Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator

TL;DR

This paper introduces a new full-reference image quality assessment metric called Mean Deviation Similarity Index, which combines improved gradient and chromaticity similarity measures with deviation pooling for efficient and reliable quality prediction.

Contribution

The paper proposes a novel fusion-based gradient similarity, an efficient chromaticity similarity calculation, and a generalized deviation pooling method for improved image quality assessment.

Findings

Outperforms or matches state-of-the-art IQA metrics on multiple datasets.

Offers low computational complexity and high reliability.

Provides publicly available MATLAB code for implementation.

Abstract

Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard deviation pooling, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six datasets of natural images, a synthetic dataset, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at https://www.mathworks.com/matlabcentral/fileexchange/59809.