Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks. (January 2019)

• Record Type: Journal Article
• Authors: Kouadria, Nasreddine; Mechouek, Khaoula; Harize, Saliha; Doghmane, Noureddine
• Is Part Of: Computers & electrical engineering. Volume 73(2019)
• Page Start: 194
• Page End: 208
• Publication Date: 2019-01
• Subjects: WISNs -- Image compression -- Energy consumption -- Discrete cosine transform -- Discrete Tchebichef transform -- Region-of-interest coding; 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.2018.11.010
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A Low complexity, energy efficient image compression suitable for WSNs. The integration of DTT in lossy image compression. A ROI coding for WSNs preserving energy and improving quality. A considerable reduction in energy consumption and data transferred. Helping development of smart surveillance systems using low-power cameras. Abstract: Reducing the algorithmic complexity of image compression techniques is a major challenge in wireless image sensor networks (WISNs). Many image compression standards, such as JPEG and JPEG2000, are unsuitable for implementation in WISNs because of their high energy consumption. In this paper, a solution to this problem is proposed. It consists of a region-of-interest (ROI) based image compression using the discrete Tchebichef transform (DTT).The main idea is about compressing only the ROI instead of the whole image. The DTT is used as an alternative to the discrete cosine transform (DCT) due to its low complexity and good energy compaction. Simulation results have shown that the proposed method reduces the number of arithmetic operations, the processing/transmission energy consumption and the amount of transmitted data. The savings obtained generally exceed 50%. Furthermore, it has a competitive compression efficiency compared with the state-of-the-art image compression techniques. Graphical abstract: Image, graphical abstract