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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/1318

Title: 基因演算法在向量量化之應用
Application of Genetic Algorithm to Vector Quantization
Authors: 洪櫻慈
Ying-Tzu Hung
Contributors: 潘欣泰;鄭志宏
Shing-Tai Pan;J. H. Jeng
資訊工程學系
Keywords: 影像壓縮;向量量化;遺傳演算法;分類向量量化
Image compression;VQ;GA;CVQ
Date: 2005
Issue Date: 2011-05-24 15:12:23 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊工程學系]
Abstract: 在失真壓縮技術中向量量化(VQ)是一種高壓縮率的編碼技術,但傳統向量量化的方法必須花費大量的編碼時間與運算量,因此許多學者紛紛提出改善方法,而在搜尋全域最佳解的技術裡基因演算法(GA)是人們相當認同的演算法之一,因此本論文將改善GVQ演算法與提出一個新的基因分類向量量化(GCVQ)演算法,由於傳統的編碼簿產生,大多局限於從訓練集中產生,而這些碼向量只具有訓練集的影像特徵,未能適用所有的影像,所以GCVQ演算法以產生全域編碼簿為主旨,結合CVQ與MRVQ的技術。
Vector quantization (VQ) is an efficient image-coding technique because of its high compression ratio for distorted compression technique. However, the method of VQ always costs much encoding time and the large amount of MSE computations. Consequently, some optimization methods are used to speedup the training procedure. It is well known that Genetic Algorithm (GA) is an efficient global search method. Hence, this thesis will improve GVQ algorithm and propose a new gene classification vector quantification (GCVQ) algorithm. In the past the codebook in VQ is mostly produced from training set. Consequently, the codevectors of codebook posess only the characteristic of the image which the training set is generated. The codebook could not be suitable for all images. So in this thesis, CVQ and MRVQ are used to implement the GCVQ algorithm, and it will produces the global codebook.
Appears in Collections:[資訊工程系(所) ] 博碩士論文

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