NDLI: Compression of image ensembles using tensor decomposition

Content Provider	IEEE Xplore Digital Library
Author	Mahfoodh, A.T. Radha, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA (Mahfoodh, A.T.; Radha, H.)
Abstract	In this paper we address the problem of compressing a collection of still images of the same type, such as an image database of faces or any other visual objects of similar shapes. We refer to such collection of images as an image ensemble. The compression of image ensembles presents a unique set of requirements such as random access to any image within the collection without the need to reconstruct other images in the same ensemble. Such requirement is readily met by any still image compression standard, simply by encoding each image in isolation of other images within the same ensemble. However, traditional approaches of still image compression do not exploit the strong correlation that might exist among images within a given ensemble. In this paper we argue that a tensor-decomposition framework can achieve both: (a) random access to any image within an ensemble and (b) the exploitation of the correlation among all images within the same ensemble. To that end, we propose a progressive tensor-factorization approach that decomposes an image ensemble into a set of block-wise rank-one tensors. We derive and encode the rank-one tensors of an image ensemble using an optimization method for rank allocation among the original block tensors. Our simulation results show the viability of the proposed tensor framework when applied to image ensembles of faces.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	21
Ending Page	24
File Size	1116579
Page Count	4
File Format	PDF
e-ISBN	9781479902941
DOI	10.1109/PCS.2013.6737673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Frequency modulation Xenon Encoding Resource management Image reconstruction Biomedical imaging
Content Type	Text
Resource Type	Article

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