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研究生:蔡阿佩
研究生(外文):A-Pei Tsai
論文名稱:植基於向量量化編碼法的影像壓縮技術
論文名稱(外文):A Study on Image Compression Based on Vector Quantization Technique
指導教授:蕭如淵蕭如淵引用關係
指導教授(外文):Ju-Yean Hsiao
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:60
中文關鍵詞:影像壓縮向量量化編碼法變動長度編碼出現次數表
外文關鍵詞:image compressionvector quantizationvariable length encoding
相關次數:
  • 被引用被引用:5
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  • 收藏至我的研究室書目清單書目收藏:1
由於網路的盛行,資料呈現的形式大多已數位化,大量的多媒體資訊例如影像、聲音…等,透過網路方便、快速的傳輸,使人們得以很快的接收到所需的訊息;如何增快傳輸速度?一般人直觀的想法不外乎加大頻寬或是減低資料量,在加大頻寬方面為滿足使用者的需求必須不斷地擴充頻寬,這對於成本而言並沒有效率,所以減低資料量是較可行的方法,目前已有很多的壓縮方法被提出來。在本篇論文中,我們提出兩個植基於向量量化編碼技術的影像壓縮方法。
在第一個方法中,我們改進向量量化編碼法在編碼時所耗費的時間,每個區塊利用額外一個位元記錄該區塊的像素值是由編碼字或是區塊平均值取代,一般而言每張影像平均有80~90%的區塊不需要透過向量量化編碼來搜尋最短距離的編碼字,因此編碼的時間便減少了,而且不論就視覺上或是數據上,所還原的影像品質與向量量化編碼法相比較皆不遜色,有的影像品質甚至比單純使用向量量化編碼法來得還要好。
在第二個方法中,我們首先將影像做向量量化編碼後,取得索引值,然後再把該索引表做第二次的壓縮,為了達到還原後的影像品質與向量量化編碼法相同,第二次壓縮採用無失真的壓縮方法,利用先前索引值出現次數統計來預測目前要編碼的索引值,加上有限狀態向量量化編碼法中狀態編碼簿的概念配合無失真的編碼技術,根據我們的實驗結果,壓縮率比以前所提出的方法來得好,我們更進一步討論狀態編碼簿大小,利用門檻值來決定使用狀態編碼簿的大小,其大小的範圍為:21~23,經由實驗可知大部分狀態編碼簿大小變動比固定大小的狀態編碼簿所產生的位元比低。
Internet becomes a popular transmission channel recently. Most data such as text, image, audio and video can be represented in digital form and transmit on the network. As we know the bandwidth is restricted, too many multimedia data transmit over Internet result in congestion or delay. The performance of network is degraded. In order to increase the compression rate but preserve more image detail, many image compression methods are proposed in the last decades. Since the human visual system is low sensitivity, we can reduce the image size but still keeping the image quality in allowable range. In this thesis, two methods based on Vector Quantization (VQ) for lossy image compression are proposed.
In the first scheme, we add an extra bit per block to indicate whether the block needs to search the nearest codeword or not. Each block is compressed according to the variance of it. If the variance is less than the predefined threshold, we represented the pixel values in the block with the mean of the block. Otherwise, we choose a nearest codeword to represent the block. We have less calculating time and better image quality than VQ after reconstructed. The experimental results reveal the compression rate of our method is almost as good as VQ.
In the second scheme, we propose a post-processing method for VQ compressed image. We apply lossless encoding technique, predictor and the concept of state codebook for lossless image compression. In our experimental results, the bit rate is lower than previous methods. We discuss the size of state codebook further. For the variable size approach, we set two thresholds to decide the size of state codebook. According to the experimental results we can find that the compression ratio of variable size is better than fixed size.
中文摘要  ……………………………………………………… i
英文摘要  ……………………………………………………… ii
誌謝  …………………………………………………………… iii
目錄  …………………………………………………………… iv
圖次  …………………………………………………………… vi
表次  …………………………………………………………… vii
第一章 緒論  ………………………………………………… 1
 第一節 研究背景與動機 …………………………………… 1
 第二節 研究目的  ………………………………………… 1
 第三節 文章架構  ………………………………………… 2
第二章 文獻探討  …………………………………………… 4
 第一節 失真壓縮方法介紹  ……………………………… 5
 第二節 無失真壓縮方法介  ……………………………… 13
 第三節 影像評估方法  …………………………………… 15
第三章 應用區塊平均值及向量量化編碼法於失真影像壓縮  17
 第一節 相關研究  ………………………………………… 17
 第二節 提出的方法  ……………………………………… 18
 第三節 實驗結果  ………………………………………… 21
第四章 植基於向量量化編碼技術的二次編碼法  ………… 35
 第一節 相關研究  ………………………………………… 35
 第二節 提出方法  ………………………………………… 39
 第三節 實驗結果  ………………………………………… 45
第五章 結論及未來研究方向  ……………………………… 55
 第一節 結論  ……………………………………………… 55
 第二節 未來研究方向  …………………………………… 56
參考文獻  ……………………………………………………… 57
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