Perceptual Gradient Similarity Deviation for Full Reference Image Quality Assessment

Perceptual image quality assessment(IQA)is one of the most indispensable yet challenging problems in image processing and computer vision.It is quite necessary to develop automatic and efficient approaches that can accurately predict perceptual image quality consistently with human subjective evaluation.To further improve the prediction accuracy for the distortion of color images,in this paper,we propose a novel effective and efficient IQA model,called perceptual gradient similarity deviation(PGSD).Based on the gradient magnitude similarity,we proposed a gradient direction selection method to automatically determine the pixel-wise perceptual gradient.The luminance and chrominance channels are both took into account to characterize the quality degradation caused by intensity and color distortions.Finally,a multi-scale strategy is utilized and pooled with different weights to incorporate image details at different resolutions.Experimental results on LIVE,CSIQ and TID2013 databases demonstrate the superior performances of the proposed algorithm.

基于边缘结构相似性的图像质量评价方法

针对现有图像质量评估方法不能有效评价所有失真类型图像、计算量大等问题进行了研究,受人眼视觉特性的启发,提出了一种新的基于图像边缘结构相似性的全参考质量评价方法。该方法利用图像边缘梯度对图像失真的敏感性不同,通过Prewitt算子计算水平和竖直方向梯度向量绝对值和,进而定义了两种基于边缘结构相似性的图像质量评价标准:图像梯度结构相似度均值(GSSM)和图像梯度结构相似度标准差(GSSD)。标准图像数据库上的实验结果表明,GSSD方法优于GSSM和现有的全参考图象质量评价方法,主客观分值一致性好,并且计算简单,适合应用于大规模的图像质量评价。