# MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation

arXiv: https://arxiv.org/abs/1811.08947 · HTML: https://mlsanity.com/paper/1811.08947/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

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

- [Introspective Learning : A Two-Stage Approach for Inference in Neural Networks](https://mlsanity.com/paper/2209.08425/) (2022)
- [On the Ramifications of Human Label Uncertainty](https://mlsanity.com/paper/2211.05871/) (2022)
- [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)
- [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 train independent linear decoder models to estimate the perceived quality of images. More specifically, we calculate the responses of individual non-overlapping image patches to each of the decoders and scale these responses based on the sharpness characteristics of filter set. We use multiple linear decoders to capture different abstraction levels of the image patches. Training each model is carried out on 100,000 image patches from the ImageNet database in an unsupervised fashion. Color space selection and ZCA Whitening are performed over these patches to enhance the descriptiveness of the data. The proposed quality estimator is tested on the LIVE and the TID 2013 image quality assessment databases. Performance of the proposed method is compared against eleven other state of the art methods in terms of accuracy, consistency, linearity, and monotonic behavior. Based on experimental results, the proposed method is generally among the top performing quality estimators in all categories.