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Optimization Based Vector Quantization for Data Reduction in Multimedia Applications

V. R. Kavitha1,*, M. Kanchana2, B. Gobinathan3, K. R. Sekar4, Mohamed Yacin Sikkandar5

1 Department of CSE, Prathyusha Engineering College, Thiruvallur, 602025, Tamil Nadu, India
2 School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India
3 Jaya Sakthi Engineering College, Chennai, 602024, Tamil Nadu, India
4 School of Computing, SASTRA Deemed University, Thanjavur, 613401, Tamil Nadu, India
5 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia

* Corresponding Author: V. R. Kavitha. Email: email

Intelligent Automation & Soft Computing 2022, 31(2), 853-867.


Data reduction and image compression techniques in the present Internet and multi-media age are essential to increase image and video capacity in relation to memory, network bandwidth use and safe data transmission. There have been a different variety of image compression models with varying compression efficiency and visual image quality in the literature. Vector Quantization (VQ) is a widely used image coding scheme that is designed to generate an efficient coding book that includes a list of codewords that assign the input image vector to a minimum distance of Euclidea. The Linde–Buzo–Gray (LBG) historically widely used model produces the local optimal codebook. The LBG model’s codebook architecture is seen as an optimization challenge that can be resolved using metaheuristic algorithms. In this perspective, this paper introduces a new DPIO algorithm for codebook generation in VQ. The model presented uses LBG to initialise the DPIO algorithm to construct the VQ technique and is called the DPIO-LBG process. The performance of the DPIO-LBG model is validated with benchmark data and the effects of different performance aspects are investigated. The simulation values showed the DPIO-LBG model to be efficient interfaces of compression efficiency and image quality reconstructed. The presented model produces an efficient codebook with minimum computational time and a better signal to noise ratio (PSNR).


Cite This Article

V. R. Kavitha, M. Kanchana, B. Gobinathan, K. R. Sekar and M. Yacin Sikkandar, "Optimization based vector quantization for data reduction in multimedia applications," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 853–867, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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