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Gnacek thesis__final format approved LW 4-13-2021.pdf (3.96 MB)
ETD Abstract Container
Abstract Header
Convolutional Neural Networks for Enhanced Compression Techniques
Author Info
Gnacek, Matthew
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620139118743853
Abstract Details
Year and Degree
2021, Master of Science in Electrical Engineering, University of Dayton, Electrical and Computer Engineering.
Abstract
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.
Committee
Bradley Ratliff, Ph.D. (Committee Chair)
Eric Balster, Ph.D. (Committee Member)
Frank Scarpino, Ph.D. (Committee Member)
Subject Headings
Electrical Engineering
Keywords
Image compression
;
machine learning
;
deep learning
;
image processing
;
convolutional neural networks
;
compression
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Citations
Gnacek, M. (2021).
Convolutional Neural Networks for Enhanced Compression Techniques
[Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620139118743853
APA Style (7th edition)
Gnacek, Matthew.
Convolutional Neural Networks for Enhanced Compression Techniques.
2021. University of Dayton, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620139118743853.
MLA Style (8th edition)
Gnacek, Matthew. "Convolutional Neural Networks for Enhanced Compression Techniques." Master's thesis, University of Dayton, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620139118743853
Chicago Manual of Style (17th edition)
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Document number:
dayton1620139118743853
Download Count:
171
Copyright Info
© 2021, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.