Compression of Third-Order Tensor Images Using 1D Discrete Wavelet Transform and Iterative Tensor Decomposition Techniques with Original Vector Initiation

Hyper spectral Images (usually referred to as 3rd order tensor Array Cubes), have initiated the need for compression because of their intense multidimensional volumetric data either while transferring or for storage purposes. A principal compression strategy suggested is that, for Hyperspectral images, a method of transferring 3 1D (1*N) matrices is emphasized in the place of 1 3D (N*N*N) matrix thereby increasing the Compression Factor to N2/3 effectively.
Another efficient approach to compress these Hyperspectral images is to exhibit both spatial and spectral information reduction techniques i.e. elimination of correlated information in adjacent spectral bands of the captured image. Therefore, a combination of one such method is proposed which involves:
Step1 refers to applying 3-D Discrete Wavelet Transform (DWT) to the bands of interest of the Hyperspectral image data such as NASA's Airborne Visual/Infrared Image Spectroscopy (AVIRIS) datasets (like Cuprite & Moffett field), etc. Step 2 refers to applying significant compression cum decomposition algorithms to the results of Step 1 to estimate individual 1D vector or tensor components. On processing with real-time Hyperspectral images of larger dimensions, a need for greater computational efficiency occurs, therefore Parallelized computational techniques are adapted accordingly in the MATLAB environment and their results have that ability to successfully reconstruct the original Hyperspectral images.