New structural similarity measure for image comparison

Prashan Premaratne, Malin Premaratne

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationEmerging Intelligent Computing Technology and Applications
Subtitle of host publication8th International Conference, ICIC 2012, Huangshan, China, July 25-29, 2012, Proceedings
EditorsDe-Shuang Huang, Phalguni Gupta, Xiang Zhang, Prashan Premaratne
Place of PublicationHeidelberg [Germany]
Number of pages6
ISBN (Electronic)9783642318375
ISBN (Print)9783642318368
Publication statusPublished - 2012
EventInternational Conference on Intelligent Computing 2012 - Huangshan, China
Duration: 25 Jul 201229 Jul 2012
Conference number: 8th (proceedings)

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceInternational Conference on Intelligent Computing 2012
Abbreviated titleICIC 2012
OtherHost Publication title = Emerging Intelligent Computing Technology and Applications
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