Abstract
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.
Highlights
In the past, it has been demonstrated holographic data can be compressed through popular techniques such as JPEG2000 and JPEG9–11 and Vector Quantization (VQ)[12,13,14,15]
The dynamic range of the hologram pixels is dependent on the object scene, as well as the optical settings of the optical scanning holography (OSH) system
Different OSH systems can have different optical settings that could affect the dynamic range of the hologram, and the compression process that follows
Summary
In this paper we report a low complexity compression method that is suitable for compact optical scanning holography (OSH) systems with different optical settings. Compression and reducing the number of scan rows are often required to enable efficient transmission of the hologram pixels with a throughput that is compatible with practical constraint In the past, it has been demonstrated holographic data can be compressed through popular techniques such as JPEG2000 and JPEG9–11 and Vector Quantization (VQ)[12,13,14,15]. Even if a step size can be estimated through this learning process, it may not be applicable to compressing the entire hologram as each row of the hologram can be very different from the others Speaking, this technique is not suitable for capturing hologram of arbitrary object scene without prior calibration of the OSH system. We shall outline the OSH system and present our proposed method
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