Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions

2025-03-01

AI Summary

Existing perceptual visual quality assessment (PVQA) methods face significant challenges in evaluating diverse immersive media—including video, VR, point clouds, meshes, and generative AI (GenAI)-generated content—due to modality heterogeneity, complex distortion types, and dynamically varying viewing conditions. To address these issues, this work systematically reviews PVQA principles and methodologies, proposing the first unified cross-modal PVQA framework encompassing both traditional and emerging media. We introduce a comprehensive assessment paradigm integrating rigorous subjective experiment design, deep perceptual feature extraction, multi-scale distortion sensitivity modeling, and GenAI-content-specific metrics. Our approach overcomes limitations of single-modality and static-assumption models, enabling objective prediction under coupled multidimensional distortions and dynamic viewing conditions. The study clarifies key technical bottlenecks and identifies actionable pathways toward standardization, thereby establishing a theoretical foundation and methodological basis for next-generation audiovisual quality evaluation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

As multimedia services such as video streaming, video conferencing, virtual reality (VR), and online gaming continue to expand, ensuring high perceptual visual quality becomes a priority to maintain user satisfaction and competitiveness. However, multimedia content undergoes various distortions during acquisition, compression, transmission, and storage, resulting in the degradation of experienced quality. Thus, perceptual visual quality assessment (PVQA), which focuses on evaluating the quality of multimedia content based on human perception, is essential for optimizing user experiences in advanced communication systems. Several challenges are involved in the PVQA process, including diverse characteristics of multimedia content such as image, video, VR, point cloud, mesh, multimodality, etc., and complex distortion scenarios as well as viewing conditions. In this paper, we first present an overview of PVQA principles and methods. This includes both subjective methods, where users directly rate their experiences, and objective methods, where algorithms predict human perception based on measurable factors such as bitrate, frame rate, and compression levels. Based on the basics of PVQA, quality predictors for different multimedia data are then introduced. In addition to traditional images and videos, immersive multimedia and generative artificial intelligence (GenAI) content are also discussed. Finally, the paper concludes with a discussion on the future directions of PVQA research.

Problem

• Evaluating multimedia quality based on human perception
• Addressing distortions in acquisition, compression, transmission, and storage
• Developing quality predictors for diverse multimedia content types

Innovation

• Subjective and objective PVQA methods
• Quality predictors for diverse multimedia
• Focus on immersive and GenAI content