視窗影像編碼研究

Abstract

視窗影像是泛指電腦所生成圖像，其中包含文字、電腦繪圖等，且伴隨自然影像。傳統混合式編碼為主的壓縮技術並無法有效壓縮此類影像，因此本文將視窗影像分成文字像素及圖像像素，並針對圖像像素做不同演算法比較。圖像像素可分為電腦繪圖，例如漸層繪圖、紋理樣式等，以及實際拍攝下的自然影像。所使用編碼演算法中，有針對漸層的二元一次方程式，或是自然影像的傳統離散餘弦轉換。但圖像像素並非完整區塊，因此離散餘弦轉換時，必須將空缺填補或使用針對非完整區塊設計的離散餘弦轉換，其為匹配演算法(Matching Pursuit)及外型可調式離散餘弦轉換(Shape Adaptive Discrete Cosine Transform, SADCT)。客觀數據下使用匹配演算法，不管圖像像素為漸層或是自然影像下有不錯壓縮效果，且可視為編碼端問題，不需更改解碼端設計。但圖像像素為紋理樣式時無好處，其中可能為使用的基底對於紋理樣式無法表示。另外採用外型可調式離散餘弦轉換演算法，由於會根據像素個數，採用不同長度的離散餘弦轉換，因此造成主觀上不連續之問題，且大區塊下會有更明顯的失真現象。針對漸層的圖像像素，由於二元一次方程式可產生相似的漸層背景，因此主觀上能得到好處。至於圖像像素為紋理樣式時，採用調色盤(Palette based)這類傳送基底顏色，並編碼各像素所對應的基底顏色，高位元率時能有較佳表現。

Screen contents are synthesized images generated by computer, which are generally composed by computer generated text, graphics, and natural images. Traditional hybrid video coding is inefficient to deal with such contents. To achieve a higher coding efficiency, this thesis classifies screen contents into text and pictorial pixels, and aims at comparing the performances of different methods for coding pictorial pixels. There are two kinds of pictorial pixels: one of which is computer graphics such as color gradient, graphic texture, and etc., and the other is natural images. The polynomial function is proposed mainly for dealing with color gradient while traditional discrete cosine transform is suitable for natural image. However, pictorial pixels cannot compose an entire image, and those non-pictorial pixels, which are viewed as holes, need to be filled before such incomplete image can be processed by discrete cosine transform. Other than that, this thesis also investigates other methods, such as matching pursuit and shape adaptive discrete cosine transform (SADCT), for incomplete images. The matching pursuit performs adequately for both color gradient and natural image, and is friendly for hardware design of the core transformation at decoder. However, the bases of matching pursuit may sometimes not suitable for characterizing the signal characteristics of graphic texture. A 2-D separable SADCT transform incorporates with variable-length 1-D DCT bases according to the horizontal and vertical lengths of input pixels. It will cause subjective discontinuity and will have distinct distortion on big fragment. The polynomial function works well on color gradient due to the capability of the polynomial function for generating similar gradient background. On the other hand, graphic texture works well at high rate by using palette-based scheme.