A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel

Abstract

Purpose

This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

Design/methodology/approach

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Findings

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

Originality/value

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

Citation

Zhang, S., Zhang, M., Cui, Y., Liu, X., He, B. and Chen, J. (2019), "A fast ELM-based machine compression scheme for underwater image transmission on a low-bandwidth acoustic channel", Sensor Review, Vol. 39 No. 4, pp. 542-553. https://doi.org/10.1108/SR-08-2018-0204