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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Spatial bandwidth-optimized compression of image plane off-axis holograms with image and video codecs.
Optics Express
|September 29, 2020
Summary
Image plane off-axis holograms (IP-OAH) compression methods were developed to reduce data size without compromising accuracy. Both lossy and lossless techniques outperform standard codecs, preserving holographic phase measurement capabilities.
Area of Science:
- Optics and Photonics
- Digital Holography
- Metrology
Background:
- Digital holographic microscopy and tomography commonly capture image plane off-axis holograms (IP-OAH).
- Increasing data storage and transmission demands necessitate efficient hologram compression.
- Compression must not degrade the metrological accuracy of holographic techniques.
Purpose of the Study:
- To develop optimized lossy and lossless compression approaches for IP-OAH.
- To evaluate the performance of these approaches against conventional codecs.
- To ensure the preservation of holographic phase measurement accuracy with lossy compression.
Main Methods:
- Implementation of conventional compression codecs optimized for IP-OAH signal bandwidth.
- Development of both lossy and lossless compression strategies.
- Comparative analysis of proposed methods against standard compression algorithms.
Main Results:
- Both optimized lossy and lossless IP-OAH compression approaches demonstrated superior performance compared to conventional codecs.
- The developed lossy compression method successfully maintained the accuracy of holographic phase measurements.
- Bandwidth optimization of compression codecs proved effective for holographic data.
Conclusions:
- Optimized compression techniques offer a viable solution for managing large IP-OAH datasets.
- Lossy compression of IP-OAH is feasible without sacrificing critical metrological information.
- The presented methods enhance the practicality of digital holographic microscopy and tomography by addressing data handling challenges.
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