Video Quality Assessment of Danmaku-Based Video Saliency Regions

Abstract

At present, objective quality assessment methods have significant advantages in video quality assessment (VQA), among which the objective assessment based on deep learning is superior in processing speed and accuracy. However, the objective assessment method has the limitation of poor interpretability of experimental results and lacks the participation of human thinking and experience. In this paper, we present a quality assessment method based on the danmaku visual saliency region. First, to obtain the sentiment trend of the danmaku, we conduct a sentiment analysis of the content of the danmaku. Then, to speed up the video quality assessment, we select the keyframes of the video according to the sentiment analysis. Finally, we innovatively use human visual characteristics to extract salient regions from video keyframes to make the results more subjective. The experiments show that the Pearson correlation coefficient (PCC) and root mean square error (RMSE) between the results of the proposed method and the subjective evaluation are improved, which conforms to the audience's subjective feelings about video viewing, and the assessment results are more accurate.