了一种用于 360 度视频的创新方法 HVASR,该方法利用视口信息进行更精确的分割,并最大限度地降低了模型训练成本和带宽需求。此外,HVASR 还采用了视口感知训练策略,旨在进一步提高性能,同时降低计算成本。实验结果表明,HVASR 在各种场景中实现了 12.46% 到 40.89% 的平均效用提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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