Convolutional neural networks for enhanced compression techniques

Date of Award


Degree Name

M.S. in Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Bradley Ratliff


Image compression is a foundational topic in the world of image processing. Reducing an image's size allows for the image to be stored in less memory and speeds up the processing and storage time. In addition, deep learning (DL) has been a featured topic. This paper seeks to find a model that uses DL for optimal image compression.There are several image codecs that already are used for image compression. The framework that is designed in this paper does not focus on eliminating these codecs; rather, it uses a method that incorporates standard codecs. The image codec is wrapped with two convolutional neural networks (CNNs). The first network, ComCNN, has the goal of compressing an image into an optimal compact representation that can be passed into an image codec for maximum compression. The second network, RecCNN, has the goal of reconstructing the decoded compact representation of the image into an output that is as similar to the original image as possible. By continuing to use tradition image codes such as JPEG and JPEG2000, the process is standardized while still producing optimal results.The paper gives an overview of image compression, machine learning, and different quality and compression metrics that determine the success of the network. In addition, the model is described in great detail, and results with different parameters and data types are presented.


Electrical Engineering, Image compression, machine learning, deep learning, image processing, convolutional neural networks, compression

Rights Statement

Copyright © 2021, author.