Color Image Compression Using Vector Quantization with Fuzzy Logic
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Abstract
Image compression is a critical method for minimizing digital image size for efficient storage and transmission, particularly in bandwidth-constrained applications. A new approach for color image compression by incorporating vector quantization (VQ) and fuzzy logic is introduced in this paper with the aim of further improving performance. Vector quantization is another commonly used lossy compression technique in which an image is divided into small blocks and mapped to a prechosen set of representative vectors called codewords, thereby compressing the image considerably.
In order to solve the problem of maintaining image quality while compressing it, we apply fuzzy logic to increase the accuracy of the codeword selection mechanism. Using the fuzzy logic rules, we define the selection rules adaptively based on the characteristics of the image in order to achieve maximum balance between compression ratio and image quality. Application of fuzzy logic enables smoother movement from one region of images with similar content to another and eliminates quantization errors characteristic for VQ, especially in regions of high variance of pixel intensity.
The new hybrid method was applied to several color images and was proven to surpass the traditional VQ method in terms of compression ratio, PSNR, and SSIM measures. The hybrid method offers an efficient plan for high-compression-quality-image with little computational cost.
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References
N. B. Karayiannis and P.-I. Pai, “Fuzzy vector quantization algorithms and their application in image compression,” IEEE Transactions on Image Processing, vol. 4, no. 9, pp. 1193–1201, 1995.
A. A. H. Ajam, A. F. Albaghdadi, and A. Zengin, “Medical image compression based on vector quantization and discrete wavelet transform,” in AIP Conference Proceedings, AIP Publishing, 2024.
G. E. Tsekouras, D. Darzentas, I. Drakoulaki, and A. D. Niros, “Fast fuzzy vector quantization,” in International Conference on Fuzzy Systems, IEEE, 2010, pp. 1–8.
S. Singh, M. H. Assaf, S. R. Das, S. N. Biswas, E. M. Petriu, and V. Groza, “Short duration voice data speaker recognition system using novel fuzzy vector quantization algorithm,” in 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, IEEE, 2016, pp. 1–6.
H. Adnan, A. Obied, and A. Al-Fayadh, “Medical Image Compression Using Discrete Wavelet Transform,” in Artificial Intelligence, Big Data, IOT and Block Chain in Healthcare: From Concepts to Applications, Y. Farhaoui, Ed., Cham: Springer Nature Switzerland, 2024, pp. 160–167.
M. F. Radad, A. O. Al-Shimmery, and A. H. Nasir, “A Hybrid Discrete Wavelet Transform with Vector Quantization for Efficient Medical Image Compression,” NeuroQuantology, vol. 20, no. 8, p. 8868, 2022.
G. Garg and R. Kumar, “A Multi-Level Enhanced Color Image Compression Algorithm using SVD & DCT,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 1, pp. 121–132, Nov. 2022, doi: 10.17762/ijritcc.v10i1s.5817.
M. B. Al-Obaidi, G. Al-Khafaji, and M. M. Siddeq, “Enhancing Color Image Compression with Linear Polynomial Coding and Adaptive Inter-Prediction,” in 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS’24), Cihan University-Erbil, 2024, pp. 87–92. doi: 10.24086/cocos2024/paper.1524.
S. F. A. Gani, R. A. Hamzah, R. Latip, S. Salam, F. Noraqillah, and A. I. Herman, “Image compression using singular value decomposition by extracting red, green, and blue channel colors,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 1, pp. 168–175, Feb. 2022, doi: 10.11591/eei.v11i1.2602.
A. Hegde and S. Deepak, “LOSSY COLOR IMAGE COMPRESSION USING NON LINEARIZATION AND COLOR REDUCTION.” [Online]. Available: http://www.esatjournals.org
S. Akram Alrubaie and I. Mohammed Hassoon, “Support Vector Machine (SVM) for Colorization the Grayscale Image,” Al-Qadisiyah Journal for Engineering Sciences, Sep. 2020, doi: 10.30772/qjes.v13i3.658.
S. L. Chen et al., “VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT,” Sensors, vol. 23, no. 3, Feb. 2023, doi: 10.3390/s23031573.
S. Kumar and D. Singh, “Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extraction and Luminance Mapping,” IOSR Journal of Computer Engineering (IOSRJCE), vol. 4, no. 5, pp. 27–32, 2012.
A. Altunkaynak and A. Jalilzadnezamabad, “Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques,” J Hydrol (Amst), vol. 601, p. 126619, 2021.
M. Fu, H. Liu, Y. Yu, J. Chen, and K. Wang, “Dw-gan: A discrete wavelet transform gan for nonhomogeneous dehazing,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 203–212.
M. J. Shensa, “The discrete wavelet transform: wedding the a trous and Mallat algorithms,” IEEE Transactions on signal processing, vol. 40, no. 10, pp. 2464–2482, 1992.
H.-T. Yang, C.-C. Liao, and J.-H. Chou, “Fuzzy learning vector quantization networks for power transformer condition assessment,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 8, no. 1, pp. 143–149, 2001.
R. Gray, “Vector quantization,” IEEE Assp Magazine, vol. 1, no. 2, pp. 4–29, 1984.
G. E. Tsekouras, D. Darzentas, I. Drakoulaki, and A. D. Niros, “Fast fuzzy vector quantization,” in International Conference on Fuzzy Systems, 2010, pp. 1–8. doi: 10.1109/FUZZY.2010.5584446.
A. Moffat, “Huffman coding,” ACM Computing Surveys (CSUR), vol. 52, no. 4, pp. 1–35, 2019.