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.
To refer code https://github.com/anant95/K_means-image-compression.
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Verma, G., Kumar, A. (2020). Image Compression Using Deep Learning Based Multi-structure Feature Map and K-Means Clustering. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_30
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DOI: https://doi.org/10.1007/978-981-15-6315-7_30
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