Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
Elements of information theory
Elements of information theory
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Learning the nonlinearity of Neurons from natural visual stimuli
Neural Computation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
On the Suitable Domain for SVM Training in Image Coding
The Journal of Machine Learning Research
Image compression via joint statistical characterization in the wavelet domain
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
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
Complex-valued independent component analysis of natural images
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
| Hi-index | 0.00 |
The conventional approach in computational neuroscience in favor of the efficient coding hypothesis goes from image statistics to perception. It has been argued that the behavior of the early stages of biological visual processing (e.g., spatial frequency analyzers and their nonlinearities) may be obtained from image samples and the efficient coding hypothesis using no psychophysical or physiological information. In this work we address the same issue in the opposite direction: from perception to image statistics. We show that psychophysically fitted image representation in V1 has appealing statistical properties, for example, approximate PDF factorization and substantial mutual information reduction, even though no statistical information is used to fit the V1 model. These results are complementary evidence in favor of the efficient coding hypothesis.