Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Combined full-reference image quality metric linearly correlated with subjective assessment
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Statistical Modeling of Image Degradation Based on Quality Metrics
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
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
Image quality assessment based on a degradation model
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
Image information and visual quality
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Image quality assessment based on distortion-aware decision fusion
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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General-purpose image quality metrics aiming for quality prediction across various distortion types exhibit, on the whole, very limited effectiveness. In this paper, we propose a two-stage scheme to alleviate this limitation. At the first stage, probabilistic knowledge about the image distortion types is obtained based on a support-vector classification method. At the second stage, decision fusion of three existing image quality metrics is performed using the k-nearest-neighbor (k-NN) regression where the aforementioned probabilistic knowledge is utilized under an adaptive weighting scheme. We evaluate our method on the TID2008 database that is the largest publicly available image quality database containing 17 distortion types. The results strongly support the effectiveness and robustness of our method.