Deep Learning-Based Compression for Phase-Only Hologram

With the aid of rapid development in optical and display devices, Holography has become an emerging technology to represent 3D objects providing the most natural and realistic depth illusion to the users. To reproduce a light field of objects, a hologram records the interference patterns between the object and reference wavefields. Especially for computer-generated holograms, they contain vast amount of data and must be efficiently compressed for storage or transmission. Although several coding solutions, including standardized compression tools and their variations, have been tried in the literature, the coding efficiency is not satisfactory because holograms have completely different unique properties from those observed in natural images, whereas the legacy coding solutions are optimized for the latter. In this work, we propose a deep-learning based image compression network for phase-only holograms. The proposed network is trained in an end-to-end manner with the help of data augmentation technique and aims at minimizing the entropy of the input hologram. In comprehensive experiments, we demonstrate that our compression network shows the state-of-the-art coding performance that even surpasses the latest compression standard Versatile Video Coding by up to 39% in terms of BD-rate gain. Furthermore, we provide in-depth analysis for experimental results in both holographic and numerical reconstruction domains, such as individual performances of the test codecs, a performance comparison with different objective image fidelity metrics, and subjective quality evaluation results.