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
this https URL

本文提出了一种可靠的全参考图像质量评价模型，该模型利用了梯度相似性、色度相似性和偏差池化，并经过了若干改进，其实验结果表明该模型在图像处理领域表现优秀。