本論文主要貢獻有兩個部分: 1)自適應點擴散區塊截斷編碼技術與2)多色調高效率藍雜訊抖動法，內容簡述如下：
區塊截斷編碼是一個應用了數十年的著名高效率影像壓縮技術，在本研究中提出一個新自適應點擴散區塊截斷編碼。在設計當中，輸入的原始影像依照影像各區域的複雜度自行判斷並切割成不同大小的區塊。此外，在此方法中使用一自適應參數用來控制量化色階權衡不自然紋理問題，例如區塊效應或是脈衝雜訊。另外，我們也提供一個利用人眼視覺絕對梯度誤差的評估方法，它能夠用來評估影像受到不自然紋理影響的程度。實驗結果可看出，相較於其它過往壓縮技術，本研究所提出的改良技術在四個資料庫及不同的壓縮率下都能夠產生最佳的影像品質。這表示本研究的自適應點擴散區塊截斷編碼，在高解析度影片或圖像場景等應用中都能夠具有最佳的競爭優勢。
數位多色調技術延伸自數位半色調技術，將連續色調的影像轉換成使用三色或者更多色調的量化影像。然而，過往的多色調技術大多容易產生不自然的紋理，像是帶狀效應及線性圖案，而這些問題發生的原因幾乎都是因為不均勻的點分佈，使得量化影像嚴重的降低品質。雖然迭代式多色調技術能夠產生極佳的影像，但也引入了高計算複雜度、帶狀效應及高對比問題。為了能夠改善上述情況，本論文提出多色調高效率藍雜訊抖動法。如實驗結果所呈現，本論文所提出的技術所產生的多色調影像有著傑出的色調相似度。此外，相較於過往其它的多色調技術，本論文提出的方法可達最快的處理速度，並避免了絕大多數的不自然紋理問題。綜合上述說明，本論文所提出的多色調高效率藍雜訊抖動法在實際應用上是最具優勢的候選。

In this thesis, two contributions are presented, including self-adaptive dot-diffused block truncation coding (SDBTC) and efficient blue-noise dithering for multitoning techniques (M-EBND).
Block truncation coding (BTC) is renowned for its high efficiency on image compression applications for decades. In this study, a new approach termed the self-adaptive dot-diffused BTC (SDBTC) is proposed. In this design, the image is adaptively divided into individual blocks of various sizes as per the image textural complexity. In addition, an adaptive parameter selection scheme is utilized to control quantization levels for leveraging artifacts such as blocking effect and impulse noise. In addition, a metric termed the human-visual absolute gradient error (HAGE) is formulated to objectively evaluate the artifacts of interest. Experimental results demonstrate that the proposed method can compress images with the lowest visually perceivable artifacts in contrast to state-of-the-arts across various compression ratios. Moreover, the proposed method is also able to yield the best image quality benchmarked with four datasets. It shows that the proposed method is competitive on the applications of high resolution video and images scenarios.
Multitoning technique, extended from the halftoning, converts the continuous-tone image into quantized image using more than two tones. However, the former methods easily result in the unnatural textures such as banding effect, and thus seriously degrade image quality. Although iterative multitoning methods introduce a better result, the high computational complexity is also involved. To improve visual quality and runtime, the efficient blue-noise dithering for multitoning (M-EBND) is proposed in this paper. As documented in the experimental results, excellent tone-similarity can be achieved. Comparing to the state-of-the-art methods, the proposed method features the shortest runtime, yet avoids the most artifacts. As a result, the proposed M-EBND can be a very competitive candidate for multitoning applications.

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