用多目标优化构建自适应码率阶梯，兼顾画质、码率和解码效率。
Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming
- 基于帕累托前沿设计动态码率阶梯，联合优化画质、码率与解码时间。
- 相比固定码率阶梯，平均节省11.76%码率，解码时间仅减少0.29%。
- 适合对能效和体验有要求的实时视频流系统，尤其适用于VVC编码场景。
自适应视频流已显著提升视频传输体验。为实现高效、内容与编码器相关的自适应流媒体，需在码率、画质与解码复杂度之间取得平衡。本文提出一种多目标帕累托前沿（PF）优化框架，用于构建满足画质单调性、内容自适应的通用视频编码（VVC）码率阶梯，联合优化视频质量、码率和解码时间（作为解码能耗的实用代理）。引入两种策略：联合码率-画质-时间帕累托前沿（JRQT-PF）与联合画质-时间帕累托前沿（JQT-PF），分别探索不同权衡形式与目标优先级。在大规模UHD数据集Inter-4K上进行实验，画质评估采用PSNR、VMAF和XPSNR，复杂度通过解码时间和能耗衡量。JQT-PF方法在保持相同XPSNR的前提下，平均码率降低11.76%，解码时间仅减少0.29%；更激进配置下可实现最高27.88%的码率节省，代价是复杂度上升。JRQT-PF策略提供更可控的权衡，实现6.38%码率节省与6.17%解码时间减少。该框架优于现有方法，包括固定码率阶梯、基于VMAF和XPSNR的动态分辨率选择及复杂度感知基准。结果表明，结合解码时间约束的帕累托优化可实现面向网络与设备能力的可持续高质量流媒体。
原文摘要 · Abstract (English)
Adaptive video streaming has facilitated improved video streaming over the past years. A balance among coding performance objectives such as bitrate, video quality, and decoding complexity is required to achieve efficient, content- and codec-dependent, adaptive video streaming. This paper proposes a multi-objective Pareto-front (PF) optimization framework to construct quality-monotonic, content-adaptive bitrate ladders Versatile Video Coding (VVC) streaming that jointly optimize video quality, bitrate, and decoding time, which is used as a practical proxy for decoding energy. Two strategies are introduced: the Joint Rate-Quality-Time Pareto Front (JRQT-PF) and the Joint Quality-Time Pareto Front (JQT-PF), each exploring different tradeoff formulations and objective prioritizations. The ladders are constructed under quality monotonicity constraints during adaptive streaming to ensure a consistent Quality of Experience (QoE). Experiments are conducted on a large-scale UHD dataset (Inter-4K), with quality assessed using PSNR, VMAF, and XPSNR, and complexity measured via decoding time and energy consumption. The JQT-PF method achieves 11.76% average bitrate savings while reducing average decoding time by 0.29% to maintain the same XPSNR, compared to a widely-used fixed ladder. More aggressive configurations yield up to 27.88% bitrate savings at the cost of increased complexity. The JRQT-PF strategy, on the other hand, offers more controlled tradeoffs, achieving 6.38 % bitrate savings and 6.17 % decoding time reduction. This framework outperforms existing methods, including fixed ladders, VMAF- and XPSNR-based dynamic resolution selection, and complexity-aware benchmarks. The results confirm that PF optimization with decoding time constraints enables sustainable, high-quality streaming tailored to network and device capabilities.
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