Evaluation of context models to code wavelet-transformed hyperspectral images

Context modeling is key in wavelet-based image coding schemes to achieve competitive coding performance. Commonly, context models are devised for a particular coding system and are employed for many different types of images. The aim of this work is to evaluate the suitability of three well-known context models for coding hyperspectral images, without focusing on a particular wavelet-based coding system. To do so, an entropy-based measure defined using the mechanisms utilized by modern image codecs is employed. The experimental results assess the appropriateness of the context models considering different coding rates and transform strategies. They reveal that some widely-used context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based codecs for hyperspectral images.