Keywords

Image quality, Gray level co-occurrence matrix, GLCM

Abstract

Gray level co-occurrence matrix has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the gray level co-occurrence matrix help understand the details about the overall image content. The aim of this research is to investigate the use of the gray level co-occurrence matrix technique as an absolute image quality metric. The underlying hypothesis is that image quality can be determined by a comparative process in which a sequence of images is compared to each other to determine the point of diminishing returns. An attempt is made to study whether the curve of image textural features versus image memory sizes can be used to decide the optimal image size. The approach used digitized images that were stored at several levels of compression. GLCM proves to be a good discriminator in studying different images however no such claim can be made for image quality. Hence the search for the best image quality metric continues.

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

2004

Semester

Fall

Advisor

Clarke, Thomas

Degree

Master of Science (M.S.)

College

College of Arts and Sciences

Degree Program

Modeling and Simulation

Format

application/pdf

Identifier

CFE0000273

URL

http://purl.fcla.edu/fcla/etd/CFE0000273

Language

English

Release Date

December 2004

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Download

Share

COinS