Low-bit-rate computer-generated color Fresnel holography with compression ratio of over 1600 times using vector quantization [Invited]

Neural compression for hologram images and videos

Holographic near-eye displays can deliver high-quality three-dimensional (3D) imagery with focus cues. However, the content resolution required to simultaneously support a wide field of view and a sufficiently large eyebox is enormous. The consequent data storage and streaming overheads pose a big challenge for practical virtual and augmented reality (VR/AR) applications. We present a deep-learning-based method for efficiently compressing complex-valued hologram images and videos. We demonstrate superior performance over the conventional image and video codecs.

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.

Dynamic-range compression scheme for digital hologram using a deep neural network

This Letter aims to propose a dynamic-range compression and decompression scheme for digital holograms that uses a deep neural network (DNN). The proposed scheme uses simple thresholding to compress the dynamic range of holograms with 8-bit gradation to binary holograms. Although this can decrease the amount of data by one-eighth, the binarization strongly degrades the image quality of the reconstructed images. The proposed scheme uses a DNN to predict the original gradation holograms from the binary holograms, and the error-diffusion algorithm of the binarization process contributes significantly to training the DNN. The performance of the scheme exceeds that of modern compression techniques such as JPEG 2000 and high-efficiency video coding.

Wavelet compression of off-axis digital holograms using real/imaginary and amplitude/phase parts

Compression of digital holograms allows one to store, transmit, and reconstruct large sets of holographic data. There are many digital image compression methods, and usually wavelets are used for this task. However, many significant specialties exist for compression of digital holograms. As a result, it is preferential to use a set of methods that includes filtering, scalar and vector quantization, wavelet processing, etc. These methods in conjunction allow one to achieve an acceptable quality of reconstructed images and significant compression ratios. In this paper, wavelet compression of amplitude/phase and real/imaginary parts of the Fourier spectrum of filtered off-axis digital holograms is compared. The combination of frequency filtering, compression of the obtained spectral components, and extra compression of the wavelet decomposition coefficients by threshold processing and quantization is analyzed. Computer-generated and experimentally recorded digital holograms are compressed. The quality of the obtained reconstructed images is estimated. The results demonstrate the possibility of compression ratios of 380 using real/imaginary parts. Amplitude/phase compression allows ratios that are a factor of 2-4 lower for obtaining similar quality of reconstructed objects.

Compression scheme of electro-holography based on the vector quantization of point light sources

Video holography has attracted attention after its invention in 1947; however, the enormous amount of data involved in recording and transmitting three-dimensional (3D) images remains a serious issue in electro-holography. Majority of the studies that have investigated holography transmission target the system that transmits the 3D images by compressing the holograms created on the distributor side using various compression techniques such as the conventional video compression techniques. However, the importance of the information in frequency space and characteristics, such as the correlation between adjacent pixels and frames, is different in natural images and holograms; therefore, these approaches are not always effective. In this study, we propose an effective electro-holography compression scheme based on the vector quantization of point light sources (PLSs). Instead of directly compressing a hologram, our method compresses and transmits PLSs from the distributor side and generates a hologram on the receiver side. To reduce the computational load that is required for creating a computer-generated hologram (CGH) on the receiver side, a fast CGH calculation technique has been developed for the vector-quantized PLS data based on the lookup tables (LUTs). This reduces the data rate by 76% when compared to that observed in case of uncompressed CGH transmission with 2K resolution and results in a calculation speed that is 1.34 times faster than that obtained using the conventional LUT method.

Phase-difference-based compression of phase-only holograms for holographic three-dimensional display

In holographic three-dimensional (3D) display, holograms for reconstructing 3D scenes require huge storage space and high transmission bandwidth. Holographic data must be compressed for practical applications. The holograms for 3D display are commonly represented in pure-phase format. Existing hologram compression techniques are generally not designed to handle phase-only holograms. In this paper, we propose a phase-difference-based compression method to compress phase-only holograms for holographic 3D display. Phase-only holograms are decomposed into grayscale images representing the phase distance and binary images containing the sign information of the phase difference. The grayscale images can be better handled by common image compression algorithms since their pixel values are proportional to the phase distance reflecting the distance between complex amplitudes. The binary images are compressed by lossless bi-level image coding. The compressed grayscale images and binary images can be synthesized to recover the phase-only holograms and reconstruct the 3D scenes. The advantages of the proposed method over existing image coding standards are verified by numerical simulations.

Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed “JPEG + deep learning” hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

Low Complexity Compression and Speed Enhancement for Optical Scanning Holography

In this paper we report a low complexity compression method that is suitable for compact optical scanning holography (OSH) systems with different optical settings. Our proposed method can be divided into 2 major parts. First, an automatic decision maker is applied to select the rows of holographic pixels to be scanned. This process enhances the speed of acquiring a hologram, and also lowers the data rate. Second, each row of down-sampled pixels is converted into a one-bit representation with delta modulation (DM). Existing DM-based hologram compression techniques suffers from the disadvantage that a core parameter, commonly known as the step size, has to be determined in advance. However, the correct value of the step size for compressing each row of hologram is dependent on the dynamic range of the pixels, which could deviate significantly with the object scene, as well as OSH systems with different opical settings. We have overcome this problem by incorporating a dynamic step-size adjustment scheme. The proposed method is applied in the compression of holograms that are acquired with 2 different OSH systems, demonstrating a compression ratio of over two orders of magnitude, while preserving favorable fidelity on the reconstructed images.