An Entropy-based Model for a Fast Computation of SSIM

Vittoria Bruni, Domenico Vitulano

Abstract

The paper presents a model for assessing image quality from a subset of pixels. It is based on the fact that human beings do not explore the whole image information for quantifying its degree of distortion. Hence, the vision process can be seen in agreement with the Asymptotic Equipartition Property. The latter assures the existence of a subset of sequences of image blocks able to describe the whole image source with a prefixed and small error. Specifically, the well known Structural SIMilarity index (SSIM) has been considered. Its entropy has been used for defining a method for the selection of those image pixels that enable SSIM estimation with enough precision. Experimental results show that the proposed selection method is able to reduce the number of operations required by SSIM of about 200 times, with an estimation error less than 8%.

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Paper Citation


in Harvard Style

Bruni V. and Vitulano D. (2016). An Entropy-based Model for a Fast Computation of SSIM . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 226-233. DOI: 10.5220/0005730002260233


in Bibtex Style

@conference{visapp16,
author={Vittoria Bruni and Domenico Vitulano},
title={An Entropy-based Model for a Fast Computation of SSIM},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={226-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005730002260233},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - An Entropy-based Model for a Fast Computation of SSIM
SN - 978-989-758-175-5
AU - Bruni V.
AU - Vitulano D.
PY - 2016
SP - 226
EP - 233
DO - 10.5220/0005730002260233