In recent years, the light field (LF) as a new imaging modality has attracted wide interest. The large data volume of LF images poses great challenge to LF image coding, and the LF images captured by different devices show significant differences in angular domain. In this paper we propose a view prediction framework to handle LF image coding with various sampling density. All LF images are represented as view arrays. We first partition the views into reference view (RV) set and intermediate view (IV) set. The RVs are rearranged into a pseudo sequence and directly compressed by a video encoder. Other views are then predicted by the RVs. To exploit the four dimensional signal structure, we propose the linear approximation prior (LAP) to reveal the correlation among LF views and efficiently remove the LF data redundancy. Based on the LAP, a distortion minimization interpolation (DMI) method is used to predict IVs. To robustly handle the LF images with different sampling density, we propose an Iteratively Updating depth image based rendering (IU-DIBR) method to extend our DMI. Some auxiliary views are generated to cover the target region and then the DMI calculates reconstruction coefficients for the IVs. Different view partition patterns are also explored. Extensive experiments on different types LF images also valid the efficiency of the proposed method.

近年来，作为新的成像方式的光场（LF）引起了广泛的兴趣。LF图像的大数据量对LF图像编码提出了很大的挑战，并且不同设备捕获的LF图像在角度域上显示出显着差异。在本文中，我们提出了一种视图预测框架来处理具有各种采样密度的低频图像编码。所有LF图像均表示为视图数组。我们首先将视图分为参考视图（RV）集和中间视图（IV）集。RV重新排列为伪序列，并由视频编码器直接压缩。然后，RV会预测其他视图。为了利用四维信号结构，我们提出了线性近似先验（LAP），以揭示LF视图之间的相关性并有效地去除LF数据冗余。根据LAP，失真最小插值（DMI）方法用于预测IV。为了稳健地处理具有不同采样密度的LF图像，我们提出了一种基于迭代更新深度图的渲染（IU-DIBR）方法来扩展DMI。生成一些辅助视图以覆盖目标区域，然后DMI计算IV的重建系数。还探讨了不同的视图分区模式。在不同类型的LF图像上进行的大量实验也证明了该方法的有效性。生成一些辅助视图以覆盖目标区域，然后DMI计算IV的重建系数。还探讨了不同的视图分区模式。在不同类型的LF图像上进行的大量实验也证明了该方法的有效性。生成一些辅助视图以覆盖目标区域，然后DMI计算IV的重建系数。还探讨了不同的视图分区模式。在不同类型的LF图像上进行的大量实验也证明了该方法的有效性。