Compression of digital holographic data: an overview

Dufaux, Frédéric; Xing, Yafei; Pesquet‐Popescu, Béatrice; Schelkens, Peter

doi:10.1117/12.2190997

Cited by 30 publications

(27 citation statements)

References 36 publications

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“…In the literature there are several researches dedicated to the compression of the holographic data [36], [35]. Different types of lossless algorithms have been tested in [37].…”

Section: Digital Holography Data Compressionmentioning

confidence: 99%

Digital holography data compression

Corda¹

2019

Telfor J

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Digital holography processing is a research topic related to the development of novel visual immersive applications. The huge amount of information conveyed by a digital hologram and the different properties of holographic data with respect to conventional photographic data require a comprehension of the performances and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of the state-of-the-art compression techniques applied to digital holographic data.

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“…In the literature there are several researches dedicated to the compression of the holographic data [36], [35]. Different types of lossless algorithms have been tested in [37].…”

Section: Digital Holography Data Compressionmentioning

confidence: 99%

Digital holography data compression

Corda¹

2019

Telfor J

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“…For example, a photograph is usually locally smooth while a hologram consists of many high-frequency fringe patterns. As a consequence, JPEG compression often cannot give optimal compression performance for holograms [30]. Instead of directly adopting JPEG standard, many customized compression algorithms are proposed for holograms such as Fresnelets [31], Wavelet-Bandelets Transform [32], vector quantization [33] and enhanced wavelet transform [34].…”

Section: Figure 1 An Example Of Complex Hologram (Left) and Phase-onlmentioning

confidence: 99%

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

et al. 2018

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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.

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“…Traditional transforms such as Daubechies wavelets, Fresnelets, directional wavelet filters and various packet and multiscale decompositions are interesting options and have been subject to many studies. A codec for static holograms delivering state of the art performance [2] has been proposed as a generic architecture suitable for the compression of many types of holograms. This framework has a JPEG 2000 codec at its core, and is extended with fully arbitrary wavelet decomposition styles and directional wavelet transforms.…”

Section: Source Codingmentioning

confidence: 99%