Image Feature Extraction Based on the Two-Dimensional Empirical Mode Decomposition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
An Image Watermarking Method Based on Bidimensional Empirical Mode Decomposition
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05
A Comparison of Multiclass SVM Methods for Real World Natural Scenes
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
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In the field of Image Quality Assessment IQA, this paper examines a Reduced Reference RRIQA measure based on the bi-dimensional empirical mode decomposition. The proposed measure belongs to Natural Scene Statistics NSS modeling approaches. First, the reference image is decomposed into Intrinsic Mode Functions IMF; the authors then use the Generalized Gaussian Density GGD to model IMF coefficients distribution. At the receiver side, the same number of IMF is computed on the distorted image, and then the quality assessment is done by fitting error between the IMF coefficients histogram of the distorted image and the GGD estimate of IMF coefficients of the reference image, using the Kullback Leibler Divergence KLD. In addition, the authors propose a new Support Vector Machine-based classification approach to evaluate the performances of the proposed measure instead of the logistic function-based regression. Experiments were conducted on the LIVE dataset.