A Clustering-Based Approach to Image Compression Using the K-Means Algorithm
Keywords:
K-Means, Clustering, Image compressionAbstract
In the current Internet era, image information is extensively utilized across various sectors of society. Due to the substantial data volume associated with digital images, compression techniques are essential for efficient transmission and storage. The K-Means algorithm, one of the most widely adopted clustering methods in unsupervised learning, offers a viable approach to this challenge. This study applies the K-Means clustering algorithm to image compression, with implementation carried out via Python programming. Experimental results demonstrate that the K-Means clustering method effectively reduces image data volume while preserving essential visual information, thereby confirming its utility as a practical technique for image compression.
References
Huang, Xingyue, et al. "Distilling Tool Knowledge into Language Models via Back-Translated Traces." arXiv preprint arXiv:2506.19171 (2025).
Abughaida, A., Daman, L., Song, M., Zhang, Y., & Ayoub, J. (2024). An Intelligent Blind Spot Indicator System to Prevent Double Lane Merge Conflicts (No. TRBAM-24-02945).
Deng, X. (2025). Homomorphic Encryption-Based Data Integrity Verification and Anti-Tampering Mechanism in Cloud Storage Environment.
Jin, Y., Zhang, Y., Xu, Z., Zhang, W., & Xu, J. (2024, November). Advanced object detection and pose estimation with hybrid task cascade and high-resolution networks. In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1293-1297). IEEE.
Mehta, R., Patwar, N., Wei, X., Saunders, E., Zhu, X., & Liu, J. (2026). Towards a National AI Security Framework for Financial Infrastructure Protection. International Journal of Advance in Applied Science Research, 5(2), 39–50. Retrieved from https://h-tsp.com/index.php/ijaasr/article/view/251
Zhou, Z. (2026). Bottleneck Diagnosis in International Automotive Sales Funnels Using Gradient Boosting Trees: Evidence from Cross-Regional Team Efficiency Evaluation. Journal of Computer Technology and Applied Mathematics, 3(1), 11-18.
Wensi, L. (2026). AI-Assisted Marketing Content Generation for Non-Standard Industrial Automation Solutions. Journal of Economic Theory and Business Management, 3(1), 18-25.
Yi, X. (2025, October). Real-Time Fair-Exposure Ad Allocation for SMBs and Underserved Creators via Contextual Bandits-with-Knapsacks. In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 1602-1607).
Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing legal citation text classification A Conv1D-based approach for multi-class classification. Journal of Theory and Practice of Engineering Science, 4(02), 15-22.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Lin, Z., Wang, Y., & Hong, Y. (2023). The computing of the Poisson multinomial distribution and applications in ecological inference and machine learning. Computational Statistics, 38(4), 1851-1877.
Zhang, T. (2025). From Black Box to Actionable Insights: An Adaptive Explainable AI Framework for Proactive Tax Risk Mitigation in Small and Medium Enterprises.
Zhang, X. (2024). Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering. Innovation & Technology Advances, 2(2), 59–75. https://doi.org/10.61187/ita.v2i2.135
Guo, Y. (2025, May). IMUs Based Real-Time Data Completion for Motion Recognition With LSTM. In Forum on Research and Innovation Management (Vol. 3, No. 6).
Li, F., & Li, H. (2024). Spatial compartmentalisation effects for multifunctionality catalysis: From dual sites to cascade reactions. Innovation & Technology Advances, 2(1), 1–13. https://doi.org/10.61187/ita.v2i1.54
Junxi, Y., Wang, Z., & Chen, C. (2024). GCN-MF: A graph convolutional network based on matrix factorization for recommendation. Innovation & Technology Advances, 2(1), 14–26. https://doi.org/10.61187/ita.v2i1.30
Zhou, J., & Cen, W. (2024). Investigating the Effect of ChatGPT-like New Generation AI Technology on User Entrepreneurial Activities. Innovation & Technology Advances, 2(2), 1–20. https://doi.org/10.61187/ita.v2i2.124
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Hongxia Mao
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
