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