Huang Xinpeng, An Ping, Cao Fengyin, Liu Deyang, Wu Qiang
Opt Express. 2019 Feb 4;27(3):3557-3573. doi: 10.1364/OE.27.003557.
Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.
先进的手持式全光相机正在迅速发展，以捕捉来自三维世界的光场信息。丰富的光场数据可用于生成密集子孔径图像（SAI），为用户提供更身临其境的体验。与传统二维图像不同，四维SAI包含光线的位置和方向信息；手持式全光相机的实际应用受到捕捉此类信息所需的大量数据的限制。因此，高效的光场压缩方法对于相机的进一步应用至关重要。为此，本文提出了步骤与深度估计对（PoS&DE）方法，并采用多视图视频与深度（MVD）编码结构来减轻光场编码负担。更具体地说，基于代价函数提出了一种针对SAI的精确深度估计方法，并设计了一种SAI引导的深度优化算法，以根据像素变化趋势细化初始深度图。同时，为了减少运行时间、中间SAI合成质量和编码比特率，通过大量统计实验设置了包括所选关键SAI和代价计算步骤在内的参数。通过这种方式，只需对有限数量的最优选择的SAI及其相应的深度图进行编码。实验结果表明，我们提出的使用PoS&DE的光场压缩解决方案能够获得令人满意的编码性能。
