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    Lossless Image Compression using Reversible Integer Wavelet Transforms and Convolutional Neural Networks

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    Author
    Ahanonu, Eze
    Issue Date
    2018
    Keywords
    Deep learning
    Image compression
    Wavelet transform
    Advisor
    Bilgin, Ali
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Image compression is an area of data compression which looks to exploit various redundancies that exist within images to reduce storage and transmission requirements. In information critical applications such as professional photography, medical diagnostics, and remote sensing, lossless image compression may be used to ensure the original data can be restored at a later time. In this work, a lossless compression framework is proposed which incorporates Convolutional Neural Networks (CNNs) to predict wavelet detail coefficients from coefficients within neighboring subbands. The main premise of the proposed framework is that information which can be recovered at the decoder via CNN prediction can be excluded from the compressed codestream, resulting in reduced file sizes. An end-to-end encoder and decoder is implemented to test the validity of the proposed, model and compression performance is compared with current state of the art methods.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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