3D modified wavelet block tree coding for hyperspectral images

Abstract

<p><span>A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors has been proposed. The proposed scheme uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients. By doing the reconstruction of the transform image cube, taking the difference between the frames, it increases the coding efficiency, reduces the memory requirement and complexity of the hyperspectral compression schemes in comparison with other state-of-the-art compression schemes.</span></p>

Curvelet Transform Based Compression Algorithm for Low Resource Hyperspectral Image Sensors

“…Te decoder follows the same overall procedure as the encoder with some low-level logical or mathematical calculations. Te inverse mathematical transform is applied to the received bit stream [13,14]. Te performance of the wavelet transform is better than that of the cosine transforms as the information about the scale and location [15].…”

“…Te selection of HSICA for a certain application depends on a number of variables. Te coding complexity, coding memory, coding gain, and data loss are the major factors for the determination of the HSICA [13]. Te rest of the manuscript is organized as follows.…”

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

“…The class to which the pixel is assigned is the one having a parameter vector that maximizes the Gaussian density function. The WMEM uses a Haar wavelet transform function that produces four outputs: image approximation, and vertical, horizontal and diagonal details of the image [30]- [32]. Two levels of approximations were produced to form a parent and a To overcome this, a mask containing all the edges of the image is formed by adding the vertical, horizontal and diagonal details images resulted from the wavelet analysis.…”

3D Medical image compression using the quincunx wavelet coupled with SPIHT

“…This initial partition is segmented recursively by means of two rules. If a set of descendants of a node is significant, it is separated into four direct child coefficients of this node, and all the other descendants [18][19][20][21][22][23]. Direct wires are added to the LIP or LSP depending on their significance.…”

“…Since the coefficients are coded in groups of four, it is interesting to treat them globally in order to exploit entropy of order greater than 1. The coefficients can only pass from the insignificant state to the signifying state; the size of the necessary alphabet to represent these changes varies according to the number of coefficients already signifying in the group [18][19][20][21][22][23].…”