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dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.advisorIslam, Md. Saiful
dc.contributor.authorSoobhee, Ateeq-Ur-Rahman
dc.contributor.authorRuma, Kamrun Nahar
dc.contributor.authorAhsan, Md. Fakhrul
dc.contributor.authorHossain, F. M. Fahmid
dc.date.accessioned2018-02-20T03:24:31Z
dc.date.available2018-02-20T03:24:31Z
dc.date.copyright2017
dc.date.issued12/26/2017
dc.identifier.otherID 13301025
dc.identifier.otherID 13101035
dc.identifier.otherID 14201050
dc.identifier.otherID 13301018
dc.identifier.urihttp://hdl.handle.net/10361/9511
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (pages 28-29).
dc.descriptionThis thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.description.abstractSince the beginning till present, the technology demands to store as massive data as possible in as little space as possible. As web, mobile, desktop and all other applications use image for different purposes, image compression technique has become one of the most important applications in image analysis as well as in computer science. Though image compression is an old concept, yet it’s considerably time consuming processes has opened a new field of research in image compression. In this paper, LZW (Lempel-Ziv-Welch) algorithm which is a lossless image compression algorithm with the implementation of parallel processing for faster computation has been proposed. As a consequence, the experimental result verifies much faster and satisfactory computation time in millisecond scale than the conventional technique along with keeping the decoded image in lossless format.en_US
dc.description.statementofresponsibilityAteeq-Ur-Rahman Soobhee
dc.description.statementofresponsibilityKamrun Nahar Ruma
dc.description.statementofresponsibilityMd. Fakhrul Ahsan
dc.description.statementofresponsibilityF. M. Fahmid Hossain
dc.format.extent29 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.titleFaster image compression (LZW algorithm) technique using GPU parallel processingen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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