Strategies in JPEG compression using Convolutional Neural Network (CNN)

2021/12/06 by Suman Kunwar, Kunwar, Suman
Engineering · #FOS: Electrical engineering #Image and Video Processing (eess.IV) #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2112.04500

arxiv created 2021/12/06 · arxiv updated 2021/12/10

Abstract

Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This paper describes an overview of JPEG Compression, Discrete Fourier Transform (DFT), Convolutional Neural Network (CNN), quality metrics to measure the performance of image compression and discuss the advancement of deep learning for image compression mostly focused on JPEG, and suggests that adaptation of model improve the compression.

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