Personalized learning aims at recognizing learner’s cognitive skills and learning preferences in the learning environment.
Personalized learning offers flexibility in learning as blended learning. Modeling personalized learning involves identifying
learners’ styles and offering the learning options in any learning environment and associated learning management system
(LMS) in use. In this context, learning management system design involves considering heterogeneous learners’

Wavelet domain statistical models have been shown to be useful for certain applications, e.g. image compression, watermarking and Gaussian noise reduction. One of the main problems for wavelet-based compression is to overcome quantisation error efficiently. Inspired by Weber–Fechners Law, we introduce a logarithmic model that approximates the non-linearity of human perception and partially precompensates for the effect of the display device.