Adaptive digital image data compression using RIDPCM and a neural network for subimage classification

Persistent Link:
http://hdl.handle.net/10150/278109
Title:
Adaptive digital image data compression using RIDPCM and a neural network for subimage classification
Author:
Allan, Todd Stuart, 1964-
Issue Date:
1992
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 or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Recursive Interpolated Differential Pulse Code Modulation (RIDPCM) is a fast and efficient method of digital image data compression. It is a simple algorithm which produces a high quality reconstructed image at a low bit rate. However, RIDPCM compresses the entire image the same regardless of image detail. This paper introduces a variation on RIDPCM which adapts the bit rate according to the detail of the image. Adaptive RIDPCM (ARIDPCM) is accomplished by dividing the original image into smaller subimages and extracting features from them. These subimage features are passed through a trained neural network classifier. The output of the network is a class label which denotes the estimated subimage activity level or subimage type. Each class is assigned a specific bit rate and the subimage information is quantized accordingly. ARIDPCM produces a reconstructed image of higher quality than RIDPCM with the benefit of a further reduced bit rate.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Artificial Intelligence.; Computer Science.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Hunt, Bobby R.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAdaptive digital image data compression using RIDPCM and a neural network for subimage classificationen_US
dc.creatorAllan, Todd Stuart, 1964-en_US
dc.contributor.authorAllan, Todd Stuart, 1964-en_US
dc.date.issued1992en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractRecursive Interpolated Differential Pulse Code Modulation (RIDPCM) is a fast and efficient method of digital image data compression. It is a simple algorithm which produces a high quality reconstructed image at a low bit rate. However, RIDPCM compresses the entire image the same regardless of image detail. This paper introduces a variation on RIDPCM which adapts the bit rate according to the detail of the image. Adaptive RIDPCM (ARIDPCM) is accomplished by dividing the original image into smaller subimages and extracting features from them. These subimage features are passed through a trained neural network classifier. The output of the network is a class label which denotes the estimated subimage activity level or subimage type. Each class is assigned a specific bit rate and the subimage information is quantized accordingly. ARIDPCM produces a reconstructed image of higher quality than RIDPCM with the benefit of a further reduced bit rate.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectArtificial Intelligence.en_US
dc.subjectComputer Science.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHunt, Bobby R.en_US
dc.identifier.proquest1348472en_US
dc.identifier.bibrecord.b27578884en_US
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