Compression of hyper-spectral images using an accelerated nonnegative tensor decomposition

Hyper-Spectral Image Compression

Hyper-spectral images consist of huge amount of data. This paper presents a compression technique developed specifically for these images. This technique explores unique characteristics of the hyper-spectral images and incorporates them into the compression. It also exploits the wavelet transformation and bit plane entropy coding used in general image compression. In this technique, predicting ...

FLIC: Fast & Lossless Image Compression Algorithm for Hyper-Spectral Images

A Comparative Analysis of Tensor Decomposition Models Using Hyper Spectral Image

Hyper spectral imaging is a remote sensing technology, providing variety of applications such as material identification, space object identification, planetary exploitation etc. It deals with capturing continuum of images of the earth surface from different angles. Due to the multidimensional nature of the image, multi-way arrays are one of the possible solutions for analyzing hyper spectral d...

Compression of Spectral Images

In this chapter we describe methods how to compress spectral imaging data. Normally the spectral data is presented as spectral images which can be considered as generalizations of colour images. Rapid technological development in spectral imaging devices has initiated the need for the compression of raw data. Spectral imaging has been central to many remote sensing applications like geology and...

Compression of Simulation Results Database Using Tensor Decomposition

With the current trend of globalization, product design and development is often conducted collaboratively among several divisions, thus making it a huge challenge to transfer large CAE (Computer Aided Engineering) data between different locations. In this study, we have developed a data compression method based on TD (Tensor Decomposition). In this method, voxel simulation results data are rep...