In adaptive bitrate streaming, resolution cross-over refers to the point on the convex hull where the encoding resolution should switch to achieve better quality. Accurate cross-over prediction is crucial for streaming providers to optimize resolution at given bandwidths. Most existing works rely on objective Video Quality Metrics (VQM), particularly VMAF, to determine the resolution cross-over. However, these metrics have limitations in accurately predicting resolution cross-overs. Furthermore, widely used VQMs are often trained on subjective datasets collected using the Absolute Category Rating (ACR) methodologies, which we demonstrate introduces significant uncertainty and errors in resolution cross-over predictions. To address these problems, we first investigate different subjective methodologies and demonstrate that Pairwise Comparison (PC) achieves better cross-over accuracy than ACR. We then propose a novel metric, Resolution Cross-over Quality Loss (RCQL), to measure the quality loss caused by resolution cross-over errors. Furthermore, we collected a new subjective dataset (LSCO) focusing on live streaming scenarios and evaluated widely used VQMs, by benchmarking their resolution cross-over accuracy.

翻译：在自适应码率流媒体传输中，分辨率切换点指的是凸包上为实现更佳质量而应切换编码分辨率的临界点。精确预测切换点对于流媒体服务提供商在给定带宽下优化分辨率至关重要。现有研究大多依赖客观视频质量指标（VQM），特别是VMAF，来确定分辨率切换点。然而，这些指标在准确预测分辨率切换点方面存在局限。此外，广泛使用的VQM通常基于采用绝对类别评分（ACR）方法收集的主观数据集进行训练，我们证明这会给分辨率切换点预测带来显著的不确定性和误差。为解决这些问题，我们首先研究了不同的主观评估方法，并证明成对比较法（PC）比ACR能获得更准确的切换点。随后，我们提出了一种新颖的指标——分辨率切换质量损失（RCQL），用于量化因分辨率切换误差导致的质量损失。此外，我们针对直播流场景收集了新的主观数据集（LSCO），并通过基准测试评估了广泛使用的VQM在分辨率切换点预测上的准确性。