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Image Compression and Retrieval Usin...

Ahamed Ayoobkhan, Mohamed Uvaze.

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Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques./
Author:	Ahamed Ayoobkhan, Mohamed Uvaze.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	150 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Contained By:	Dissertation Abstracts International80-01B(E).
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10932216
ISBN:	9780438315242

Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques.
Ahamed Ayoobkhan, Mohamed Uvaze.

Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 150 p.

Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.

Thesis (Ph.D.)--Multimedia University (Malaysia), 2017.

The topic of image compression and retrieval has become one of the most researched areas in the recent years due to the acute demand for storage and transmission of large volume of image data that are generated in the Internet and other applications. When compressing an image, it is necessary to satisfy two conflicting requirements, namely, compression ratio (CR) and the image quality which is usually measured by the parameter, peak signal-to- noise ratio (PSNR). In this thesis, several lossless and lossy image compression techniques as well as an integrated image retrieval system are proposed using prediction and wavelet based techniques. Employing prediction errors instead of the actual image pixels for compression and retrieval processes ensures data security.

ISBN: 9780438315242Subjects--Topical Terms:

532993
Information technology.

Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques.
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245 1 0 $a Image Compression and Retrieval Using Prediction and Wavelet - Based Techniques.
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300 $a 150 p.
500 $a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
500 $a Adviser: Chikkannan Eswaran.
502 $a Thesis (Ph.D.)--Multimedia University (Malaysia), 2017.
520 $a The topic of image compression and retrieval has become one of the most researched areas in the recent years due to the acute demand for storage and transmission of large volume of image data that are generated in the Internet and other applications. When compressing an image, it is necessary to satisfy two conflicting requirements, namely, compression ratio (CR) and the image quality which is usually measured by the parameter, peak signal-to- noise ratio (PSNR). In this thesis, several lossless and lossy image compression techniques as well as an integrated image retrieval system are proposed using prediction and wavelet based techniques. Employing prediction errors instead of the actual image pixels for compression and retrieval processes ensures data security.
520 $a A lossless algorithm (LLA) is proposed which uses neural network predictors and entropy encoding. Classification is performed as a pre-processing step to improve the compression ratio. For this purpose, classification algorithm1(CL1) and classification algorithm2(CL2) which make use of wavelet based contourlet transform coefficients and Fourier descriptors as features are proposed. Two identical artificial neural networks (ANNs) are employed at the compression (sending) and decompression (receiving) sides to carry out the prediction. The prediction error which is the difference between the original and the predicted pixel values is used instead of the actual image pixels. The prediction is performed in a lossless manner by rounding-off the predicted values to the nearest integer values at both sides.
590 $a School code: 1615.
650 4 $a Information technology. $3 532993
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650 4 $a Multimedia communications. $3 590562
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710 2 $a Multimedia University (Malaysia). $b Information Technology. $3 1682348
773 0 $t Dissertation Abstracts International $g 80-01B(E).
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792 $a 2017
793 $a English
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