# On the Use of Deep Learning for Blind Image Quality Assessment

arXiv: https://arxiv.org/abs/1602.05531 · HTML: https://mlsanity.com/paper/1602.05531/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2016.

## Built on by (most cited first, 30 of 34)

- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [NTIRE 2022 Challenge on Perceptual Image Quality Assessment](https://mlsanity.com/paper/2206.11695/) (2022)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Quantifying Visual Image Quality: A Bayesian View](https://mlsanity.com/paper/2102.00195/) (2021)
- [No-Reference Image Quality Assessment by Hallucinating Pristine Features](https://mlsanity.com/paper/2108.04165/) (2021)
- [DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild](https://mlsanity.com/paper/2405.19996/) (2024)
- [Deep Ensembling for Perceptual Image Quality Assessment](https://mlsanity.com/paper/2305.09141/) (2023)
- [Black-box Adversarial Attacks Against Image Quality Assessment Models](https://mlsanity.com/paper/2402.17533/) (2024)
- [PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression](https://mlsanity.com/paper/2305.09214/) (2023)
- [PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition](https://mlsanity.com/paper/2404.13299/) (2024)
- [ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining](https://mlsanity.com/paper/2406.01020/) (2024)
- [Explainable Image Quality Assessments in Teledermatological Photography](https://mlsanity.com/paper/2209.04699/) (2022)
- [Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge](https://mlsanity.com/paper/2109.12161/) (2021)
- [Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment](https://mlsanity.com/paper/2209.05321/) (2022)
- [Consumer Image Quality Prediction using Recurrent Neural Networks for Spatial Pooling](https://mlsanity.com/paper/2106.00918/) (2021)
- [Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment](https://mlsanity.com/paper/2203.14557/) (2022)
- [Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model](https://mlsanity.com/paper/2209.10451/) (2022)
- [DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment](https://mlsanity.com/paper/2505.24002/) (2025)
- [Half of an image is enough for quality assessment](https://mlsanity.com/paper/2301.12891/) (2023)
- [Enhanced Dermatology Image Quality Assessment via Cross-Domain Training](https://mlsanity.com/paper/2506.16116/) (2025)
- [HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2508.15130/) (2025)
- [Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning](https://mlsanity.com/paper/2602.10744/) (2026)
- [Deep Superpixel-based Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2110.06564/) (2021)
- [Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2206.06541/) (2022)
- [Apples and Oranges? Assessing Image Quality over Content Recognition](https://mlsanity.com/paper/2301.09190/) (2023)
- [Mask Reference Image Quality Assessment](https://mlsanity.com/paper/2302.13770/) (2023)
- [Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention](https://mlsanity.com/paper/2307.09857/) (2023)
- [Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification](https://mlsanity.com/paper/2308.06055/) (2023)
- [ExIQA: Explainable Image Quality Assessment Using Distortion Attributes](https://mlsanity.com/paper/2409.06853/) (2024)

## Abstract (arXiv)

In this work we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average pooling the scores predicted on multiple sub-regions of the original image. The score of each sub-region is computed using a Support Vector Regression (SVR) machine taking as input features extracted using a CNN fine-tuned for category-based image quality assessment. Experimental results on the LIVE In the Wild Image Quality Challenge Database and on the LIVE Image Quality Assessment Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a Linear Correlation Coefficient (LCC) with human subjective scores of almost 0.91 and 0.98 respectively. Furthermore, in most of the cases, the quality score predictions of DeepBIQ are closer to the average observer than those of a generic human observer.