基于视觉信息保真度构建每帧码率阶梯
Constructing Per-Shot Bitrate Ladders using Visual Information Fidelity
AI总结：
本文提出基于视觉信息保真度构建每帧码率阶梯的方法，通过低级特征和VIF特征优化，实现高质量低码率视频传输，提升带宽效率和用户体验。
摘要：
视频服务提供商需要其传输系统能够适应网络状况、用户偏好、显示设置等因素。HTTP自适应流媒体（HAS）通过动态切换不同视频表示形式，同时提高带宽消耗和用户流媒体体验。每帧编码，由Netflix首创，优化每帧或场景的编码参数。动态优化器（DO）使用视频多方法评估融合（VMAF）感知视频质量预测引擎，以减少码率提供高质量视频。本文开发了一种基于低级特征和视觉信息保真度（VIF）特征的感知优化方法，用于构建最优每帧码率和质量阶梯。在推理过程中，我们的方法可预测源视频的码率或质量阶梯，而无需任何压缩或质量估计。我们比较了所提模型与其他内容自适应码率阶梯预测方法、固定码率阶梯以及通过穷举编码使用Bjontegaard-delta（BD）指标构建的参考码率阶梯的性能。所提方法在码率和质量方面相对于固定码率阶梯有显著提升，仅对参考码率阶梯有小幅损失，同时提供显著的计算优势。
英文摘要：
Video service providers need their delivery systems to be able to adapt to network conditions, user preferences, display settings, and other factors. HTTP Adaptive Streaming (HAS) offers dynamic switching between different video representations to simultaneously enhance bandwidth consumption and users' streaming experiences. Per-shot encoding, pioneered by Netflix, optimizes the encoding parameters on each scene or shot. The Dynamic Optimizer (DO) uses the Video Multi-Method Assessment Fusion (VMAF) perceptual video quality prediction engine to deliver high-quality videos at reduced bitrates. Here we develop a perceptually optimized method of constructing optimal per-shot bitrate and quality ladders, using an ensemble of low-level features and Visual Information Fidelity (VIF) features. During inference, our method predicts the bitrate or quality ladder of a source video without any compression or quality estimation. We compare the performance of our model against other content-adaptive bitrate ladder prediction methods, a fixed bitrate ladder, and reference bitrate ladders constructed via exhaustive encoding using Bjontegaard-delta (BD) metrics. Our proposed method shows excellent gains in bitrate and quality against the fixed bitrate ladder and only small losses against the reference bitrate ladder, while providing significant computational advantages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
