Image Compression Using Deep Learning Based Multi-structure Feature Map and K-Means Clustering

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Abstract

Image compression play significant role in the data transfer and storage. Recently, deep learning has achieved tremendous success in various domain of image processing. In this paper, we propose a multi-structure Feature map-based Deep Learning approach with K-means Clustering for image compression. We first use a modified CNN to select a multi-structured region of interest MS-ROI feature map by using several stacked of convolution layers then compress the image by integrating MS-ROI map with K-means. We can establish through experimental results that the proposed approach perform better compared to traditional K-means clustering approach.

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Verma, G., & Kumar, A. (2020). Image Compression Using Deep Learning Based Multi-structure Feature Map and K-Means Clustering. In Communications in Computer and Information Science (Vol. 1240 CCIS, pp. 365–374). Springer. https://doi.org/10.1007/978-981-15-6315-7_30

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