Abstract
Optical computing has recently become a very active research field. The advantage of optics is its capability of providing highly parallel operations in a three dimensional space. Image compression suffers from large computational requirements. We propose optical architectures to execute various image compression techniques, utilizing the inherent massive parallelism of optics.
We optically implement the following compression and corresponding decompression techniques:
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• transform coding
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• vector quantization
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• interframe coding for video
We show many generally used transform coding methods, for example, the cosine transform, can be implemented by a simple optical system. The transform coding can be carried out in constant time.
Most of this paper is concerned with an innovative optical system for vector quantization using holographic associative matching. Limitations of conventional vector quantization schemes are caused by a large number of sequential searches through a large vector space. Holographic associative matching provided by multiple exposure holograms can offer advantageous techniques for vector quantization based compression schemes. Photorefractive crystals, which provide high density recording in real time, are used as our holographic media. The reconstruction alphabet can be dynamically constructed through training or stored in the photorefractive crystal in advance. Encoding a new vector can be carried out by holographic associative matching in constant time.
An extension to interframe coding is also discussed.
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Reif, J.H., Yoshida, A. (1992). Optical Techniques for Image Compression*. In: Storer, J.A. (eds) Image and Text Compression. The Kluwer International Series in Engineering and Computer Science, vol 176. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3596-6_3
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DOI: https://doi.org/10.1007/978-1-4615-3596-6_3
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