publication . Article . 2011

Structural similarity image quality reliability

Name: Structural similarity image quality reliability
Creator: Javier Silvestre-Blanes
Keywords: Control and Systems Engineering, Signal Processing, Electrical and Electronic Engineering, Software, Computer Vision and Pattern Recognition, Image quality assessment, Structural similarity, ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES

Javier Silvestre-Blanes;

Open Access

Published: 01 Apr 2011 Journal: Signal Processing, volume 91, pages 1,012-1,020 (issn: 0165-1684,

Publisher: Elsevier BV

Country: Spain

Summary

Abstract

The need to obtain objective values of the quality of distorted images with respect to the original is fundamental in multimedia and image processing applications. It is generally required that this value correlates well with the human vision system (HVS). In spite of the properties and the general use of the mean square error (MSE) measurement, this has a poor correlation with HSV, which has led to the development of methods such as structural similarity (SSIM). This metric improves the correlation with respect to the classic MSE and PSNR (peak signal to noise ratio). However, its behavior depends on the values assigned to constants and on the windows size sele...

doi: 10.1016/j.sigpro.2010.10.003

handle: 10251/47595

Subjects

free text keywords: Control and Systems Engineering, Signal Processing, Electrical and Electronic Engineering, Software, Computer Vision and Pattern Recognition, Image quality assessment, Structural similarity, ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES, Correlation, Image processing, Similitude, Mean squared error, Peak signal-to-noise ratio, Image quality, Mathematics, Artificial intelligence, business.industry, business, Computer vision, Signal-to-noise ratio, Machine vision, Pattern recognition

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