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 在本篇論文中，我們將提出二個基於mean-sorted 上的方法，來預先過濾不可能的codeword，以期減少Encoding 時，所需計算Euclidean Distance 的次數。在此演算法中，我們先將每一個codeword 中的pixels，依其值選擇不同的投影模式，並儲存在Codebook 中，或是在產生codebook 之後，便依照每一個codeword 不同的pixels的分佈，產生其獨特的投影模式。當編碼時，取出先前的紀錄，並套用在codeword以及原始vector 上，再用distortion measure 式子作為判斷。如果上述方法取出的結果符合條件，才再進一步計算Euclidean Distance。 我們提出的方法是架構在MPS 的方法之上，並且可以避免計算不必要的codeword的Euclidean Distance。因為在codebook 中搜尋時，我們不用藉著計算SED 值就可以判斷此codeword 會不會是closest codeword ;並經過實驗證明此法可以有效降低計算量。此外，因為這個方法，在對一張影像編碼的過程中，並不只是固定於某一種投影方式。而能依每個vector 的不同而有所差異，所以十分具有彈性，其效能在所有類型的圖片也同樣都有很好的表現。
 In this thesis, we propose two methods which base on mean-sorted method , to filter the impossible codeword in advance, expecting to reduce the times of the calculation of Euclidean Distance in the Encoding times.In these algorithms,we choice different projection masks for each codeword by it''s pixels value, and store in the Codebook;Or after producing the codebook , producing a unique projection mask for each codeword according to it''s distribution of pixels value.When compressing,take out the previous records, and set these on the codeword and source vectors, then use the distortion measure function to judge.If the result which was calculated by that method is matched,then calculate the Euclidean Distance further. Our proposed method is base on MPS, and can avoid unnecessary calculaion of the Euclidean Distance. Because when we search in the codebook, we do not need to calculate the SED value and can judge whether this codeword is the closest codeword;Besides, it is proved this method can reduce the calculation quantity effectively through experiment.In addition, because this method is not only fix in a certain projection mask.And can choice different types according to difference of vector，so it has much flexibility. its effect also has the good performance equally in all types of pictures.
 目錄...........................................................................i 表目錄........................................................................ii 圖目錄........................................................................iv 1. 簡介....................................................................... 1 1.1 前言...................................................................... 1 1.2 研究動機與研究目的........................................................ 1 1.3 貢獻...................................................................... 2 2 影像壓縮與向量編碼.......................................................... 3 2.1 數位影像類型.............................................................. 3 2.2 影像壓縮.................................................................. 5 2.3 向量編碼.................................................................. 5 2.4 失真壓縮影像效能評估...................................................... 8 3 向量壓縮的編碼方法......................................................... 10 3.1 基礎的方法............................................................... 10 3.2 Partial Distortion Search Method (PDS)....................................11 3.3 Mean-distortion-ordered Partial Codebook Search Algorithm (MPS) ......... 12 3.4 Integral Projection Mean-sorted Partial Search Algorithm (IPMPS) ........ 17 3.5 Fast LBG (FLBG) ......................................................... 18 4 Classify Projection Mean-sorted Partial Search Algorithm (CPMPS) .......... 21 4.1 導論..................................................................... 21 4.2 CPMPS.................................................................... 22 4.3 實驗數據................................................................. 27 5 Dynamic Projection Mean-sorted Partial Search Algorithm (DPMPS)............ 33 5.1 DPMPS ................................................................... 33 5.2 演算法................................................................... 36 5.3 實驗數據................................................................. 37 5.4 改進方法一: Regular Base ................................................ 40 5.5 改進方法二: Dynamic Base System ......................................... 47 6 實驗結果比較............................................................... 50 6.1 Codebook 大小為256 ...................................................... 54 6.2 codebook 大小為512 ...................................................... 60 6.3 codebook 大小為1024 ..................................................... 66 6.4 加權後值................................................................. 73 7 討論與未來展望............................................................. 74 7.1 討論..................................................................... 74 7.2 未來展望................................................................. 74
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