A Codebook Compression Method for Vector Quantization Algorithm
DOI:
https://doi.org/10.46604/peti.2024.13268Keywords:
codebook, peak signal-to-noise ratio, structural similarity index, quantization, VQAbstract
This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook.
References
A. Hasnat, D. Barman, S. Halder, and D. Bhattacharjee, “Modified Vector Quantization Algorithm to Overcome the Blocking Artefact Problem of Vector Quantization Algorithm,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 5, pp. 3711-3727, 2017.
R. Li, Z. Pan, and Y. Wang, “A General Codebook Design Method for Vector Quantization,” Multimedia Tools and Applications, vol. 77, no. 18, pp.23803-23823, September 2018.
L. Wang, Z. M. Lu, L. H. Ma, and Y. P. Feng, “VQ Codebook Design Using Modified K-Means Algorithm with Feature Classification and Grouping Based Initialization,” Multimedia Tools and Applications, vol. 77, no. 7, pp. 8495-8510, April 2018.
S. Yang and Y. Mao, “Vector Quantization of Deep Convolutional Neural Networks with Learned Codebook,” 17th Canadian Workshop on Information Theory, pp. 39-44, June 2022.
M. H. Vali and T. Bäckström, “NSVQ: Noise Substitution in Vector Quantization for Machine Learning,” IEEE Access, vol. 10, pp. 13598-13610, January 2022.
H. A. S. Leitao, W. T. A. Lopes, and F. Madeiro, “PSO Algorithm Applied to Codebook Design for Channel-Optimized Vector Quantization,” IEEE Latin America Transactions, vol. 13, no. 4, pp.961-967, April 2015.
A. Hasnatand D. Barman, “A Proposed Multi-Image Compression Technique,” Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp.3177-3193, 2019.
D. Barman, A. Hasnat, and B. Barman, “A Quantization Based Codebook Formation Method of Vector Quantization Algorithm to Improve the Compression Ratio While Preserving the Visual Quality of the Decompressed Image,” Multidimensional Systems and Signal Processing, vol. 34, no. 1, pp. 127-145, March 2023.
P. K. Shah, R.P. Pandey, and R. Kumar, “Vector Quantization with Codebook and Index Compression,” International Conference System Modeling & Advancement in Research Trends, pp. 49-52, November 2016.
J. Rahebi, “Vector Quantization Using Whale Optimization Algorithm for Digital Image Compression,” Multimedia Tools and Applications, vol. 81, no. 14, pp. 20077-20103, June 2022.
D. Barman, A. Hasnat, and B. Barman, “A Codebook Modification Method of Vector Quantization to Enhance Compression Ratio,” High Performance Computing and Networking, vol. 853, pp. 227-234, 2022.
D. Barman, A. Hasnat, and B. Barman, “An Enhanced Technique to Improve the Performance of Multi-Image Compression Technique,” Advanced Computing and Intelligent Technologies, vol. 914, pp. 307-315, 2022.
P. Chavan, B. Sheela Rani, M. Murugan, P. Chavan, and M. Kulkarni, “An Analysis of Codebook Optimization for Image Compression: Modified Genetic Algorithm and Particle Swarm Optimization Algorithm,” Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems, vol. 977, pp. 849-866, 2023.
A. Hasnat, D. Barman, and B. Barman, “Luminance Approximated Vector Quantization Algorithm to Retain Better Image Quality of the Decompressed Image,” Multimedia Tools and Applications, vol. 80, no. 8, pp.11985-12007, March 2021.
U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Computer and Communications, vol. 7, no. 3, pp. 8-18, March 2019.
J. K. Mandal, Reversible Steganography and Authentication via Transform Encoding, Studies in Computational Intelligence, vol. 901, Singapore: Springer, 2020.
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Copyright (c) 2024 Abul Hasnat, Dibyendu Barman, Md Azizul Hoque, Santanu Halder, Debotosh Bhattacharjee
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