NPR(Non-photorealistic Rendering)主要目的是透過不同的演算法，由電腦自動產生各種不同繪畫風格的影像，目前的NPR系統礙於演算法的計算速度，多數都僅針對靜止的單一圖片進行處理，故本研究試圖對二維空間中已發展的NPR演算法做延伸，在空間領域以及MPEG壓縮領域上分別提出不同的加速效能方式。在空間方面針對不同的範圍套用NPR演算法，如臉部、膚色區塊等有意義的部份；而在MPEG壓縮格式上，透過MPEG中的I,P,B-frame不同的特性，視影像中的差異度做不同的套用方式，以求改進NPR演算法效能，達到即時產生NPR特效的影片或動畫，進一步應用於多媒體娛樂以及人機互動機制。

Recently, various non-photorealistic rendering (NPR) techniques have been developed for computers to generate images of different artistic styles automatically. Due to the complexity of the algorithms, however, most NPR methods are limited to the processing of static images. It is the objective of this thesis to extend and improve existing NPR techniques to enable near real-time processing of video.

The enhancement can be achieved in both spatial and compressed domains. In the spatial domain, computational complexity is reduced by applying NPR only to selective regions in the images, e.g., face or skin area. In the MPEG compressed domain, by exploiting the relationship among I, P, and B frames, different strategies can be developed to increase the efficiency of the NPR algorithm. Experimental results have demonstrated the efficacy of the proposed methods and validated the near real-time creation of NPR video effects.

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URL:<http://ise.stanford.edu/class/ee368a_proj00/project18/index.html>
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