隨著3D列印的技術逐漸成熟，市場上已經販售許多單色3D列印的設備，全彩3D列印也是指日可待。由於彩色3D列印須要經過固化劑浸泡以及表面拋光等後加工使成品的硬度提高、表面光滑化。但這會使得成品表面的色澤不一致。立體物件的表面無法用色差儀直接量測，因此須要開發評估立體物件色差的技術。為此，本研究設計了一系列的心理物理實驗評估視覺色差，觀測的對象有實體物件與HDR影像。本研究探討了低動態範圍(LDR)到高動態範圍(HDR)偏好的階調調變模式、以及使用相機拍攝不同曝光之LDR影像融合成HDR影像的優化模式。從立體樣本上討論了虛擬單色簡單模型、實體複雜模型以及影像化的複雜模型。最後將視覺色差實驗的結果用於修正既有的色差公式，並建立影像化後的立體物件色差評估模式，找出單一數值的綜合性色差評價指標。
結果表示最好的LDR到HDR階調調變模式是使用50%的屈膝曲線線性調整，本研究也將此應用到建立虛擬模型之影像上。本研究使用了3階多項式迴歸，並且在對數曲線上調整得到接近真實環境亮度之HDR影像。視覺色差的分析上，結果表示3D物件使用HDR方式顯示影像能夠較接近真實感受。單色模型的分析中，不論哪一種方式進行觀測評估視覺色差，CIE94與CIE2000色差公式優化結果的加權參數大約等於(KL,KC,KH)=(2:1:1.5)，這可能意味著高反光和較深的陰影對視覺色差的判斷較不重要。且人眼較能感受到深色的視覺色差。多色模型的分析中，結果表明對於人型模型膚色的正確的重要性，且影像觀看會降低人眼對於膚色區域性變化的差異。最後本研究使用視覺色差得到的優化色差參數，系統化的找出影像上綜合性視覺色差評價後的單一指標。結果表示，影像色差色差第百分之九十五分位數與視覺色差相關度最高。

3D printing is widespread in recent years. Color 3D printers are also available in the market now for rapid prototyping and small amount productions. However, evaluating the quality of 3D printing is an important but unresolved issue. The aim of this study is to evaluate color differences of 3D objects based on the HDR (High Dynamic Range) image. The experiment established a series of psychophysical experiments to evaluate the visual color differences. Observations includes entity viewing and image viewing. The image includes LDR (Low Dynamic Range) to HDR conversion. Multi-exposure LDR photos were integrated as HDR image using 3rd polynomial regression in logarithmic scales. In terms of color differences of 3D objects, simple and complex objects were tested psychophysically. The results were used to develop an imaging system for evaluating color differences of 3D objects.
The psychophysical experiment results show that the best LDR to HDR conversion method is to apply a knee function to adjust the tone of an LDR image. In the analysis of visual color differences, the results show that the 3D objects displayed in HDR mode can be closer to the real feeling compared to LDR mode. In the analysis of the monochromatic model, no matter which way to observe and evaluate the visual color differences, the weighting parameters of CIE94 and CIE2000 color difference formulae optimization result is approximately equal to (KL, KC, KH) = (2:1:1.5), which suggests that highlight and deep shadows are less important in the perceptual color differences. And the human eye would sense dark color difference easily. In the analysis of the multicolor model, the results indicate that the correct importance of the skin color of the human model. In terms of image differences, the results show that the perceptual color differences are well-correlated to 95th percentile of the image color differences.

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