應用於立體顯示器之二維轉立體靜態影像技術

Abstract

本論文提出一個將2D影像全自動轉換成3D立體影像的技術。即將一張2D影像轉換成可供左、右眼分別觀測的兩張影像，並應用於3D立體顯示器。此轉換技術可以分為兩部分，第一部分將2D影像編碼出具有深度資訊之深度圖，第二部分為將此深度圖解碼還原成模擬左、右雙眼觀測之立體影像。編碼部分又可以分為兩個步驟，第一步驟為基於3D立體影像特性的影像分割技術。利用3D立體影像的特性，經由去除反光效果的SSR(Single Scale Retinex)處理。同時也利用色調、飽和度、亮度為特徵，及使用傳統分類法FCM(Fuzzy C-means clustering method)，再經由Size Filter 篩選區域大小。第二步驟為深度擷取，依據影像的深度線索及物件之間深度規則，推測出此物件所具有的單一或漸進深度。如此在3D顯示器能得到更真實且舒適之視覺效果。第二部分解碼亦分為兩個步驟。第一步驟為深度圖轉換成供左、右眼觀測之兩張影像，在此提出兩種方法，第一種方法為線性平移法，此為簡化之方法，能有效加快轉換的速度。第二種方法為模擬人眼視覺之平移法，此方法為依據人眼的特性為基礎所開發之技術。但由於影像經由平移運算後會遺失部分像素的資訊，故第二部分為”破洞補償”，依照破洞的特性，選擇平均或鏡射的方法填補破洞。經過編碼和解碼的過程，可以將單一2D影像自動產生可用於立體顯示器之立體影像。並建立計算深度正確性的方法，最後以人之實際觀感和科學化數據與SANYO公司提出的CID(Computed Image Depth)演算法作為比較之依據。

In this thesis, a novel automatic 2D to 3D image conversion technique is proposed. It means to convert a 2D image into the left and right eye images and apply on the 3D stereo monitor. We can divide the conversion technique into two parts. The first part is to encode the 2D image into the depth map which includes the depth information, and the second part is to decode the depth map into the stereo images which simulate the left and right eyes. The part of encoder can be divided into two steps. The first step is the image segmentation technique based on the property of 3D stereo images. According to the property of 3D stereo image, we use SSR (Single Scale Retinex) to decrease the light reflection effect. We also use hue, saturation, and intensity to be our features and chose the traditional clustering method FCM (Fuzzy C-means clustering). And we use the size filter to filter the regions with size .The second step is depth extraction. According to the depth cues and the depth rules between each object, we estimate the objects belong to the fixed or gradual depths. We can get more real and comfortable effects on the 3D monitor. The second part, decoder can be divided into two steps. The first step is to convert the depth map into the left and right eye images. We proposed two methods. The first method is linear shift algorithm, and this is the simplified method which can increase the speed of conversion effectively. The second method is binocular vision shift algorithm, and this method is based on the property of the human vision. After shifting, some information of pixels will be lost. So the second part is “interpolating holes method”. According to the property of holes, we choose the method of average or mirror to interpolate. After the process of encoder and decoder, we can automatically generate the stereo images displayed in 3D monitor from a single 2D image and establish the method of calculating the accuracy of the depths. Finally, we compare the results of our proposed method with SANYO’s method “CID” (Computed Image Depth) algorithm by the human perception and scientific data.