Automatic Prediction of Perceptual Video Quality: Recent Trends and Research Directions

Abstract

Objective video quality assessment (VQA) refers to evaluation of the quality of a video by an algorithm. The performance of any such VQA algorithm is gaged by how well the algorithmic scores correlate with human perception of quality. Research in the area of VQA has produced a host of full-reference (FR) VQA algorithms. FR VQA algorithms are those in which the algorithm has access to both the original reference video and the distorted video whose quality is being assessed. However, in many cases, the presence of the original reference video is not guaranteed. Hence, even though many FR VQA algorithms have been shown to correlate well with human perception of quality, their utility remains constrained. In this chapter, we analyze recently proposed reduced/no-reference (RR/NR) VQA algorithms. RR VQA algorithms are those in which some information about the reference video and/or the distorting medium is embedded in the video under test. NR VQA algorithms are expected to assess the quality of videos without any knowledge of the reference video or the distorting medium. The utility of RR/NR algorithms has prompted the Video Quality Experts Group (VQEG) to devote resources towards forming a RR/NR test group. In this chapter, we begin by discussing how performance of any VQA algorithm is evaluated. We introduce the popular VQEG Phase-I VQA dataset and comment on its drawbacks. New datasets which allow for objective evaluation of algorithms are then introduced. We then summarize some properties of the human visual system (HVS) that are frequently utilized in developing VQA algorithms. Further, we enumerate the paths that current RR/NR VQA algorithms take in order to evaluate visual quality. We enlist some considerations that VQA algorithms need to consider for HD videos. We then describe exemplar algorithms and elaborate on possible shortcomings of these algorithms. Finally, we suggest possible future research directions in the field of VQA and conclude this chapter.