Perceptual Video Quality Assessment: A Survey

TL;DR

The paper addresses the challenge of evaluating perceptual video quality across diverse content and deployment scenarios. It systematically surveys subjective VQA methodologies and databases, and catalogs general-purpose FR/RR/NR objective VQA models alongside emerging topic metrics for compression, streaming, VR/AR, HDR, and more. It highlights data-driven and knowledge-driven approaches, emphasizes evaluation practices and cross-dataset performance, and outlines open challenges and future directions. The work provides a structured reference for researchers to access progress, benchmark methods, and identify promising directions in perceptual VQA and QoE modeling.

Abstract

Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of internet communication and cloud service technology, video content and traffic are growing exponentially, which further emphasizes the requirement for accurate and rapid assessment of video quality. Therefore, numerous subjective and objective video quality assessment studies have been conducted over the past two decades for both generic videos and specific videos such as streaming, user-generated content (UGC), 3D, virtual and augmented reality (VR and AR), high frame rate (HFR), audio-visual, etc. This survey provides an up-to-date and comprehensive review of these video quality assessment studies. Specifically, we first review the subjective video quality assessment methodologies and databases, which are necessary for validating the performance of video quality metrics. Second, the objective video quality assessment algorithms for general purposes are surveyed and concluded according to the methodologies utilized in the quality measures. Third, we overview the objective video quality assessment measures for specific applications and emerging topics. Finally, the performances of the state-of-the-art video quality assessment measures are compared and analyzed. This survey provides a systematic overview of both classical works and recent progresses in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.

Figures (15)

• Figure 1: Scope of this survey.
• Figure 2: The categories of objective video quality assessment models.
• Figure 3: The framework of IQA-based objective VQA methods.
• Figure 4: The framework of MOVIE seshadrinathan2009motion.
• Figure 5: The framework of VMAF li2016toward. Image credit: NETFLX$^\circledR$.