A Secure Lossless Image Compression Based on Discrete Spatial Multilayer Perceptron with Semantic Polynomial Blue Fish Algorithm

A Secure Lossless Image Compression Based on Discrete Spatial Multilayer Perceptron with Semantic Polynomial Blue Fish Algorithm

© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : P. Renukadevi, M. Syed Mohamed

How to Cite?

P. Renukadevi, M. Syed Mohamed, "A Secure Lossless Image Compression Based on Discrete Spatial Multilayer Perceptron with Semantic Polynomial Blue Fish Algorithm," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 212-221, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P224

Digital medical imaging has been a valuable platform in health centres to encourage decision-making and treatment. The medical image occupies huge memory sizes, and the scale continues to increase because of medical image technology trends. Telemedicine technology allows physicians to exchange the patient picture to facilitate the exchange of information for the diagnosis and analysis of the image. With zero loss of detail, the health system must ensure the rapid and safe distribution of the medical image correctly. The compression of images is useful to ensure that these data are shared. In storage and transmission, the function of compression is unavoidable. The discrete spatial multilayer perceptron based image compression is proposed in this work for the compression of retinal fundal medical images. The input images are preprocessed by the weighted adaptive median filter, and the histogram of the image can get equalized by the laplacian partial differential equation. Then the enhanced image pixels are scanned and subjected to a symbol coding approximation process. The approximated coefficients are subjected to quantization and encoded by spatial domain transformation. Then the compressed image can be securely stored in a cloud environment by using the Semantic polynomial blue fish algorithm. All the experimental simulations are obtained in the Python environment. The obtained results illustrated that the suggested algorithm has good performance in imperceptibility, security, efficiency and capacity.

Digital medical fundal images, Discrete spatial multilayer perceptron, Laplacian partial differential equation, Semantic polynomial blue fish algorithm, Weighted adaptive median filter.

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