In video streaming applications, a fixed set of bitrate-resolution pairs (known as a bitrate ladder) is typically used during the entire streaming session. However, an optimized bitrate ladder per scene may result in (i) decreased storage or delivery costs or/and (ii) increased Quality of Experience. This paper introduces a Just Noticeable Difference (JND)-aware per-scene bitrate ladder prediction scheme (JASLA) for adaptive video-on-demand streaming applications. JASLA predicts jointly optimized resolutions and corresponding constant rate factors (CRFs) using spatial and temporal complexity features for a given set of target bitrates for every scene, which yields an efficient constrained Variable Bitrate encoding. Moreover, bitrate-resolution pairs that yield distortion lower than one JND are eliminated. Experimental results show that, on average, JASLA yields bitrate savings of 34.42% and 42.67% to maintain the same PSNR and VMAF, respectively, compared to the reference HTTP Live Streaming (HLS) bitrate ladder Constant Bitrate encoding using x265 HEVC encoder, where the maximum resolution of streaming is Full HD (1080p). Moreover, a 54.34% average cumulative decrease in storage space is observed.

翻译：在视频流应用中，通常在整个流媒体会话期间采用固定的码率-分辨率组合集（即码率阶梯）。然而，针对每个场景优化的码率阶梯可降低存储或传输成本，和/或提升用户体验质量。本文提出了一种基于最小可觉差的逐场景码率阶梯预测方案（JASLA），适用于自适应视频点播流应用。JASLA利用场景的空间和时间复杂度特征，为每个场景下的目标码率集合联合优化分辨率及对应的恒定速率因子，从而实现高效的约束变码率编码。此外，剔除产生低于一个最小可觉差异的失真所对应的码率-分辨率组合。实验结果表明，与采用x265 HEVC编码器的参考HTTP实时流码率阶梯恒定码率编码相比，JASLA在维持相同PSNR和VMAF指标时，平均节省码率分别达34.42%和42.67%（流媒体最大分辨率为全高清1080p）。同时，存储空间平均累计减少54.34%。