Hierarchical mixtures of experts and the EM algorithm
Neural Computation
BM3E: Discriminative Density Propagation for Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual quality assessment algorithms: what does the future hold?
Multimedia Tools and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
Bayesian hierarchical mixtures of experts
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
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The success of some recently proposed multi-strategy image quality metrics supports the hypothesis that the Human Visual System (HVS) uses multiple strategies when assessing image quality, where the effect from each strategy on the final quality prediction is conditioned on the quality level of the test image. To date, how to optimally combine multiple strategies into a final quality prediction remains an unsolved problem, especially when more than two strategies are involved. In this paper, we present a data-driven combination method based on a conditional Bayesian Mixture of Experts (BME) model. This method provides an effective way to model the interaction of a flexible number of strategies. Extensive evaluation on three publicly-available image quality databases demonstrates the potential of our method.