Neurocomputing
Neural networks and the bias/variance dilemma
Neural Computation
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
Co-occurrence Matrixes for the Quality Assessment of Coded Images
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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Imaging algorithms often require reliable methods to evaluate the quality effects of the visual artifacts that digital processing brings about. This paper adopts a no-reference objective method for predicting the perceived quality of images in a deterministic fashion. Principal Component Analysis is first used to assemble a set of objective features that best characterize the information in image data. Then a neural network, based on the Circular Back-Propagation (CBP) model, associates the selected features with the corresponding predictions of quality ratings and reproduces the scores process of human assessors. The neural model allows one to decouple the process of feature selection from the task of mapping features into a quality score. Results on a public database for an image-quality experiment involving JPEG compressed-images and comparisons with existing objective methods confirm the approach effectiveness.