An image quality motivated complex content classifier

Image quality loss is often determined by the nature and level of image artifacts along with the image context they appear in. For example, grain may be masked by texture, and blur is tolerable in flat fields, but offensive in regions of edges and structure. This paper develops image region classifiers for complex (real life) images. Based on the content´s structure, the classes of interest are: a random field (such as sky or painted surfaces); textured regions (such as grass or line textures); regions with transients (such as edges on buildings). The linear classifiers examined use features from the optical density histogram (ODH), the cortex transform, and the co-occurrence matrix. The performance testing of the classifiers show that the best feature set size is four. Larger sets show no classification error reduction and tend to suffer from overfitting. The best performance is 3.3% misclassification, and is achieved using four features from the ODH and cortex transform. A misclassification rate of 10% is achieved using only co-occurrence matrix features. This rate drops to 4.4%, when ODH, cortex transform, and co-occurrence features are combined. The classifiers were trained on image regions assigned to each of the three classes by human observers, then tested on a larger non-overlapping image region set.