Vector quantization and signal compression
Vector quantization and signal compression
What's wrong with mean-squared error?
Digital images and human vision
Transform Coding of Images
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Multi-dimensional Function Approximation and Regression Estimation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A tutorial on support vector regression
Statistics and Computing
SVM regression and its application to image compression
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Perceptual feedback in multigrid motion estimation using an improved DCT quantization
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Nonlinear image representation for efficient perceptual coding
IEEE Transactions on Image Processing
Regularization operators for natural images based on nonlinear perception models
IEEE Transactions on Image Processing
Combining support vector machine learning with the discrete cosine transform in image compression
IEEE Transactions on Neural Networks
Perceptual adaptive insensitivity for support vector machine image coding
IEEE Transactions on Neural Networks
SVM Based Decision Analysis and Its Granular-Based Solving
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Image Denoising with Kernels Based on Natural Image Relations
The Journal of Machine Learning Research
PCA Gaussianization for image processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Visual cortex performs a sort of non-linear ICA
NOLISP'09 Proceedings of the 2009 international conference on Advances in Nonlinear Speech Processing
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Conventional SVM-based image coding methods are founded on independently restricting the distortion in every image coefficient at some particular image representation. Geometrically, this implies allowing arbitrary signal distortions in an n-dimensional rectangle defined by the ε-insensitivity zone in each dimension of the selected image representation domain. Unfortunately, not every image representation domain is well-suited for such a simple, scalar-wise, approach because statistical and/or perceptual interactions between the coefficients may exist. These interactions imply that scalar approaches may induce distortions that do not follow the image statistics and/or are perceptually annoying. Taking into account these relations would imply using non-rectangular ε-insensitivity regions (allowing coupled distortions in different coefficients), which is beyond the conventional SVM formulation. In this paper, we report a condition on the suitable domain for developing efficient SVM image coding schemes. We analytically demonstrate that no linear domain fulfills this condition because of the statistical and perceptual inter-coefficient relations that exist in these domains. This theoretical result is experimentally confirmed by comparing SVM learning in previously reported linear domains and in a recently proposed non-linear perceptual domain that simultaneously reduces the statistical and perceptual relations (so it is closer to fulfilling the proposed condition). These results highlight the relevance of an appropriate choice of the image representation before SVM learning.