Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary

Joaquin Zepeda 1, 2 Christine Guillemot 2 Ewa Kijak 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 TEMICS - Digital image processing, modeling and communication
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We introduce a new image coder which uses the Iteration Tuned and Aligned Dictionary (ITAD) as a transform to code image blocks taken over a regular grid. We establish experimentally that the ITAD structure results in lower-complexity representations that enjoy greater sparsity when compared to other recent dictionary structures. We show that this superior sparsity can be exploited successfully for compressing images belonging to specific classes of images (e.g. facial images). We further propose a global rate-distortion criterion that distributes the code bits across the various image blocks. Our evaluation shows that the proposed ITAD codec can outperform JPEG2000 by more than 2 dB at 0:25 bpp and by 0:5 dB at 0:45 bpp, accordingly producing qualitatively better reconstructions.
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https://hal.archives-ouvertes.fr/hal-00647264
Contributor : Ewa Kijak <>
Submitted on : Thursday, December 1, 2011 - 5:20:42 PM
Last modification on : Friday, November 16, 2018 - 1:25:22 AM

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  • HAL Id : hal-00647264, version 1

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Joaquin Zepeda, Christine Guillemot, Ewa Kijak. Image Compression Using Sparse Representations and the Iteration-Tuned and Aligned Dictionary. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2011, 5 (5), pp.1061 -1073. ⟨hal-00647264⟩

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