为低延迟游戏流媒体设计自适应分辨率编码，提升画质且无额外延迟。
Content Adaptive Encoding For Interactive Game Streaming
- 基于历史编码统计信息，用轻量CNN预测下一场景最佳分辨率。
- 相比固定分辨率方案，画质提升2.3个Bjøntegaard Delta-VMAF点。
- 每场景仅需1毫秒单核CPU时间，适合实时游戏流媒体部署。
视频点播已受益于内容自适应编码（CAE），即根据场景调整分辨率和量化参数，采用凸包优化方法。然而，将其应用于交互式游戏流媒体（IGS）极具挑战：商业IGS服务要求超低延迟编码，无预览或缓冲，且对任何CAE算法的计算资源有极严限制。本文提出首个基于过去帧编码元数据的分辨率自适应CAE方法。具体而言，训练卷积神经网络（CNN）从当前场景聚合的编码块统计信息中，推断下一场景可用分辨率的最佳选择。在基于HEVC编码的实际IGS系统中部署该方案后：(i) 相比默认固定分辨率阶梯，画质提升2.3个Bjøntegaard Delta-VMAF点；(ii) 每场景推理仅消耗1毫秒单核CPU时间，无额外延迟开销。
原文摘要 · Abstract (English)
Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead.
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