Paper Info
Title
Low-Complexity Block Tree Image Coder For Visual Sensor Networks
Abstract
The wavelet block tree coding (WBTC) algorithm is an efficient wavelet-based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low-Complexity Block Tree Coding (LCBTC) algorithm that uses two state-tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit-plane. However, it encodes the coefficients in block-tree manner using depth-first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state-of-the-art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real-time visual sensor networks.
Year
DOI
Venue
2020
10.1049/iet-ipr.2020.0124
IET IMAGE PROCESSING
Keywords
DocType
Volume
data compression, tree codes, image reconstruction, wavelet transforms, image sensors, image coding, discrete wavelet transforms, trees (mathematics), low-complexity block tree image coder, wavelet block tree coding algorithm, WBTC, efficient block-tree based image coder, multiple spatial orientation trees, single block tree, size lists, conventional discrete wavelet, transformed coefficients, low-complexity block-tree coder, LCBTC, state-tables, transformed image, encoding passes, block-tree manner, list size, image size, particular wavelet decomposition level, fixed size, fractional wavelet filter, conventional DWT, memory requirement, image quality, reconstructed image, real-time visual sensor networks, memory size 2, 69 KByte, memory size 10, 21 KByte, memory size 40, 24 KByte
Journal
14
Issue
ISSN
Citations 
16
1751-9659
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Mohd Rafi Lone100.34
E. Khan25612.71