Date of Award

1-20-2006

Degree Type

Thesis

Degree Name

M.S.

Degree Program

Engineering

Department

Electrical Engineering

Major Professor

Charalampidis, Dimitrios

Second Advisor

Jovanovich, Kim

Third Advisor

Jilkov, Vesselin

Abstract

Image-related communications are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Image compression is important for effective storage and transmission of images. Many techniques have been developed in the past, including transform coding, vector quantization and neural networks. In this thesis, a novel adaptive compression technique is introduced based on adaptive rather than fixed transforms for image compression. The proposed technique is similar to Neural Network (NN)-based image compression and its superiority over other techniques is presented It is shown that the proposed algorithm results in higher image quality for a given compression ratio than existing Neural Network algorithms and that the training of this algorithm is significantly faster than the NN based algorithms. This is also compared to the JPEG in terms of Peak Signal to Noise Ratio (PSNR) for a given compression ratio and computational complexity. Advantages of this idea over JPEG are also presented in this thesis.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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