在本論文中，我們在HEVC上針對螢幕內容影像(SCIs)提出亮度導引彩度自適應抽樣方法與新穎的LM-Quad 模式之預測方法。提出之方法包含兩個部分，第一，當針對SCI之彩度4:2:0抽樣之影像做壓縮時，我們會先將原始影像做基於投票之一元線性回歸模型方法來辨識當前彩度抽樣的類型。基於辨識出的結果，我們會自適應的選出最合適的彩度4:2:0抽樣類型使用在亮度導引彩度之預測，以得到更小的預測誤差。第二，我們提出新穎的LM-Quad 模式預測方法，來提升壓縮率與影像品質。其將區塊近一步的切割成四個子區塊，並使用區域性的鄰近像素點預測各個子區塊之彩度值。因為子區塊與其區域性的鄰近像素點通常有著相似的紋理，所以可以得到較好的彩度重建影像。在實驗方面，我們使用26張SCI影像來評估所提出的方法，並與兩種最先進的方法進行比較。實驗結果顯示，本論文提出的方法在BD-rate和客觀視覺效果方面都有較佳的表現。

In this thesis, we propose a luma-guided chroma 4:2:0 prediction method of SCI images in HEVC. The proposed method consists of two parts. First, we apply the unary linear regression model to identify the 4:2:0 subsampling type of a given SCI image. Based on the identification result, we adaptively select an appropriate 4:2:0 subsampling type to perform the luma-guided chroma prediction with less residual error. Second, we develop a novel LM-quad mode to improve the compression ratio and prediction quality. LM-quad mode divides the image block into four sub-blocks, and predicts each sub-block's chroma by its local neighboring pixels. As the sub-block and its local neighboring pixels usually share similar texture, it can yield better chroma reconstruction. In the experiment, we take 26 SCI images to evaluate the proposed method and compare with two state-of-the-art methods. The results demonstrate the superiority of the proposed method in terms of BD-rate, and objective visual effect.

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