# Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images

arXiv: https://arxiv.org/abs/1804.02554 · HTML: https://mlsanity.com/paper/1804.02554/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions](https://mlsanity.com/paper/2412.07079/) (2024)
- [Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer](https://mlsanity.com/paper/2307.02625/) (2023)
- [Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation](https://mlsanity.com/paper/2205.05880/) (2022)
- [Echocardiographic Image Quality Assessment Using Deep Neural Networks](https://mlsanity.com/paper/2209.00959/) (2022)
- [No-Reference Image Contrast Assessment with Customized EfficientNet-B0](https://mlsanity.com/paper/2509.21967/) (2025)
- [No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference](https://mlsanity.com/paper/2510.05053/) (2025)

## Abstract (arXiv)

In this paper, an efficient Minkowski Distance based Metric (MDM) for no-reference (NR) quality assessment of contrast distorted images is proposed. It is shown that higher orders of Minkowski distance and entropy provide accurate quality prediction for the contrast distorted images. The proposed metric performs predictions by extracting only three features from the distorted images followed by a regression analysis. Furthermore, the proposed features are able to classify type of the contrast distorted images with a high accuracy. Experimental results on four datasets CSIQ, TID2013, CCID2014, and SIQAD show that the proposed metric with a very low complexity provides better quality predictions than the state-of-the-art NR metrics. The MATLAB source code of the proposed metric is available to public at http://www.synchromedia.ca/system/files/MDM.zip.