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New Structural Similarity Measure for Image Comparison

  • Prashan Premaratne
  • Malin Premaratne
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

Subjective quality measures based on Human Visual System for images do not agree well with well-known metrics such as Mean Squared Error and Peak Signal to Noise Ratio. Recently, Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the performance of the SSIM metric. Furthermore, despite SSIM’s popularity, it does not provide adequate insight into how it handles ‘structural similarity’ of images. We propose a structural similarity measure based on approximation level of a given Discrete Wavelet Decomposition that evaluates moment invariants to capture the structural similarity with superior results over SSIM.

Keywords

Image similarity structural similarity moment invariants SSIM MISM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Prashan Premaratne
    • 1
    • 2
  • Malin Premaratne
    • 1
    • 2
  1. 1.School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongNorth WollongongAustralia
  2. 2.Department of Electrical and Computer Systems EngineeringMonash UniversityVictoriaAustralia

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