Conventional per-title encoding schemes strive to optimize encoding resolutions to deliver the utmost perceptual quality for each bitrate ladder representation. Nevertheless, maintaining encoding time within an acceptable threshold is equally imperative in online streaming applications. Furthermore, modern client devices are equipped with the capability for fast deep-learning-based video super-resolution (VSR) techniques, enhancing the perceptual quality of the decoded bitstream. This suggests that opting for lower resolutions in representations during the encoding process can curtail the overall energy consumption without substantially compromising perceptual quality. In this context, this paper introduces a video super-resolution-based latency-aware optimized bitrate encoding scheme (ViSOR) designed for online adaptive streaming applications. ViSOR determines the encoding resolution for each target bitrate, ensuring the highest achievable perceptual quality after VSR within the bound of a maximum acceptable latency. Random forest-based prediction models are trained to predict the perceptual quality after VSR and the encoding time for each resolution using the spatiotemporal features extracted for each video segment. Experimental results show that ViSOR targeting fast super-resolution convolutional neural network (FSRCNN) achieves an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder encoding of 4 s segments using the x265 encoder, when the maximum acceptable latency for each representation is set as two seconds. Considering a just noticeable difference (JND) of six VMAF points, the average cumulative storage consumption and encoding energy for each segment is reduced by 79.32 % and 68.21 %, respectively, contributing towards greener streaming.

翻译：传统按标题编码方案致力于优化编码分辨率，以在每一码率阶梯表示中提供最高感知质量。然而，在在线流媒体应用中，将编码时间控制在可接受阈值内同样至关重要。此外，现代客户端设备具备基于深度学习的快速视频超分辨率（VSR）技术能力，可提升解码比特流的感知质量。这表明在编码过程中选择较低分辨率的表示，能够在不显著牺牲感知质量的前提下降低整体能耗。基于此，本文提出一种面向在线自适应流媒体的基于视频超分辨率且感知延迟的优化码率编码方案（ViSOR）。ViSOR为每个目标码率确定编码分辨率，确保在最大可接受延迟的约束下，实现VSR后所能达到的最高感知质量。利用每个视频片段提取的时空特征，训练基于随机森林的预测模型，以预测VSR后的感知质量及每种分辨率的编码时间。实验结果表明，当每段表示的最大可接受延迟设为两秒时，针对快速超分辨率卷积神经网络（FSRCNN）的ViSOR，与采用x265编码器、每段4秒的HTTP实时流媒体（HLS）码率阶梯编码相比，在保持相同PSNR和VMAF的情况下，平均码率分别降低24.65%和32.70%。考虑六点VMAF的恰可察觉差异（JND），每个片段的平均累积存储消耗和编码能耗分别降低79.32%和68.21%，为迈向更绿色流媒体做出贡献。