Improving compression time in zero-tree based image coding procedures

Valantinas, Jonas; Kancelkis, Deividas

doi:10.1109/ipta.2010.5586735

Cited by 3 publications

(2 citation statements)

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“…First and foremost, wavelets have been applied in computer science research areas such as signal processing (time series analysis, noise reduction, feature extraction, texture analysis and classification, etc. (Donoho and Johnstone, 1995;Perlibakas, 2004;Zhang et al, 2005;Molavi and Sadr, 2007;Liu et al, 2009;Chaovalit et al, 2011;Kaganami et al, 2011), data (digital signal, image, video) compression (Shapiro, 1993;Said and Pearlman, 1996;Christopoulos et al, 2000;Jensen and Cour-Harbo, 2001;Vetterli, 2001;Sudhakaret et al, 2005;Song, 2006;Valantinas and Kančelkis, 2010), data hiding techniques for images (Lai and Chang, 2006;Yang et al, 2012), data mining (data management, data mining tasks and algorithms, etc. (Shahabi et al, 2000;Hand et al, 2001;Prabakaran et al, 2006), and computer graphics (multi-resolution data representation, multi-resolution rendering, etc.…”

Section: Introductionmentioning

confidence: 99%

Improving Space Localization Properties of the Discrete Wavelet Transform

Valantinas¹,

KANCELKIS²,

VALANTINAS³

et al. 2013

Informatica

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In this paper, a modified version of the discrete wavelet transform (DWT), distinguishing itself with visibly improved space localization properties and noticeably extended potential capabilities, is proposed. The key point of this proposal is the full decorrelation of wavelet coefficients across the lower scales. This proposal can be applied to any DWT of higher orders (Le Gall, Daubechies D4, CDF 9/7, etc.). To open up new areas of practical applicability of the modified DWT, a novel exceptionally fast algorithm for computing the DWT spectra of the selected signal (image) blocks is presented. In parallel, some considerations and experimental results concerning the energy compaction property of the modified DWT are discussed.

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Section: Introductionmentioning

confidence: 99%

Improving Space Localization Properties of the Discrete Wavelet Transform

Valantinas¹,

KANCELKIS²,

VALANTINAS³

et al. 2013

Informatica

Self Cite

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“…Some other interesting ideas and developments in the wavelet-based image compression field are presented in [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A New Le Gall Wavelet-Based Approach to Progressive Encoding and Transmission of Image Blocks

Kancelkis

Valantinas

2012

ITC

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In this paper, a modified version of the discrete reversible (integer-to-integer) Le Gall wavelet transform (DLGT), distinguishing itself by apparently improved space localization properties and visibly extended potential capabilities, is proposed. The key point of the proposal-ensuring full decorrelation of Le Gall wavelet coefficients across the lower scales. Based on the latter circumstance, a novel exceptionally fast procedure for computing the integer DLGT spectra of the selected image blocks (regions of interest-ROI) is presented. It is shown that the new developments can be efficiently applied to progressive encoding and transmission of image blocks. Progressive encoding and transmission of image blocks is achieved by first transmitting a "rough" estimate of the original image, then sending further details related to one or another image block (ROI). To translate the idea into action, the zero-treebased encoder SPIHT (Set Partitioning in Hierarchical Trees) with an improved quad-tree analysis scheme is employed.

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