Image coding through predictive vector quantization

Ajai Narayan; Tenkasi V. Ramabadran

doi:10.1117/12.139093

12 January 1993 Image coding through predictive vector quantization

Ajai Narayan, Tenkasi V. Ramabadran

Proceedings Volume 1771, Applications of Digital Image Processing XV; (1993) https://doi.org/10.1117/12.139093
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

This paper describes a Predictive Vector Quantizer (PVQ) for coding grayscale images. The method described can be regarded as an extention of an existing speech coding algorithm in 1- dimension to 2-dimensional images. The method applies vector quantization (VQ) to innovations generated by the well known scalar Differential Pulse Code Modulation (DPCM) method. It tries to exploit the advantages of both the simplicity of DPCM and the high compressibility of VQ. Two types of code books, viz., random and deterministic, are used in the implementation. Performance results of the method with both types of codebooks are presented for industrial radiographic images. The results are also compared with reconstructions obtained using the Discrete Cosine Transform (DCT) method.

Citation Download Citation

Ajai Narayan and Tenkasi V. Ramabadran "Image coding through predictive vector quantization", Proc. SPIE 1771, Applications of Digital Image Processing XV, (12 January 1993); https://doi.org/10.1117/12.139093

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