Plenoptic 2.0 cameras enable high-resolution light field capture by incorporating focused optical designs that differ fundamentally from traditional plenoptic 1.0 systems. These structural differences produce distinct motion characteristics that challenge existing motion estimation (ME) algorithms. In this paper, we first conduct a comprehensive statistical analysis on real captured datasets to identify the primary differences in motion vector distributions among conventional, plenoptic 1.0, and plenoptic 2.0 videos. Building on these observations, we propose a novel fast ME algorithm specifically designed for plenoptic 2.0 video coding. The proposed method performs a joint search over macropixel collocated positions (MCPs) and their neighboring regions to effectively handle the large motion deviations typically observed in plenoptic 2.0 sequences. To further improve efficiency, we introduce a macropixel-level diamond search pattern (MLDSP) that follows the center-biased motion-vector distribution at the macropixel resolution, along with a fast MCP neighbor search restricted to the top K number of MCPs with the lowest distortion costs. Experimental results demonstrate that the proposed algorithm achieves better bitrate savings and computational complexity reductions compared to existing ME methods.

翻译：全光2.0相机通过采用与传统全光1.0系统有根本区别的聚焦光学设计，实现了高分辨率光场捕获。这些结构差异产生了独特的运动特性，对现有的运动估计（ME）算法构成了挑战。本文首先对实际采集的数据集进行了全面的统计分析，以确定传统视频、全光1.0视频和全光2.0视频在运动矢量分布上的主要差异。基于这些观察，我们提出了一种专为全光2.0视频编码设计的新型快速运动估计算法。该方法对宏像素同位位置（MCPs）及其邻域进行联合搜索，以有效处理全光2.0序列中通常观察到的大幅度运动偏差。为了进一步提高效率，我们引入了一种宏像素级菱形搜索模式（MLDSP），该模式遵循宏像素分辨率下中心偏向的运动矢量分布，同时采用一种快速MCP邻域搜索，该搜索仅限于具有最低失真代价的前K个MCP。实验结果表明，与现有的运动估计方法相比，所提算法实现了更好的码率节省和计算复杂度降低。