# UNIQUE: Unsupervised Image Quality Estimation

arXiv: https://arxiv.org/abs/1810.06631 · HTML: https://mlsanity.com/paper/1810.06631/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 9 of 9)

- [Introspective Learning : A Two-Stage Approach for Inference in Neural Networks](https://mlsanity.com/paper/2209.08425/) (2022)
- [Explanatory Paradigms in Neural Networks](https://mlsanity.com/paper/2202.11838/) (2022)
- [Probing the Purview of Neural Networks via Gradient Analysis](https://mlsanity.com/paper/2304.02834/) (2023)
- [On the Ramifications of Human Label Uncertainty](https://mlsanity.com/paper/2211.05871/) (2022)
- [VOICE: Variance of Induced Contrastive Explanations to quantify Uncertainty in Neural Network Interpretability](https://mlsanity.com/paper/2406.00573/) (2024)
- [Stochastic Surprisal: An inferential measurement of Free Energy in Neural Networks](https://mlsanity.com/paper/2302.05776/) (2023)
- [Perceptual Quality-based Model Training under Annotator Label Uncertainty](https://mlsanity.com/paper/2403.10190/) (2024)
- [InsHuman: Towards Natural and Identity-Preserving Human Insertion](https://mlsanity.com/paper/2605.07402/) (2026)
- [Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets](https://mlsanity.com/paper/2408.05697/) (2024)

## Abstract (arXiv)

In this paper, we estimate perceived image quality using sparse representations obtained from generic image databases through an unsupervised learning approach. A color space transformation, a mean subtraction, and a whitening operation are used to enhance descriptiveness of images by reducing spatial redundancy; a linear decoder is used to obtain sparse representations; and a thresholding stage is used to formulate suppression mechanisms in a visual system. A linear decoder is trained with 7 GB worth of data, which corresponds to 100,000 8x8 image patches randomly obtained from nearly 1,000 images in the ImageNet 2013 database. A patch-wise training approach is preferred to maintain local information. The proposed quality estimator UNIQUE is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases and compared with thirteen quality estimators. Experimental results show that UNIQUE is generally a top performing quality estimator in terms of accuracy, consistency, linearity, and monotonic behavior.