Wavelet compression of medical image using tree-structured vector quantization and high-order entropy coding

Jun Seok Song; Seung Jun Lee; HyoJoon Kim; JongHyo Kim; ChoongWoong Lee

doi:10.1117/12.273954

7 May 1997 Wavelet compression of medical image using tree-structured vector quantization and high-order entropy coding

Jun Seok Song, Seung Jun Lee, HyoJoon Kim, JongHyo Kim, ChoongWoong Lee

Proceedings Volume 3031, Medical Imaging 1997: Image Display; (1997) https://doi.org/10.1117/12.273954
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

A tree-structured vector quantization system employing conditional arithmetic coding is introduced to encode wavelet coefficients of medical image. The proposed scheme efficiently reduces bit rate by exploiting inter- and intra- band correlation and effectively approximates the embedded scheme by utilizing sequential bit allocation results of the nested quantizers. The proposed scheme provides good bitrate-PSNR performance and subjective reconstruction quality with lower encoding complexity than the wavelet full-search vector quantization systems.

Citation Download Citation

Jun Seok Song, Seung Jun Lee, HyoJoon Kim, JongHyo Kim, and ChoongWoong Lee "Wavelet compression of medical image using tree-structured vector quantization and high-order entropy coding", Proc. SPIE 3031, Medical Imaging 1997: Image Display, (7 May 1997); https://doi.org/10.1117/12.273954

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