Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Chris Pollett

Second Advisor

Nada Attar

Third Advisor

Robert Chun

Keywords

Convolutional Neural Networks, Generative Adversarial Networks

Abstract

Image compression is a well-studied field of Computer Vision. Recently, many neural network based architectures have been proposed for image compression as well as enhancement. These networks are also put to use by frameworks such as end-to-end image compression.

In this project, we have explored the improvements that can be made over this framework to achieve better benchmarks in compressing images. Generative Adversarial Networks are used to generate new fake images which are very similar to original images. Single Image Super-Resolution Generative Adversarial Networks

(SI-SRGAN) can be employed to improve image quality. Our proposed architecture can be divided into four parts : image compression

module, arithmetic encoder, arithmetic decoder, image reconstruction module. This ar- chitecture is evaluated based on compression rate and the closeness of the reconstructed image to the original image.

Structural similarity metrics and peak signal to noise ratio are used to evaluate the image quality. We have also measured the net reduction in file size after compression and compared it with other lossy image compression techniques. We have achieved better results in terms of these metrics compared to legacy and newly proposed image compression algorithms. In particular, an average PSNR of 28.48 and SSIM value of 0.86 is achieved as compared to 28.45 PSNR and 0.81 SSIM value in end to end image compression framework [1]

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