Strategies in JPEG compression using Convolutional Neural Network (CNN)

TL;DR

This paper reviews how deep learning, especially CNNs, enhances JPEG image compression by improving quality and efficiency, highlighting recent advancements and potential model adaptations.

Contribution

It provides an overview of deep learning techniques applied to JPEG compression, emphasizing CNN-based methods and their impact on compression quality.

Findings

CNN-based methods improve JPEG compression quality.

Deep learning models enhance compression efficiency.

Model adaptation further optimizes image compression.

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.