Academic Journal of Computing & Information Science, 2023, 6(10); doi: 10.25236/AJCIS.2023.061001.
Shengzhong Zhang, Lei Yu, Yinqian Cheng
Information Network and Data Center, China University of Geosciences (Beijing), Beijing, China
Vector Quantization (VQ) is an effective lossy compression technology developed in the late 1970s. Its theoretical basis is Shannon's rate distortion theory. The basic principle of vector quantization is to use the index of the codeword in the codebook that best matches the input vector for transmission and storage, while decoding only requires a simple table lookup operation. Its outstanding advantages are high compression ratio, simple decoding, and the ability to preserve signal details well. In this article, several VQ approaches are introduced for lossy compression.
Lossy Compression, Vector Quantization (VQ), Codebook, Self-Organizing Feature Mapping (SOFM)
Shengzhong Zhang, Lei Yu, Yinqian Cheng. Lossy Compression Approaches Based on Vector Quantization. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 10: 1-6. https://doi.org/10.25236/AJCIS.2023.061001.
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