近年來由於網際網路以及數位相機的盛行，個人的影像資料不斷地增加是可預期的結果。根據近期的研究，人們發現到採用離散小波轉換 (discrete wavelet transform，DWT)為基礎的壓縮技巧，其壓縮效率往往勝過以離散餘弦轉換 (discrete cosine transform，DCT)為基礎的壓縮技術。因此近年來許多以DWT為基礎的壓縮技巧不斷的被提出與發表，如JPEG2000即是以小波轉換為基礎的一靜態影像壓縮標準。其中由Taubman所提出的EBCOT為JPEG2000的核心演算法，但是其時間複雜度太高。因此由 Pearlman, Islam, Nagaraj, Said所提出的SPECK (Set Partitioning Embedded bloCK)演算法被整合在VM3.2A (Verification Model, version3.2A)中已提供一個時間複雜度較低的選擇。
在本論文中，我們提出了稱為HSPECK (Huffman SPECK)的方法，此方法是以SPECK為基礎，並利用小波轉換的特性搭配Huffman coding來提升效能。從觀察中我們發現一個編碼區塊所衍生的四個子編碼區塊的重要性樣式之間存在著某種機率分配的關係，因此我們利用霍夫曼編碼法來進一步改善編碼效能。從實驗結果中可以看出在相同的PSNR底下，位元率降低的比例約為3%至9%之間。

As a result of the popularization of the Internet and digital cameras, the increase of the amount of personal image data is huge. In recent years, many researchers have found that image compression techniques adopting discrete wavelet transform (DWT) frequently outperform techniques adopting discrete cosine transform (DCT). So a lot of compression techniques involved DWT have been proposed recently. JPEG2000, for example, is a wavelet-based still image compression standard. The kernel algorithm for JPEG2000, EBOCT, proposed by Taubman, however, takes higher computation time than other state-of-the-art wavelet-based image compression schemes such as Set Partitioning Embedded block (SPECK). SPECK proposed by Pearlman, Islam, Nagaraj, and Said is thereafter integrated into the VM3.2A (Verification Model, version 3.2A) as a low complexity solution.
In this thesis, we propose a method called HSPECK (Huffman SPECK) which is based on SPECK to further improve the compression ratio. From observation, we are able to establish the probability distribution over significance pattern of four sub-blocks generated successively along the wavelet decomposition process. Therefore, Huffman coding scheme can then be employed for coding wavelet coefficients. The experimental results show that under the same PSNR, the bit rates via the proposed approach are improved by 3% to 9%.

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