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Image similarity/distance measures: what is really behind MSE and SSIM?

Palubinskas, Gintautas (2017) Image similarity/distance measures: what is really behind MSE and SSIM? International Journal of Image and Data Fusion, 8 (1), pp. 32-53. Informa UK Limited. DOI: 10.1080/19479832.2016.1273259 ISBN 1947-9832 ISSN 1947-9832

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Official URL: http://dx.doi.org/10.1080/19479832.2016.1273259


Similarity/distance measures play an important role in various signal/image processing applications such as classification, clustering, change detection and matching. In most cases, maybe excluding visual perception, the distance measure should be amplitude/intensity translation invariant what means that it depends only on the relative difference of compared variables/parameters, but not on their absolute values. The two most popular measures: mean squared error (MSE) and structural similarity (SSIM) index used in image processing have been analysed theoretically and experimentally by showing their origin, similarities/differences and main properties. Both measures depend on the same parameters: sample means, standard deviations and correlation coefficient. It has been shown that SSIM originates from the two generalised Dice measures and thus inherit their main property scale invariance. Consequently, this property leads to the dependence of the measure on absolute mean and standard deviation values. Similarly, MSE depends on the absolute standard deviation values. A new composite similarity/distance measure based on means, standard deviations and correlation coefficient (CMSC) which has been proposed recently exhibits translation invariance property with respect to means and standard deviations. Experiments on simulated and real data corrupted with various types of distortions: mean shift, contrast stretching, noise (additive/multiplicative, impulsive) and blurring, supported theoretical results.

Item URL in elib:https://elib.dlr.de/110687/
Document Type:Article
Title:Image similarity/distance measures: what is really behind MSE and SSIM?
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Palubinskas, GintautasGintautas.Palubinskas (at) dlr.dehttps://orcid.org/0000-0001-7322-7917
Journal or Publication Title:International Journal of Image and Data Fusion
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1080/19479832.2016.1273259
Page Range:pp. 32-53
UNSPECIFIEDTaylor & Francis Group
Publisher:Informa UK Limited
Keywords:Similarity; distance; Euclidian; Dice; composite; correlation coefficient; translation invariant; Image processing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Palubinskas, Dr.math. Gintautas
Deposited On:13 Jan 2017 12:16
Last Modified:14 Dec 2019 04:21

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