CBP neural network for objective assessment of image quality

This work applies neural-network technologies to the quality assessment of digital pictures processed by image-enhancement algorithms. The objective model uses a circular back-propagation (CPB) neural network to mimic human perception: the feed-forward structure maps input \´feature\´ vectors characterizing images into the associated quality ratings, obtained from human voters. "Objective" feature vectors describe images by measuring global statistical properties, which are worked out on a block-by-block basis. CPB networks can handle multidimensional data with non-linear relationships; at the same time, the neural model allows one to decouple the feature-selection task from the mapping-function set-up. Experimental results confirm the approach effectiveness, as the system provides a satisfactory approximation of the results of tests involving human viewers.