Abstract-In this paper, we investigate different approaches for multi/hyperspectral image compression. In particular, we compare the classic multi-2D compression approach and two different implementations of 3D approach (full 3D and hybrid) with regards to variations in spatial and spectral dimensions. All approaches are combined with a weighted Principal Component Analysis (PCA) decorrelation stage to optimize performance. For consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR, including eight metrics divided into three families. The results show the weaknesses and strengths of each approach.

This paper deals with the problem of multispectral image compression. In particular, we propose to substitute the built-in JPEG 2000 wavelet transform by an adequate multiresolution analysis that we devise within the Lifting-Scheme framework. We compare the proposed method to the classical wavelet transform within both multi-2D and full-3D compression strategies. The two strategies are combined with a PCA decorrelation stage to optimize their performance. For a consistent evaluation, we use a framework gathering four families of metrics including the largely used PSNR. Good results have been obtained showing the appropriateness of the proposed approach especially for images with large dimensions.

Science and technology progress fast, but mouse and keyboard are still used to control multimedia devices. One of the limiting factors of gesture based HCIs adoption is the detection of the user's intention to interact. This study tries to make a step in that direction with use of consumer EEG sensor headset. EEG headset records in real-time data that can help to identify intention of the user based on his emotional state. For each subject EEG responses for different stimuli are recorded. Acquiring these data allows to determine the potential of EEG based intention detection. The findings are promising and with proper implementation should allow to building a new type of HCI devices.

In this paper, we investigate different approaches for multi/hyperspectral image compression. In particular, we compare the classic Multi-2D compression approach and two different implementations of a 3D approach (Full 3D and Hybrid) with regards to variations in spatial and spectral dimensions. All approaches are combined with a spectral Principal Component Analysis (PCA) decorrelation stage to optimize performance. For consistent evaluation, we propose a larger comparison framework than the conventionally used PSNR, including eight metrics divided into three families. We also discuss the time and memory consumption difference between the three compression approaches. The results show the weaknesses and strengths of each approach.

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