A modified listless strip based SPIHT for wireless multimedia sensor networks. (November 2016)

• Record Type: Journal Article
• Authors: ZainEldin, Hanaa; Elhosseini, Mostafa A.; Ali, Hesham A.
• Is Part Of: Computers & electrical engineering. Volume 56(2016)
• Page Start: 519
• Page End: 532
• Publication Date: 2016-11
• Subjects: WMSN -- SPIHT -- Listless SPIHT -- Power consumption -- PSNR; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.10.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Proposed a modified listless strip based zero tree structure (LZT) based on SPIHT. Image quality in terms of signal-to-noise ratio is improved besides memory reduction obtained. Decreasing processing time reduces system complexity and power consumption. Abstract: Set Partitioning In Hierarchical Tree (SPIHT) is considered one of the most important algorithms for reducing the size of the vision data collected by the sensor node within wireless multimedia sensor network (WMSN). The traditional SPIHT algorithm suffers from image coders complexity due to large memory requirement. This is an essential problem for the implementation on limited resource environments such as WMSN. The main objective of this paper is to introduce a listless pipelined strip based SPIHT for WMSN to reduce system complexity and minimize processing time and memory usage. The proposed algorithm is implemented using discrete wavelet transform (DWT) lifting-based instead of DWT convolution-based filter. The experimental results show the superiority of the proposed algorithm in terms of peak signal-to-noise ratio (PSNR) which reaches 1 dB for all bit rates. In addition, the memory requirement is reduced to 71% with 27% of energy saving. Graphical abstract: