Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this realization, we predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration using an XGBoost-based model. Based on the predicted XPSNR scores, we introduce a Quality-A ware Dynamic Resolution Adaptation (QADRA) framework for adaptive video streaming applications, where we determine the convex-hull online. Furthermore, keeping the encoding and decoding times within an acceptable threshold is mandatory for smooth and energy-efficient streaming. Hence, QADRA determines the encoding resolution and quantization parameter (QP) for each target bitrate by maximizing XPSNR while constraining the maximum encoding and/ or decoding time below a threshold. QADRA implements a JND-based representation elimination algorithm to remove perceptually redundant representations from the bitrate ladder. QADRA is an open-source Python-based framework published under the GNU GPLv3 license. Github: https://github.com/PhoenixVideo/QADRA Online documentation: https://phoenixvideo.github.io/QADRA/

翻译：传统单标题编码方案旨在优化编码分辨率，以为每个表征提供最高感知质量。研究表明，XPSNR指标与VVC编码码流的主观质量具有更优相关性。基于此认识，我们利用视频的空时复杂度特征和目标编码配置，通过基于XGBoost的模型预测VVC编码码流的平均XPSNR值。依据预测的XPSNR分数，我们提出一种面向自适应视频流应用的质量感知动态分辨率适配（QADRA）框架，并在该框架中实现在线凸包构建。此外，将编解码时间控制在可接受阈值内是实现流畅且节能流式传输的必要条件。因此，QADRA通过最大化XPSNR同时约束最大编码和/或解码时间低于阈值，为每个目标码率确定编码分辨率与量化参数（QP）。该框架采用基于JND的表征消除算法，从码率阶梯中移除感知冗余表征。QADRA是基于Python的开源框架，遵循GNU GPLv3许可协议发布。GitHub: https://github.com/PhoenixVideo/QADRA 在线文档: https://phoenixvideo.github.io/QADRA/