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

arXiv: https://arxiv.org/abs/2503.00625 · HTML: https://mlsanity.com/paper/2503.00625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment](https://mlsanity.com/paper/2103.07666/) (2021)
- [Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment](https://mlsanity.com/paper/2403.11956/) (2024)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images](https://mlsanity.com/paper/2311.15556/) (2023)
- [Tensor Oriented No-Reference Light Field Image Quality Assessment](https://mlsanity.com/paper/1909.02358/) (2019)
- [Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.15163/) (2024)
- [Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment](https://mlsanity.com/paper/2406.16641/) (2024)
- [No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement](https://mlsanity.com/paper/1908.06280/) (2019)
- [Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics](https://mlsanity.com/paper/2302.12393/) (2023)
- [No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image](https://mlsanity.com/paper/1908.10087/) (2019)
- [Deep Multi-Scale Features Learning for Distorted Image Quality Assessment](https://mlsanity.com/paper/2012.01980/) (2020)
- [Perceptual Crack Detection for Rendered 3D Textured Meshes](https://mlsanity.com/paper/2405.06143/) (2024)

## Built on by (most cited first, 5 of 5)

- [VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results](https://mlsanity.com/paper/2509.06413/) (2025)
- [PhysLab: A Benchmark Dataset for Multi-Granularity Visual Parsing of Physics Experiments](https://mlsanity.com/paper/2506.06631/) (2025)
- [Elastic3D: Controllable Stereo Video Conversion with Guided Latent Decoding](https://mlsanity.com/paper/2512.14236/) (2025)
- [AegisRF: Adversarial Perturbations Guided with Sensitivity for Protecting Intellectual Property of Neural Radiance Fields](https://mlsanity.com/paper/2510.19371/) (2025)
- [SDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates](https://mlsanity.com/paper/2512.06344/) (2025)

## Abstract (arXiv)

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.