近年來，在靜態影像壓縮方面，已開發出好幾種演算法，主要目的是希望將資料量大的影像圖片，壓成資料量小的檔案，使之方便在有限的網路頻寬傳輸時，不佔大量的頻寬量，且在接收還原之後，又不失去原始影像圖片所要表達的訊息。在眾多演算法當中，較受歡迎的是離散小波轉換壓縮編碼，利用小波轉換產生的子頻帶，所具有的樹狀結構特性來做編碼，其中Shapiro針對樹狀結構，提出零元樹觀念，之後有兩位學者Said 與 Pearlman針對Shapiro的方法加以修改，提出增加編碼效能的SPIHT(Set Partitioning in Hierarchical Trees) 分集階層樹編碼演算法。在本論文中，針對小波轉換後產生的樹狀結構，使用模糊技術來計算推論樹狀結構能量的分佈特性，進一步產生新的小波樹狀結構能量，使零元樹結構產生的機會降低，使得利用無算數編碼之分集階層編碼演算法(SPIHT)時，能更有效地找出重要係數，另外也針對於抓取之後的重要係數，使用適應性量化編碼法，來取代傳統零元樹對於重要係數編碼的二分逼近法，由這兩個方法的改進以達到在固定壓縮比時，使影像圖片在還原時，能有更高的品質提昇。

　Several notable static image compression algorithms have been developed recently. The objective of the algorithms is to reduce the large size of image files into smaller files that can be rapidly transferred through the Internet within limited transmission bandwidth; meanwhile, the original image can be retrieved in receivers without losing too much essential information. Among these developed compression algorithms, the Discrete Wavelet Transform (DWT) decomposition is one the most popular and promising framework that provides efficient quad-tree data structures that embed space-frequency localization of subband image. Shapiro was the one who first focused on quad-tree coding and proposed the embedded zerotree wavelet (EZW) algorithm. Subsequently, Said and Pearlman proposed the set partitioning in hierarchical trees (SPIHT) to further improve the drawbacks of the EZW. In this thesis, we integrated a fuzzy inference filter into the SPIHT algorithm to compute the entropy energies of wavelet-subband coefficients and to effectively determine significant coefficients. In addition, an adaptive quantization technique instead of conventional quantization techniques for coding significant is incorporated in the SPIHT coding framework to improve the overall quality of image compression.

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