本論文主要提出改良式錯誤擴散區塊截斷編碼技術與使用快速查詢隱密性角度策略之半色調技術兩種方法，其內容大致如下：
第一個方法為改良式錯誤擴散區塊截斷編碼技術：首先，由於在錯誤擴散區塊截斷編碼技術當中，使用區塊內的最大值與最小值兩種數值當作量化色階，會造成脈衝雜訊的問題。因此本論文提出自適應性量化色階的方法，其主要是加入可調式參數來調整量化色階，並且統計不同區塊當中的最佳參數，即可解決脈衝雜訊的問題。除此之外，由於在錯誤擴散區塊截斷編碼技術當中的錯誤擴散權重，主要是針對半色調影像而提出，並非區塊截斷編碼影像。因此本論文提出以基因演算法，將錯誤擴散權重針對區塊截斷編碼影像重新進行最佳化。由實驗結果可以得知，在相同壓縮率的情況下，本論文所提出的方法相較於其他不同的區塊截斷編碼技術有最佳的影像品質。
第二個方法為使用快速查詢隱密性角度策略之半色調技術，該方法主要是植基於一個低計算複雜度的半色調浮水印技術。在編碼端，為了確保輸出影像為半色調影像，因此利用直接二元搜尋法結合不同紋理角度資訊來建立壓縮表。隨後，利用查詢壓縮表的方式將浮水印嵌入其中。在解碼端當中，利用最小均方演算法來增加不同維度之嵌入角度紋理特徵之間的差異性。最後，利用天真貝氏分類器將不同的角度資訊以機率的方式進行分類。由實驗結果可以得知，在影像大小為512x512的情況下，本論文所提出的方法相較於其他不同的半色調浮水印技術，除了有較佳影像品質與解碼率外，還有最快的處理效率0.6毫秒。因此本論文所提出的方法將能進一步解決半色調影像之安全性問題並提升印刷市場的商業競爭力。

In this thesis, two contributions are delivered, including improved block truncation coding using optimized error diffusion and speed-oriented halftone watermarking using implicit angle lookup strategy.
The first proposed method is an improved halftoning-based Block Truncation Coding (BTC) using optimized error diffusion. First, the error-diffused block truncation coding (EDBTC) scheme adopts the two extreme values, maximum and minimum, within a block to form the quantization levels. Yet, this induces the unpleasant visual impulse noises. Thus, an adaptive quantization levels selection strategy is proposed to ease this problem, where the adjustable parameters are added to further modify the quantization levels, and the optimized parameters for different blocks are investigated. In addition, the error kernel used in the EDBTC is, in fact, for the typical halftoning, rather than BTC-based. Consequently, the optimization on error kernel for BTC-based scheme is proposed, in which the Genetic Algorithm (GA) is adopted. Experimental results demonstrate that the proposed method can achieve the highest image quality with the same compression ratio former BTC methods.
The second proposed method is a speed-oriented halftone watermarking using implicit angle lookup strategy. Herein, the computational complexity can be significantly reduced. In encoder, the Direct Binary Search (DBS) is employed to generate the reference table to ensure the output is in halftone format. Subsequently, a number of optimized compressive tables with various texture angles are established for subsequent table lookup. In decoder, the Least-Mean-Square (LMS) enlarges the differences among those phenotypes of the embedded angles and the required number of dimensions for each angle. Finally, the naive Bayes classifier is employed to collect the probability information for classifying various angles. As documented in the experimental results, good image quality and correct detect rate can be obtained simultaneously. Moreover, a high processing efficiency of 0.6 milliseconds for an image of size 512x512 can also be achieved, which can thus increase the commercial competitive strength in printing market, in particular the security issue is well addressed.

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