隨著5G通訊時代來臨和人工智慧物聯網技術的快速提升，智慧生活越來越被人們所重視。對於醫療領域來說，更大尺寸、更高解析度以及更高顯示規格的醫療用顯示器是不可或缺的。

鑒於顯示器在長時間使用下會因為操作溫度的上升，產生色衰變和光效降低的情形，進而導致顯示器產生光色飄移，因此建立一套能夠即時針對顯示器本身進行亮度及色彩校正的系統是應用上的一大重點。

為了達成此目的，便需要使用感測設備長時間測量顯示器的光色變化，而本論文以RGB色彩感測元件進
行測量，並透過多項式回歸的方式建立色彩校正模型。接著針對不同設計及數量的訓練樣本進行亮度與色彩之精確度研究，以此讓 RGB色彩感測元件能夠更加精準地再現標準色度計的量測結果。

結果表明，在訓練樣本的選擇時，色度及亮度分布的均勻性、樣本如何設計等等因素都會對校正結果產生影響，而本論文針對此進行研究，找出了最佳化的樣本設計方式，並透過常數補償，進一步降低了訓練樣本的總數。

Along with the 5G communication era and the rapid improvement of Artificial Intelligence of Things, people pay more and more attention to smart life. For the medical field, medical displays with larger sizes, higher resolutions and higher display specifications are indispensable.

In view of the fact that the display will cause color decay and decrease in light efficiency due to the increase in operating temperature for a long period of time, which will cause the display to produce light color drift. Therefore, it is very important to establish a system that can instantly correct the luminance and color
of the display itself.

In order to achieve this goal, it is necessary to use a sensing device to measure the light color change of the display for a long period of time. This paper using RGB color sensor components to measure, and establish a color correction model through polynomial regression. Then, use different designs and different numbers of training samples to research on the accuracy of luminance and color, so that the RGB color sensor can reproduce the measurement results of the standard colorimeter more accurately.

The results show that in the selection of training samples, factors such as the uniformity of the chromaticity and luminance distribution, how the samples are designed, etc., will affect the calibration results. This paper studies this and finds the optimal sample design method , And through constant compensation, further reducing the total number of training samples.

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