Lossless compression applying linear predictive coding based on the directionality of interference patterns of a hologram.

This paper proposes lossless linear predictive coding based on the directionality of the interference patterns of a hologram. We approached this study from two aspects. First, to determine the directionality of the interference patterns, we performed differential pulse coding modulation (DPCM), segmenting interference patterns into n blocks and scanning the pixels in eight directions for each block. Then, we determined the direction that had minimum entropy, calculating entropy in each direction, and encoded the difference by DPCM in the determined direction. In the second approach, we attempted linear prediction using the prediction coefficients for the determined direction based on the first process. In this case, the DPCM was utilized only to determine the direction in which to progress prediction about the original pixel. Then, we calculated the difference between the predicted and the original, and encoded it. Through the above procedure, we derived an appropriate compression scheme for the hologram by comparing DPCM with the linear predictive coding. Experimental results showed that the compression rate of 26.7% could be obtained through the first process.

[1]  Ji-Sang Yoo,et al.  Scalable hologram video coding for adaptive transmitting service. , 2013, Applied optics.

[2]  A. Habibi,et al.  Comparison of nth-Order DPCM Encoder With Linear Transformations and Block Quantization Techniques , 1971 .

[3]  Ting-Chung Poon,et al.  Low-bit-rate computer-generated color Fresnel holography with compression ratio of over 1600 times using vector quantization [Invited]. , 2011, Applied optics.

[4]  Bahram Javidi,et al.  Compression of digital holograms for three-dimensional object reconstruction and recognition. , 2002, Applied optics.

[5]  Y. Sakamoto,et al.  Holographic display method for volume data by volume rendering. , 2019, Optics express.

[6]  David Blinder,et al.  JPEG 2000-based compression of fringe patterns for digital holographic microscopy , 2014 .

[7]  Jae-Hyeung Park,et al.  Compression of digital hologram for three-dimensional object using Wavelet-Bandelets transform. , 2011, Optics express.

[8]  Chao Zhang,et al.  Information Compression of Computer-Generated Hologram Using BP Neural Network , 2010 .

[9]  D. Gabor.  A New Microscopic Principle , 1948, Nature.

[10]  D. O'Shaughnessy,et al.  Linear predictive coding , 1988, IEEE Potentials.

[11]  Emmanouil Darakis,et al.  Compression of interference patterns with application to phase-shifting digital holography. , 2006, Applied optics.

[12]  Atsushi Kato,et al.  An electro holography using reflective LCD for enlarging visual field and viewing zone with the Fourier transform optical system in CGH , 2010, OPTO.

[13]  Guofan Jin,et al.  Phase-difference-based compression of phase-only holograms for holographic three-dimensional display. , 2018, Optics express.

[14]  Takeshi Yamaguchi,et al.  Image quality evaluation and control of computer-generated holograms , 2016, SPIE OPTO.

[15]  Yuji Sakamoto,et al.  Holographic display methods for volume data: polygon-based and MIP-based methods. , 2018, Applied optics.

[16]  Inkyu Moon,et al.  Lossless and lossy compression of quantitative phase images of red blood cells obtained by digital holographic imaging. , 2016, Applied optics.

[17]  Kyoji Matsushima,et al.  Full-color large-scaled computer-generated holograms using RGB color filters. , 2017, Optics express.

[18]  Shuming Jiao,et al.  Holographic Data Compression with JPEG Standard and Deep Learning , 2019 .