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 regigns at multiple resolutions are based on the optical density histogram (ODH), the coktex transform, and the co-occurrence matrix. It was found that selecting features from the ODH and cortex transform provides classification results in agreement with human assessment, and performances comparable to those of classifiers using co-occurrence matrix features. Experiments to assess performance show misclassification rates ranging from 3.3% for the lowest resolutions to 32.2% at highest. This paper also presents a hierarchical classification algorithm that combines classifiers operating at multiple resolutions and achieves an overall misclassification rate as low as 4.8%.