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.
 Zhengxing Cheng, Wavelet Analysis Algorithms and Applications, Xi'an Jiaotong University Press, 1998. 8
 Wenji Xu, Research on Fast Codeword Search Algorithms for Vector Quantization, Master's Thesis at Suzhou University, 2008. 5
 Qiang Li, Zhengzhi Wang. Research on High Fidelity Compression Methods for Remote Sensing Images Based on Wavelet Theory, Chinese Journal of Image and Graphics, 1999, 3 (1): 31-36
 Wen Jiang, Zhongxin Le. Video Coding Algorithm Based on Wavelet Transform, Chinese Journal of Image and Graphics, 1997, 2 (10), 721-725
 An Li et al. A VQ image encoding method based on wavelet transform combined with reverse selection correction, Chinese Journal of Image and Graphics, 1997, 2 (7): 488-490
 Mareboyanna Manohar et al. Model-Based Vector Quantization with Application to Remotely Sensed Image Data, IEEE Trans. on Image Processing, 1999, 8(1): 15-21
 Zhang Jihong, Wang Hui, et al. Image Coding Research Based on Fuzzy Vector Quantization, Chinese Journal of Image and Graphics, 1998, 3 (4): 295-298
 Seong Joon Back et al. A Fast Encoding Algorithm for Vector Quantization, IEEE Signal Processing Letters, 1997, 4(20): 325-327
 A. J. Hussain et al. , Image compression techniques: A survey in lossless and lossy algorithms, Neurocomputing, 2018, 300: 44–69