Abstract
We apply two novel nonuniform quantization techniques to digital holograms of three-dimensional real-world objects. Our companding approach, combines the efficiency of uniform quantization with the improved performance of nonuniform quantization. We show that the performance of companding techniques can be comparable with k-means clustering and a competitive neural network, while only requiring a single-pass processing step. The quantized holographic pixels are coded using lossless techniques for the calculation of compression ratio.
Highlights
With the development of megapixel CCD sensors with high spatial resolution and dynamic range, digital holography [1, 2, 3, 4, 5, 6, 7] has become a popular technique for threedimensional (3D) imaging [8]
A technique known as phase-shift interferometry [2, 5] was used to record our in-line digital holograms
Experimentation has shown that this technique provides comparable hologram reconstruction quality for quantization compression equal to that of k-means and Kohonen competitive, combined with the advantage that the quantization can be performed during a single pass of the data
Summary
With the development of megapixel CCD sensors with high spatial resolution and dynamic range, digital holography [1, 2, 3, 4, 5, 6, 7] has become a popular technique for threedimensional (3D) imaging [8]. The noisy appearance of digital holograms causes lossless data compression techniques, such as Huffman, to perform poorly [13]. The use of lossy compression techniques to initially quantize our hologram data seems essential. Quantization and phase quantization have been applied successfully to Fourier and holographic data in the past [14, 13, 15, 16, 12, 17, 18, 19, 20, 21]. Nonuniform quantization achieves better compression ratios, but at the cost of requiring an iterative clustering technique. We employ two companding approaches to quantize our holographic data and further apply lossless compression to the quantized data
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