一种新的基于奇异值分解的图像质量评价方法

Mohammad Esmaeilpour, Azadeh Mansouri, Ahmad Mahmoudi-Aznaveh
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引用次数: 5

摘要

近年来,为了设计一种基于人类视觉系统的感知图像质量评估算法,人们做了很多努力。尽管已经提出了一些令人印象深刻的指标,但完全参考图像质量评估(IQA)仍然是一个具有挑战性的问题。在本文中,我们提出了一种新的基于奇异值分解的IQA方法,该方法利用参考图像和失真图像之间的结构相似性作为测量施加畸变的关键因素。实验结果表明,该算法能够有效地评估图像质量,且与人类视觉感知一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new SVD-based image quality assessment
In recent years, many efforts have been performed in order to design an algorithm assessing perceptual image quality based on human visual system. Although some impressive metrics have been presented, full reference image quality assessment (IQA) is still a challenging issue. In this paper, we present a new SVD-based IQA method in which the structural similarity between the reference and distorted image is utilized as a key factor for measuring the imposed distortions. The experimental results show that the proposed algorithm can effectively evaluated the image quality in a consistent manner with human visual perception.
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