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A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure | IEEE Journals & Magazine | IEEE Xplore

A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure


Abstract:

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient wavelet-based progressive image-compression technique, designed to minimize the mean-squared ...Show More

Abstract:

The set partitioning in hierarchical trees (SPIHT) algorithm is an efficient wavelet-based progressive image-compression technique, designed to minimize the mean-squared error (MSE) between the original and decoded imagery. However, the MSE-based distortion measure is not in general well correlated with image-recognition quality, especially at low bit rates. Specifically, low-amplitude wavelet coefficients that may be important for classification are given low priority by conventional SPIHT. In this paper, we use the kernel matching pursuits (KMP) method to autonomously estimate the importance of each wavelet subband for distinguishing between different textures, with textural segmentation first performed via a hidden Markov tree. Based on subband importance determined via KMP, we scale the wavelet coefficients prior to SPIHT coding, with the goal of minimizing a Lagrangian distortion based jointly on the MSE and classification error. For comparison we consider Bayes tree-structured vector quantization (B-TSVQ), also designed to obtain a tradeoff between MSE and classification error. The performances of the original SPIHT, the modified SPIHT, and B-TSVQ are compared.
Published in: IEEE Transactions on Image Processing ( Volume: 15, Issue: 3, March 2006)
Page(s): 713 - 725
Date of Publication: 13 February 2006

ISSN Information:

PubMed ID: 16519357

I. Introduction

When performing compression at relatively low bit rates, there is in general information lost between the original image and that recovered after decoding. Most compression schemes are based on minimizing the mean-square error (MSE) between the original and compressed imagery. While this is a natural direction in many applications, there are problems for which one will ultimately make a classification decision based on the decoded imagery. For example, in medical-image compression, for transmission or storage, an expert will often make a diagnosis based on the decoded imagery [3]. In remote sensing, one often collects very large quantities of data (e.g., infrared or synthetic-aperture-radar imagery), necessitating low-bit-rate compression. In the remote-sensing problem, humans will also often make decisions based on the decoded imagery. It is therefore desirable to encode the original imagery in a manner that accounts for the ultimate classification task, this motivating consideration of non-MSE distortion measures and, hence, modification of the associated encoders/decoders.

References

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