人类视觉感知图像的质量分数计算综述

Mangesh Patil, Abhipray Paturkar
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引用次数: 1

摘要

由于人的意见评分受生理和心理因素的影响,对图像质量的测量是一个复杂而困难的过程。人们提出了几种测量图像质量的技术,但其中任何一种技术都被认为是测量图像质量的理想技术。图像质量评价在图像处理领域中占有重要地位。图像质量评估(IQA)通过测量参考图像和失真图像之间的差异来估计图像的质量。在IQA中,目标域的失真估计也起着重要的作用。IQA方法有三类:全参考(FR)、部分或简化参考(RR)和无参考(NR)。简化参考图像质量评估(RR - IQA)为仅提供部分原始图像或参考图像信息的各种应用中的自动图像质量估计提供了一种现实的解决方案。RR IQA的思想是利用较少的图像信息,获得更高的评价精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey of quality score calculation for human visual perception of an image
The quality of measuring the image is a complicated and difficult process since humans opinion score is affected by physical and psychological factors. Several techniques are proposed for measuring the image quality but any of this techniques is considered to be ideal for measuring the image quality. Image quality assessment plays a significant role in the field of image processing. Image quality assessment (IQA) estimates the quality of an image by measuring the difference between the reference and distorted images. Distortion estimation in objective domain also plays a significant role during IQA. There are three categories of IQA methods: full-reference (FR), partial or reduced-reference (RR), and no-reference (NR). Reduced reference image quality assessment (RR IQA) gives a realistic solution for automatic image quality estimation in different kinds of applications where only partial information of the original or reference image is available. The idea of RR IQA's to use less information about the image and obtain higher evaluation precision.
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