隨著照明產品的快速發展與多樣化，各式各樣的新興光源與照明燈具在我們的生活中隨處可見。然而在照明設計上，雖然有多種人工光源可供選擇與使用，但也相對更加容易產生不舒適眩光。現階段在眩光感受上通常僅考量光源的輝度以及照明場景中的明暗對比，鮮少探討光源與環境色溫對眩光程度的影響。為了量化照明場景中色溫變化對眩光的影響，以符合人類的心理層面反應及人眼感受，本研究將建立考量相關色溫之眩光評價模型。同時，為減少評估成本以及提升評估眩光之方便性，本研究提出以可攜式眩光檢測系統對環境中之眩光進行評估之方法。
　　研究初期透過人因實驗來了解可變色溫之燈具對眩光程度的影響，初步結果顯示，刺激光源色溫的改變的確會對人眼產生一定程度的眩光影響。接著探討光源色溫和環境色溫之改變對不舒適眩光之影響，並同時考量不同視角與色溫改變所帶來的變化。實驗結果發現，受測者們對於環境色溫的改變存在著兩種不同的眩光感受。第一群受測者對環境色溫與眩光程度呈正比關係，即環境色溫越高，眩光感受越為強烈。而第二群受測者對環境色溫與視覺舒適度則呈現反比關係，環境色溫越高，眩光程度越低，推測造成此差別的原因在於色對比的產生。本研究根據所有實驗的結果改良CIE統一眩光指數，並分別針對兩群受測者提出適用於現階段照明環境之眩光評價模型。在驗證場景中，針對兩群受測者分別所建立的改良眩光評價模型在眩光預測與視覺舒適度之R2可達0.84和0.9，顯示改良後眩光評價模型的確能有效預測不同色溫下之眩光程度。
　　最後根據實驗評價所建立之眩光評價模型，並透過在智慧型手機平台上進行影像處理與分析，發展一套可攜式眩光檢測系統。手機相機在進行色彩校正後其輝度誤差為4.77%，而在色溫部分，其ΔCCT僅76.5 K。在驗證場景部分，檢測系統在計算參數輝度、色溫以及立體角的平均誤差分別為4.03%、114K和4.18%，其結果顯示檢測系統在眩光計算之準確度是相當具有可靠性的。利用此套眩光檢測系統可提供快速且方便的照明環境檢測及環境眩光指數評估，未來將能應用於智慧照明的世代。

Due to the diversity of light fixture in lighting application, there is a rising concern that different kinds of light source may easily cause various degree of glare to human especially discomfort glare. Discomfort glare may cause people to look away from a bright light source and even to have painful sensation in seeing a task.
With the development of lighting fixture, more levels of correlated color temperature (CCT) of LEDs-based luminaries may be used in interior lighting. Different color temperature of luminaries may cause different degrees of discomfort glare. Therefore, we intend to develop an evaluation model to predict the visual comfort with different CCT in interior lighting based on the foundation of Unified Glare Rating (UGR). Furthermore, a portable and smart system for UGR inspection is also developed to obtain the status of various lighting scenes and evaluation UGR conveniently for lighting environment.
In this study, the influence of stimulus CCT, surround CCT, luminance and viewing angle on discomfort glare would be re-investigated. According to the results of psychological experiments, we established two new UGR models for improving prediction on observer’s discomfort glare. The correlation R2 between two different evaluation model and visual comfort were up to 0.84 and 0.9 in verification scenes. The results show improved evaluation model can really predict the degree of glare under different color temperatures.
Finally, we established glare inspection system based on the smart phone and evaluation model by image processing. The luminance measurement error of phone camera after color correction is 4.77%, and the ΔCCT is only 76.5 K. In verification scenes, the parameters of the luminance, color temperature and solid angle calculated by inspection system were 4.03%, 114 K and 4.18%. The results show that the prediction of discomfort glare by using inspection system have high reliability. With the establishment of discomfort glare inspection system, it can provide fast and convenient lighting environmental testing and environmental assessment. And we wish this inspection system can also be applied for smart lighting in next generation.

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