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dc.contributor.authorAwad Kh. Al-Asmari
dc.contributor.authorAbobakr S. Ahmed
dc.contributor.authorAbdulla A. Al-Doweesh
dc.contributor.otherElectrical Engineering Department, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
dc.contributor.otherResearch Center, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
dc.contributor.otherElectrical Engineering Department, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
dc.date.accessioned2025-10-09T05:32:46Z
dc.date.available2025-10-09T05:32:46Z
dc.date.issued01-01-1996
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1018363918306603
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/41150
dc.description.abstractIn this paper, a predictive locally adaptive vector quantization (PLAVQ) for real-time image compression is developed and simulated. It is a hybrid of improved predictor, LAVQ and (VLC). The latter is a combination of Laplacian quantizer, Huffman coding, and optimum-modified B1-code for encoding the codewords and indices, respectively. Simulation shows that an improvement in speed of about 76% can be achieved with about 1.1 dB increase in the peak signal-to-noise ratio (PSNR) at the same bit rate. Optimization of the modified B1-code decreases the bit rate with an average of 37% for the same image quality. Simulation data of PSNR and bit rate vs. error threshold can be expressed by a decaying exponential model independent of image type.
dc.language.isoEN
dc.publisherElsevier
dc.subject.lccEngineering (General). Civil engineering (General)
dc.titleImage Compression Scheme Using Improved Basic-LAVQ and Optimized VLC
dc.typeArticle
dc.description.pages251-265
dc.description.doi10.1016/S1018-3639(18)30660-3
dc.title.journalJournal of King Saud University: Engineering Sciences
dc.identifier.oaioai:doaj.org/journal:1c3cb57b31db4438be814c17d97e004f
dc.journal.infoVolume 8, Issue 2


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