Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images
This addresses the need for efficient image quality assessment without reference images, though it appears incremental as it builds on existing NR metric approaches.
The paper tackles the problem of no-reference quality assessment for contrast-distorted images by proposing an efficient Minkowski Distance based Metric (MDM) that extracts only three features for prediction and classification. Experimental results on four datasets show it outperforms state-of-the-art NR metrics with very low complexity.
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.