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Structural Similarity Index with Predictability of Image Blocks

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Details

Original languageEnglish
Title of host publication2018 IEEE 17th International Conference on Mathematical Methods in Electromagnetic Theory, MMET 2018 - Proceedings
PublisherIEEE COMPUTER SOCIETY PRESS
Pages115-118
Number of pages4
Volume2018-July
ISBN (Print)9781538654385
DOIs
Publication statusPublished - 10 Sep 2018
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Mathematical Methods in Electromagnetic Theory - Kyiv, Ukraine
Duration: 2 Jul 20185 Jul 2018

Publication series

Name
ISSN (Electronic)2161-1750

Conference

ConferenceIEEE International Conference on Mathematical Methods in Electromagnetic Theory
CountryUkraine
CityKyiv
Period2/07/185/07/18

Abstract

Structural similarity index (SSIM) is a widely used full-reference metric for assessment of visual quality of images and remote sensing data. It is calculated in a block-wise manner and is based on multiplication of three components: similarity of means of image blocks, similarity of contrasts and a correlation factor. In this paper, two modifications of SSIM are proposed. First, a fourth multiplicative component is introduced to SSIM (thus obtaining SSIM4) that describes a similarity of predictability of image blocks. A predictability for a given block is calculated as a minimal value of mean square error between the considered block and the neighboring blocks. Second, a simple scheme for calculating the metrics SSIM and SSIM4 for color images is proposed and optimized. Effectiveness of the proposed modifications is confirmed for the specialized image databases TID2013, LIVE, and FLT. In particular, the Spearman rank order correlation coefficient (SROCC) for the recently introduced FLT Database, calculated between the proposed metric color SSIM4 and mean opinion scores (MOS), has reached the value 0.85 (the best result for all compared metrics) whilst for SSIM it is equal to 0.58.

Keywords

  • image visual quality assessment, masking of unpredictable energy

Publication forum classification

Field of science, Statistics Finland