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

TL;DR

This paper introduces an efficient Minkowski Distance based Metric (MDM) for no-reference quality assessment and classification of contrast distorted images, achieving high accuracy with low complexity.

Contribution

The paper proposes a novel low-complexity NR quality assessment metric using Minkowski distance and entropy, capable of both predicting quality and classifying contrast distortions.

Findings

Outperforms state-of-the-art NR metrics on multiple datasets

Uses only three features for prediction and classification

Achieves high accuracy with low computational complexity

Abstract

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.