本論文提出了在兩台色域大小不同的顯示器上，探討彼此之間的色域相似性演算法和色域對映演算法，在相似度的部分，在CIE L*a*b*以及CIE L*C*h的色彩空間下進行，利用形狀和面積來比較兩者的相似程度，利用極座標的和矩陣資訊來進行形狀相似度比較。面積相似度則用色域面積資訊來進行相似度的比對。
在色域對映演算法之階調方面，設計有非線性階調壓縮法和裁切法。非線性階調壓縮法是將顯示器LAB色域分割成六個主色相頁，利用一次回歸法將不同集中方式和線性壓縮比例存成內插矩陣，再利用色相內插進行色域對映。裁切法是對於輸入影像做明度調降或不變明度，利用小色域的六個主色相頁的色域邊界資訊，做二維線性內插找到目標色相色相頁邊界值，再判斷輸入影像彩度在目標色相頁的內部或外部，若是在外部，裁切到邊界值，內部則保持不變。在心理物理實驗結果顯示，對於一般的自然影像而言，所測試之改良型裁切法效果較佳。

In this thesis, we proposed the gamut similarity descriptions and gamut mapping algorithms among different displays. In the gamut similarity part, we used the shape and area information to compare the color gamut similarity between multi-displays which are under the CIE L*a*b* and CIE L*C*h color space. The shape compare method use the polar quantization to get the shape similarity, and the area compare method uses the intersection of gamut to calculate gamut area similarity.
We used the non-linear tone compression and clipping methods for designing various Gamut Mapping Algorithms (GMAs). In the non-linear tone compression, we divided the color gamut into six main hue pages: [R],[Y],[G],[C],[B] and [M], and use the first-order regression model and non-linear compression ratio to store the interpolation matrices for the gamut mapping. The clipping algorithm could divided the lightness of input pixels into adjusted lightness or not, and use the information of the six boundaries of small gamut to get the destination color which could be used for determine whether the input pixel is out or in the boundary of gamut. The psychophysical experiment shows the improved clipping models are better for the natural images.

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