Subjective and objective video quality assessment of high dynamic range sports content
High Dynamic Range (HDR) video streaming has become more popular because of the faithful color and brightness 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 significant 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 multiple datasets using anchor videos. We also benchmarked existing 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 parameters, such as frame rate and adaptive quantization.