K. Sano, H. Takizawa, T. Nakajima, H. Kobayashi, and T. Nakamura (Japan)
volume compression, vector quantization, law-of-the-jungle algorithm
Volume compression without severe loss of information is required for large-scale volumetric visualization. Vector quantization(VQ) is one of the attractive compression techniques that can overcome scalar quantization(SQ). Although several compression algorithms using VQ have been proposed, their codebook generation algorithms still have some problems. Furthermore, the algorithms do not perform entropy encoding on quantized data that is quite effective for practical compression. Therefore, they do not defeat the conventional compression algorithm based on SQ in terms of processing time or compression efficiency. In this paper, we mention a practical volume compression algorithm that outperforms the conventional SQ-based compression algorithms by introducing LOJ-based codebook design and entropy encoding. We compare compression performance of the proposed algorithm with that of the SQ-based algorithm. Experi mental results show that the proposed algorithm is superior to n SQ-based algorithm, especially in the case of compression keeping the higher quality of rendered im ages.
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