Abstract

Presented are two new methods based on entropy for reconstructing images compressed with the Discrete Cosine transform. One method is based upon a sequential implementation of the Minimum Relative Entropy Principle; the other is based upon the Maximum Entropy Principle. These will be compared with each other and with the conventional method employing the Inverse Discrete Cosine transform. Chapter 2 describes the traditional use of the Discrete Cosine transform for image compression. Chapter 3 explains the theory and implementation of the entropy-based reconstructions. It introduces a fast algorithm for the Maximum Entropy Principle. Chapter 4 compares the numerical performance of the three reconstruction methods. Chapter 5 shows the theoretical convergence limit of the iterative implementation of the Minimum Relative Entropy Principle to equal the limit of the convergence of the Maximum Relative Entropy method. Preliminary results of this thesis were presented at Southeastern '87 in Tampa. Final results will be presented at the Annual Meeting of the American Optical Society in Rochester on October 19, 1987.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

1987

Semester

Fall

Advisor

Tzannes, Nicolaos S.

Degree

Master of Science (M.S.)

College

College of Engineering

Format

PDF

Pages

135 p.

Language

English

Rights

Public Domain

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0021492

Accessibility Status

Searchable text

Download

Included in

Engineering Commons

Share

COinS