研究如何用算法评估视频等多媒体的视觉质量，提升用户体验。
Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions
- 结合人眼感知规律，用算法预测多媒体质量。
- 覆盖图像、视频、虚拟现实及生成式AI内容的质量评估。
- 适合做音视频优化、体验评测的研究者和工程师参考。
随着视频流、视频会议、虚拟现实（VR）和在线游戏等多媒体服务持续扩展，确保高感知视觉质量成为维持用户满意度和竞争力的关键。然而，多媒体内容在采集、压缩、传输和存储过程中会经历多种失真，导致体验质量下降。因此，基于人类感知的感知视觉质量评估（PVQA）对于优化先进通信系统中的用户体验至关重要。PVQA面临诸多挑战，包括图像、视频、VR、点云、网格、多模态等多媒体内容的多样特性，以及复杂的失真场景和观看条件。本文首先综述了PVQA的基本原理与方法，涵盖主观评价（用户直接打分）和客观评价（算法基于码率、帧率、压缩级别等可测量因素预测人类感知）。在此基础上，介绍了针对不同多媒体数据的质量预测模型，不仅包括传统图像与视频，还涉及沉浸式多媒体和生成式人工智能（GenAI）内容。最后，论文展望了PVQA未来的研究方向。
原文摘要 · Abstract (English)
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.
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