A SIFT DESCRIPTOR BASED METHOD FOR GLOBAL DISPARITY VECTOR ESTIMATION IN MULTIVIEW VIDEO CODING

論文發表人:劉冠顯(亞利桑那州立大學電機系博士班)

 

http://www.icme2010.org/

 

多視角視訊編碼近年來已經吸引到非常多的注意，而視差估計在多視角視訊編碼中是相當重要的一個步驟。聯合多視角視訊模型是多視角視訊編碼的一個參考軟體，在此參考軟體中則利用框架匹配法在空間上相鄰的視角上來估計整體視差向量。在我們的論文中，一個以比例不變特徵轉換為基礎的視差估計法被提出來估計整體視差向量。從實驗的結果顯示與其他經常被使用的聯合多視角視訊模型方法比較，藉由我們提出的方法在峰值訊雜比和節省位元上均能得到優勢。

 

Disparity estimation is crucial to multiview video coding (MVC), which has attracted much attention recently. In the MVC reference software, named JMVM, the global disparity vector (GDV) was estimated by frame matching on spatial neighbor views. In this paper, a scale invariant feature transform (SIFT) based disparity estimation method is proposed to estimate the GDV. The experimental results show that benefits on peak signal-to-noise ratio (PSNR) and saved bits can be obtained by adopting our proposed method compared to the JMVM method that was often used.