Hierarchical Image Segmentation by Structural Content

Image quality loss resulting from artifacts depends on the nature and strength of the artifacts as well as the context or background in which they occur. In order to include the impact of image context in assessing artifact contribution to quality loss, regions must first be classified into general categories that have distinct effects on the subjective impact of the particular artifact. These effects can then be quantified to scale the artifact in a perceptually meaningful way. This paper formulates general context categories, develops automatic image region classifiers, and evaluates the classifier performance using images containing multiple categories. Linear classifiers are designed to identify three main classes which include random, textured, and transient regions. Features for identifying these areas over regions at multiple resolutions are based on the optical density histogram (ODH), the cortex transform, and the cooccurrence matrix. It was found that selecting features from ...

• Type: Journal
• Year: 2008
• Where: JSW
• Authors: Nathir A. Rawashdeh, Shaun T. Love, Kevin D. Donohue
• Keywords: Image Quality Loss, Images, JSW 2008, Multiple Resolutions