# A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction

arXiv: https://arxiv.org/abs/1708.08190 · HTML: https://mlsanity.com/paper/1708.08190/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 28 of 28)

- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2304.00451/) (2023)
- [Image Quality Assessment using Contrastive Learning](https://mlsanity.com/paper/2110.13266/) (2021)
- [ARNIQA: Learning Distortion Manifold for Image Quality Assessment](https://mlsanity.com/paper/2310.14918/) (2023)
- [VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining](https://mlsanity.com/paper/2303.14302/) (2023)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2207.08124/) (2022)
- [Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning](https://mlsanity.com/paper/2312.06995/) (2023)
- [QMamba: On First Exploration of Vision Mamba for Image Quality Assessment](https://mlsanity.com/paper/2406.09546/) (2024)
- [UniAR: A Unified model for predicting human Attention and Responses on visual content](https://mlsanity.com/paper/2312.10175/) (2023)
- [Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token](https://mlsanity.com/paper/2305.09353/) (2023)
- [Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment](https://mlsanity.com/paper/2406.09858/) (2024)
- [Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization](https://mlsanity.com/paper/2406.04654/) (2024)
- [DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2407.03886/) (2024)
- [Pairwise Comparisons Are All You Need](https://mlsanity.com/paper/2403.09746/) (2024)
- [Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content](https://mlsanity.com/paper/2404.13484/) (2024)
- [Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment](https://mlsanity.com/paper/2409.07115/) (2024)
- [Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance](https://mlsanity.com/paper/2411.17390/) (2024)
- [Deep Superpixel-based Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2110.06564/) (2021)
- [Multi-task Feature Enhancement Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2411.07556/) (2024)
- [Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment](https://mlsanity.com/paper/2501.15485/) (2025)
- [Perceptual Classifiers: Detecting Generative Images using Perceptual Features](https://mlsanity.com/paper/2507.17240/) (2025)
- [DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2507.19418/) (2025)
- [SHAMISA: SHAped Modeling of Implicit Structural Associations for Self-supervised No-Reference Image Quality Assessment](https://mlsanity.com/paper/2603.13669/) (2026)
- [Spatially Localized Image Degradation Embeddings for Image Quality Assessment](https://mlsanity.com/paper/2606.29162/) (2026)

## Abstract (arXiv)

Blind image quality assessment (BIQA) remains a very challenging problem due to the unavailability of a reference image. Deep learning based BIQA methods have been attracting increasing attention in recent years, yet it remains a difficult task to train a robust deep BIQA model because of the very limited number of training samples with human subjective scores. Most existing methods learn a regression network to minimize the prediction error of a scalar image quality score. However, such a scheme ignores the fact that an image will receive divergent subjective scores from different subjects, which cannot be adequately represented by a single scalar number. This is particularly true on complex, real-world distorted images. Moreover, images may broadly differ in their distributions of assigned subjective scores. Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model. The proposed PQR method is shown to not only speed up the convergence of deep model training, but to also greatly improve the achievable level of quality prediction accuracy relative to scalar quality score regression methods. The source code is available at https://github.com/HuiZeng/BIQA_Toolbox.