本論文探討動態影像之品質特性，並嘗試量化動態影像品質。本研究共包含4個評價實驗，分別是眼動儀評價實驗、影像內容重要性評估和2個動態影像觀測評價實驗。利用改變影像的色彩屬性(色相、明亮度、飽和度、對比度、銳利度)來製作測試影像，經過2次動態影像觀測評價實驗，推導出影像品質方程式模型，綜合結果發現觀測舒適度(visual comfort, VC)對影像品質最具影響，而影像鮮豔度(vividness, V)和影像陰影或亮部細節(detail in shadow or highlight, D)則次之。本論文亦調查影像內容對於影像品質的影響，從2次動態影像觀測評價實驗的原動態影像中，各挑選出代表性場景，進行眼動儀觀測及影像內容重要性評價，根據觀測者對於影像內容物件的注視時間或關注程度，來探討它們對於動態影像品質的影響。最後，建議出『最高法(supreme method)』、『前三名權重法(weighting method)』和『均量法(isometric method)』等三種方法，從受關注的影像內容物件中，推算測試影像與原動態影像之間於CIELUV色彩空間的明度差、彩度差和色相差，並互相比較三種方法推算的色差與影像屬性變化的相關性，以量化影像品質。

The purpose of this study is to understand the characteristic of the image quality for motion pictures, and tries to quantity image quality (IQ) by image attributes. In the thesis, four IQ evaluation experiments were performed, which were Exp.1: “Evaluation of image characteristics for motion pictures”, Exp.2: “Visual assessment by using eye-tracker”, Exp.3: “Second evaluation of image characteristics for motion pictures” and Exp.4: “Evaluation of importance for objects in images”. Manipulated images were produced by adjusting the original motion picture’s colors in terms of the changes of hue, brightness, saturation, contrast and sharpness etc. After the evaluations of image characteristics for motion picture, the IQ formulae were deduced. We found the “visual comfort (VC)” of image has a great influence on IQ, while the “vividness (V)” of image and “Details in shadow and highlight (D)” has less influence on the quality. To investigate the relationship between image quality and image contents, two evaluation experiments, Exp.2 and Exp.4, were further produced to record the observers’ gazing time of interests and the degree of influence of objects in a series of selected pictures, respectively. At last, for analyzing the efficiencies of IQ models, three methods (“supreme method”, “weighting method” and “isometric method”) were introduced to calculate CIELUV color differences between manipulated images and original images. Pearson correlation coefficient was used to compare the image color characteristics and predicted color difference values.

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