Deep-Learning-Based Dynamic Range Compression for 3D Scene Hologram
 
Deep-Learning-Based Dynamic Range Compression for 3D Scene Hologram ■
 
Tomoyoshi Shimobaba, David Blinder, Peter Schelkens, Yota Yamamoto, Ikuo Hoshi, Atsushi Shiraki, Takashi Kakue, Tomoyoshi Ito
 
Abstract ■

This study proposes a dynamic-range compression for digital holograms generated from three-dimensional scenes using deep neural network (DNN). This method uses an error diffusion algorithm to binarize holograms with an 8-bit gradation; moreover, the DNN predicts the original gradation holograms from binary holograms. This method{\textquoteright}s performance exceeds that of JPEG 2000 and high-efficiency video coding.