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dc.contributor.advisorZhuang, Xinhuaeng
dc.contributor.authorJiang, Xiaoboeng
dc.date.issued2018eng
dc.date.submitted2018 Springeng
dc.description.abstractAn image coding algorithm, SLCCA Plus, is introduced in this dissertation. SLCCA Plus is a wavelet-based subband coding method. In wavelet-based subband coding, the input images will go through a wavelet transform and be decomposed into wavelet subband pyramids. Then the characteristics of the wavelet coefficients within and among subbands will be utilized to removing the redundancy. The rest information will be organized and go through entropy encoding. SLCCA Plus contains a series improvement method to the SLCCA. Before SLCCA, there are three top-ranked wavelet image coders. Namely, Embedded Zerotree Wavelet coder (EZW), Morphological Representation of Wavelet Date (MEWD), and Set Partitioning in Hierarchical Trees (SPIHT). They exploit either inter-subband relation among zero wavelet coefficients or within-subband clustering. SLCCA, on the other hand, outperforms these three coders by exploring both the inter- subband coefficients relations and within-subband clustering of significant wavelet coefficients. SLCCA Plus strengthens SLCCA in the following aspects: Intelligence quantization, enhanced cluster filter, potential-significant shared-zero, and improved context models. The purpose of the first three improvements is to remove redundancy information further while keeping the image error as low as possible. As a result, they achieve a better trade-off between bit cost and image quality. Moreover, the improved context lowers the entropy by refining the classification of symbols in cluster sequence and magnitude bit-planes. Lower entropy means the adaptive arithmetic coding can achieve a better coding gain. For performance evaluation, SLCCA Plus is compared to SLCCA and JPEG2000. On average, SLCCA Plus achieves 7% bit saving over JPEG 2000 and 4% over SLCCA. The results comparison shows that SLCCA Plus shows more texture and edge details at a lower bitrate.eng
dc.description.bibrefIncludes bibliographic referenceseng
dc.format.extentxi, 93 pages : illustrationeng]
dc.format.extentxi, 93 pages : illustrationeng]
dc.identifier.urihttps://hdl.handle.net/10355/66087
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsOpenAccesseng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Licenseeng
dc.titleSignificance linked connected component analysis pluseng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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