近年來虛擬實境(Virtual reality, VR)與擴增實境(Augmented reality, AR)的立體視覺、沉浸式體驗及視覺資訊接收的即時性獲得了普羅大眾與科技業的青睞，紛紛視其顯示載體：頭戴顯示器(Head-mounted display, HMD)為下世代最具潛力的計算與通訊平台。HMD作為一結合光學透鏡與雙目顯示的虛像光學顯示設備，其所帶來的視覺衝擊對於使用者視覺所感知的影像品質變化更是複雜許多。為此，本研究結合基於VR裝置的心理物理學實驗(Psychophysical experiment)與量測影像，搭配機器學習建立一套以人眼為判定標準的雜散光檢測方法：以心理物理學實驗記錄受測者恰可感知雜散光閾值做為本研究資料訓練的目標，而廣角相機拍攝的VR顯示影像則透過進行一系列的影像處理程序，針對感興趣區域進行數據萃取，將取得的數據透過機器學習的演算法訓練並建立以人眼感知為基礎的雜散光評價模型。
本研究從影像資料中萃取出四個有效的關鍵變數(刺激源影像之外半徑、刺激源影像之平均灰階值、雜散光影像之平均灰階值、雜散光影像之灰階值之標準差)，並將之與三種目標值的標註(受測者群體的平均閾值、最小閾值、最大閾值)做為監督式學習所使用的資料，最後比較了四款監督式學習演算法(KNN, Logistic regression, SVM, Random forest)所建立出的模型之效能，四款演算法的準確率(Accuracy)皆高達約九成，亦證明了未來可根據需求，利用不同百分比的閾值來標註資料，以用於預測各式細分檢測規格等級的可行性。
本研究成果不僅提供一個快速且有效的雜散光定性評價方法，並期待未來能藉此作為HMD光學系統設計、品質控管的依據，使雜散光的評價成為影像品質及VR/AR內容設計的關鍵指標之一。

In recent years, the stereoscopic vision, immersive experience, and real-time visual information reception of Virtual Reality (VR) and Augmented Reality (AR) have been favored by the general public and the technology industry, and they have seen their device carrier: Head-Mounted Display (HMD) is one of the most promising computing and communication platforms for the next generation. HMD is a virtual image optical display device that combines the optical lens modules and binocular displays, and the visual impact it brings is much more complicated than the traditional display, and so does the impact to image quality performing.
To this end, this research completed the establishment of a qualitative model from the combination of psychophysical experiments and measurement images based on VR devices. The recorded threshold data of stray light perception psychophysical experiment is used as the data training target, and the VR display image captured by the wide-angle camera is processed through a series of image processing procedures to　extract variables in the range of interest. Through finished model training by the machine learning algorithm, an evaluation method for the human eye perceived stray light was established.
This research extracts four effective key variables (the external radius of stimulus source image, the average of stimulus image’s grayscale value, the average of stray light image’s grayscale value, the standard deviation of stray light image’s grayscale value) from the image data. Then, combines them with three target labels (the average threshold, minimum threshold, and the maximum threshold of the subject group) used as supervised learning data. Finally, four supervised learning algorithms (KNN, Logistic regression, SVM, Random forest) are compared. The established model's effectiveness (accuracy) is as high as about 90% by all the four algorithms. It also proves that in the future, different percentages of thresholds can be used to label data according to demand to predict the feasibility of various subdivision inspection specifications.
This research aims not only to provide a fast and effective stray light qualitative evaluation method but also to use it as a basis for HMD optical system design and quality control in the future so that the evaluation of stray light will become one of the key indicators of image quality and also apply to VR/AR content design.

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