An efficient image compression technique using vector quantization in multiple transform domains

Keywords

Image compression; Image processing; Signal processing -- Digital techniques

Abstract

The last few decades have witnessed what is commonly known as an "information explosion". The advent of the Internet has enabled the sharing of large amounts of information between users in almost every part of the world. This had led to increased bandwidth and storage requirements. These requirements necessitate the development of new techniques to optimize the utilization of existing bandwidth and to minimize storage requirements. In emerging areas of real-time multimedia applications, researchers have successfully developed new approaches, employing techniques such as transform coding, prediction, vector quantization, etc, to efficiently represent and consequently compress data, especially image data.

Recently, multiple transform domain representation techniques have been reported which successfully compress one and multidimensional signals. In this thesis, a novel and efficient codec using multiple transform domain representation in conjunction with split vector quantization is presented, which provides superior coding performance for images. An adaptive scheme that further enhances the representation accuracy of the above codec is also described. Results of simulations of the proposed technique are presented that confirm the improved performance of the codec.

Notes

This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by downloading and filling out the Internet Distribution Consent Agreement. You may also contact the project coordinator Kerri Bottorff for more information.

Graduation Date

2003

Degree

Master of Science (M.S.)

College

College of Engineering

Format

Print

Pages

87 p.

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

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