BiSR:用于移动视频流的双向优化超分辨率

Q. Yu, Qing Li, Rui He, Gareth Tyson, Wanxin Shi, Jianhui Lv, Zhenhui Yuan, Peng Zhang, Yulong Lan, Zhicheng Li
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引用次数: 1

摘要

移动web视频流的用户体验经常受到网络带宽不足和动态的影响。本文设计了双向优化的超分辨率(BiSR),以提高有限带宽下移动web用户的体验质量(QoE)。BiSR利用基于深度神经网络(DNN)的模型在不改变帧间时空信息的情况下有效地超分辨关键帧。然后,我们提出了一个缩小的深度神经网络和一个针对移动设备优化的轻量级超分辨率深度神经网络来提高性能。最后,提出了一种新的基于强化学习的自适应比特率(ABR)算法,在实际网络轨迹上验证了BiSR算法的性能。我们的评估,使用完整的系统实现,表明BiSR比传统的H.264编解码器节省了26%的比特率,并将视频的SSIM比现有技术提高了3.7%。总体而言,BiSR将用户感知的体验质量提高了30.6%。
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BiSR: Bidirectionally Optimized Super-Resolution for Mobile Video Streaming
The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.
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