Improved non-negative tensor Tucker decomposition algorithm for interference hyper-spectral image compression

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

Some Theory on Non-negative Tucker Decomposition

Some theoretical difficulties that arise from dimensionality reduction for tensors with non-negative coefficients is discussed in this paper. A necessary and sufficient condition is derived for a low nonnegative rank tensor to admit a non-negative Tucker decomposition with a core of the same non-negative rank. Moreover, we provide evidence that the only algorithm operating mode-wise, minimizing...

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...

Image compression via improved quadtree decomposition algorithms

Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quadtree de...