Image quality, Gray level co-occurrence matrix, GLCM
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.
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Master of Science (M.S.)
College of Arts and Sciences
Modeling and Simulation
Length of Campus-only Access
Masters Thesis (Open Access)
Gadkari, Dhanashree, "Image Quality Analysis Using GLCM" (2004). Electronic Theses and Dissertations, 2004-2019. 187.