Subjective and Objective Video Quality Assessment of High Dynamic Range Sports Content

High Dynamic Range (HDR) video streaming has be-come more popular because of the faithful color and bright-ness presentation. However, the live streaming of HDR, especially of sports content, has unique challenges, as it was usually encoded and distributed in real-time without the post-production workflow. A set of unique problems that occurs only in live streaming, e.g. resolution and frame rate crossover, intra-frame pulsing video quality defects, complex relationship between rate-control mode and video quality, are more salient when the videos are streamed in HDR format. These issues are typically ignored by other subjective databases, disregard the fact that they have a sig-nificant impact on the perceived quality of the videos. In this paper, we present a large-scale HDR video quality dataset for sports content that includes the above mentioned important issues in live streaming, and a method of merging multi-ple datasets using anchor videos. We also benchmarked ex-isting video quality metrics on the new dataset, particularly over the novel scopes included in the database, to evaluate the effectiveness and efficiency of the existing models. We found that despite the strong overall performance over the entire database, most of the tested models perform poorly when predicting human preference for various encoding pa-rameters, such as frame rate and adaptive quantization.