3D 立體影像品質預測模組與製程開發研究

Abstract

依據文獻指出3D 立體影像之交錯殘影是影響影像品質和視覺疲勞最大的主要原因，常造成觀看者心理與生理的負擔。目前3D 立體光學設計乃憑藉著設計者過去先前累積的經驗或由光學軟體模擬所得到的結果，最後選擇了較保守的設計參數，這樣不僅不容易掌握設計參數品質，無形中也花費了許多時間與成本。因此，在要求設計、製造效率與加工品質前提下，若能事先掌握光學模擬設計的預測模型，以及解決設計參數的最佳化問題，確有其必要性。 本研究為建立裸眼3D 立體影像之交錯殘影品質條件下，光學模擬設計參數最佳化模組。其中應用類神經網路(Artificial Neural Network ; ANN)之倒傳遞網路(Back-Propagation Neural Network ; BPNN)，建構裸眼3D 立體影像之交錯殘影品質預測模型，以模擬的設計數據作為輸入參數，以模擬後落於兩眼的輝度值作為輸出目標值，並利用模擬實驗的量測結果來進行網路訓練與測試後，預測模型的推論值與目標值相比較。將建立類神經預測模型之預測值做為田口方法的觀測值，利用田口方法之望小特性SN比求出最佳因子組合，經確認實驗數值落在信賴區間之內，表示確認實驗落於95%信賴區間內，故本研究之類神經預測模型是可信賴的。 利用最佳因子組合為設計依據，以微柱狀透鏡為例，驗證結合奈米壓印技術製程，使用共焦3D雷射顯微鏡量測結果發現製作完成微柱狀透鏡之幾何形狀與設計值非常接近，幾何尺寸誤差都在5%以下，表面粗糙度達到光學鏡面等級，複製率達到95%，結果驗證此製程可以製作出單一曲率、無間隙、外形長扁形且高精度之微透鏡陣列。可見，設計模擬參數最佳化預測模組能有效且快速求得最佳設計參數組合，並可降低製程加工後交錯殘影品質的要求並獲得最大效益。

Crosstalk is a major factor affecting the quality of 3D stereoscopic images. High levels of crosstalk may cause visual fatigue and mental and physical discomforts. Current 3D stereoscopic optics is created based on past designs and simulation results of optical software. Using conservative design parameters not only makes the design quality difficult to control but also wastes time and costs. Creating a predictive optical simulation model is necessary to solve the optimization problem in parameter designs, thereby ensuring image quality and manufacturing efficiency. This research aims to build an optimization module for optics design parameters to improve the quality of naked-eye 3D stereoscopic images. The proposed module applies back-propagation of artificial neural network to construct a model for crosstalk prediction in naked-eye 3D stereoscopic images. The performance of the model was evaluated by using simulated design parameters as input parameters and binocular luminance as target output value. Simulation results were then utilized for network training and testing. The difference of the deduction value of the prediction model and the target value. Take the predictive value of the artificial neural prediction model as the observed value of the Taguchi method; we can determine the combination of the optimal factors, by using the signal-to-noise ratio from the smaller-the-better response of the Taguchi method. This study utilized the optimal factor combination as the design basis and the microlenticular lens as an example to verify the combination of nanoimprint technology process. The Confocal 3D laser microscopy was used to measure the molds that results show that the geometric shape of the produced microlenticular lens is close to the design values, in which the geometric error is less than 5%. The surface roughness reaches the optical specular level, and the replication rate reaches 95%. Therefore, this process can produce single curvature, gap-free, long, flat, and high-precision microlens arrays. The optimization module can lower the post-processing crosstalk quality requirement effectively. The optimization module for optics design parameters can effectively and quickly obtain the best design parameter combination and reduce the quality requirement of interlaced blur image after processing.