METHODS AND ALGORITHMS FOR IMAGE COMPRESSION BASED ON MACHINE LEARNING
M. V. Gashnikov, M. A. Chubar, M. A. Yakubenko
Samara National Research University, Samara, Russia
Keywords: digital images, approximation, autoencoders, convolutional neural networks, adversarial neural networks
Abstract
An image compression technology based on machine learning is developed. Segmentation of the original image into discarded and stored zones is applied. An algorithm of compression of stored zones based on the nested coverage of the image is used. Discarded zones are replaced by a reliable fake during decompression. Machine-learning algorithms based on autoencoders, convolutional and adversarial neural networks are used at all stages of compression technology (segmentation, pixel approximation of stored zones, fake of discarded zones, etc.). Computational experiments are performed to study the proposed compression technology and the included machine learning algorithms in natural images. The results of computational experiments confirm the prospects of the proposed technology for problems related to digital image compression.
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