Comparison of data costs for depth estimation from compressed light field images

Abstract

High-quality depth estimation from light field (LF) image is an important and challenging task for which many algorithms have been developed so far. While compression is inevitably required in practice for LF data due to its huge data amount, most depth estimation methods have not yet paid sufficient attention to the effect of compression on it. In this paper, we investigate various LF depth estimation methods to design a LF compression method in the context of good depth estimation. By noting that building the data cost is a very first step in most depth estimation algorithms and the data cost computation has a great impact on eventual quality of the depth image, in this paper, we present an in-depth analysis of data cost computation in LF depth estimation problem in the context of compression. Our results show that the data cost building on Epipolar Plane Image (EPI) outperforms other tested methods in this paper and is more robust to compression.