Lossy image compression techniques with high compression ratio, 2001

Item — Call Number: MU Thesis Pei

Identifier: b2125203

Staff Only

Scope and Contents

From the Collection:

The collection consists of theses written by students enrolled in the Monmouth University graduate Electrical Engineering program. The holdings are bound print documents that were submitted in partial fulfillment of requirements for the Master of Science degree.

Dates

Creation: 2001

Creator

Pei, Jun (Author, Person)
Erfani, Shervin (Thesis advisor, Person)

Conditions Governing Access

The collection is open for research use. Access is by appointment only.

Access to the collection is confined to the Monmouth University Library and is subject to patron policies approved by the Monmouth University Library.

Collection holdings may not be borrowed through interlibrary loan.

Research appointments are scheduled by the Monmouth University Library Archives Collections Manager (723-923-4526). A minimum of three days advance notice is required to arrange a research appointment for access to the collection.

Patrons must complete a Researcher Registration Form and provide appropriate identification to gain access to the collection holdings. Copies of these documents will be kept on file at the Monmouth University Library.

Extent

1 Items (print book) : 43 pages ; 8.5 x 11.0 inches (28 cm).

Language of Materials

English

Additional Description

Abstract

Broad-based lossy compression techniques are developed for efficient image data compression. Discrete Cosine Transform (DCT) has become the industry standard. Vector Quantization (VQ) is a relatively new coding technique. VQ image compression has become realities [sic] and practical algorithm in fast codebooks design with LBG algorithm. Hybrid DCT-VQ scheme takes advantage of both VQ and transform coding techniques and presents the basic principles of image coding with the objective of reducing the bandwidth for transmission or memory for storage. A sophisticated idea regarding more efficient image compression with an important feature in low memory or low bandwidth situations is truncating DCT-CVQ scheme whose codebook design complexity is reduced significantly and preserves the perceptual features, when compared to conventional VQ.

Partial Contents

1. Introduction -- 2. Transform coding -- 3. Vector quantization -- 4. Classified VQ -- 5. Transform (DCT) VQ design -- 6. Conclusion -- Bibliography -- Appendix A. The 8x8 block image for 2D-DCT -- Appendix B. LBG vector quantization design program -- Appendix C. The classifier design of edge-oriented classification VQ.