This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.
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.
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.
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.
This research is sponsored by National Natural Science Foundation of China (Grant No. 61701165, 61771181, 61603121, 61501168), Natural Science Foundation of Hebei Province (Grant No. F2016205182), Natural Science Foundation of Shandong Province (Grant No. ZR201702220373).
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
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