Abstract
Subjective video quality assessment (SVQA) is essential in order to produce a sufficient benchmark dataset, which is especially important for extreme environments such as underwater videos. However, SVQA has always been plagued by its disadvantages, such as high cost and time-consuming. In this paper, we propose an effective SVQA method based on active learning and clustering (AL-SVQA). Our method initializes dual regression models for video quality prediction with a few quality-known videos. During the quality assessment process with active learning and clustering results, a batch of videos are iteratively selected by a sampling strategy and their quality is scored by a subject. The scored videos are then used to continually update the prediction models until meeting the stop criteria. When the active learning stops, the prediction models will annotate the video scores instead of humans while maintaining good performance. In this way, AL-SVQA can reduce about 40% of human the workload for subjective quality assessment. Evaluation experiments were conducted with an underwater video database.