Kernel matching pursuits prioritization of wavelet coefficients for SPIHT image coding | IEEE Conference Publication | IEEE Xplore

Kernel matching pursuits prioritization of wavelet coefficients for SPIHT image coding


Abstract:

The Set Partitioning In Hierarchical Trees (SPIHT), an efficient wavelet-based progressive image-compression scheme, is oriented to minimize the mean-squared error (MSE) ...Show More

Abstract:

The Set Partitioning In Hierarchical Trees (SPIHT), an efficient wavelet-based progressive image-compression scheme, is oriented to minimize the mean-squared error (MSE) between the original and decoded imagery. In this paper, we use the kernel matching pursuits (KMP) method to estimate the importance of each wavelet sub-band for distinguishing between different textures segmented by an HMT mixture model. Before the SPIHT coding, we weight the wavelet coefficients, with the goal of achieving improved image-classification results at low bit rates. A modified SPIHT algorithm is proposed to improve the coding efficiency. The performance of the original SPIHT and the modified SPIHT algorithms is compared.
Date of Conference: 17-21 May 2004
Date Added to IEEE Xplore: 30 August 2004
Print ISBN:0-7803-8484-9
Print ISSN: 1520-6149
Conference Location: Montreal, QC, Canada

1. INTRODUCTION

It is often useful to implement compression algorithms that account for the ultimate classification task associated with the decoded imagery, such as in detecting biological abnormalities in compressed medical images and in compressing aerial imagery for remote-sensing applications. The goal is to compress the image efficiently while accounting for the fact that the decompressed image will be employed in a classification task. The overall encoding scheme is shown in Fig. 1. We here focus on wavelet-based image compression algorithms, since such now represent the state of the art and are used in practical algorithms.

References

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