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Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression

Author

Listed:
  • Aditya Bakshi

    (Department of Computer Science & Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India)

  • Akhil Gupta

    (School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Gulshan Sharma

    (Department of Electrical Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa)

  • Pitshou N. Bokoro

    (Department of Electrical Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa)

  • Fayez Alqahtani

    (Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia)

  • Amr Tolba

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

  • Maria Simona Raboaca

    (Doctoral School, University Politehnica of Bucharest, Splaiul Independentei Street, No. 313, 060042 Bucharest, Romania
    National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Vâlcea, Uzinei Street, No. 4, P.O. Box 7 Râureni, 240050 Râmnicu Vâlcea, Romania)

Abstract

For constructing the best local codebook for image compression, there are many Vector Quantization (VQ) procedures, but the simplest VQ procedure is the Linde–Buzo–Gray (LBG) procedure. Techniques such as the Gaussian Dissemination Function (GDF) are used for the searching process in generating a global codebook for particle swarm optimization (PSO), Honeybee mating optimization (HBMO), and Firefly (FA) procedures. However, when particle velocity is very high, FA encounters a problem when brighter fireflies are trivial, and PSO suffers uncertainty in merging. A novel procedure, Cuckoo Search–Kekre Fast Codebook Generation (CS-KFCG), is proposed that enhances Cuckoo Search–Linde–Buzo–Gray (CS-LBG) codebook by implementing a Flight Dissemination Function (FDF), which produces more speed than other states of the art algorithms with appropriate mutation expectations for the overall codebook. Also, CS-KFGC has generated a high Peak Signal Noise Ratio (PSNR) in terms of high duration (time) and better acceptability rate.

Suggested Citation

  • Aditya Bakshi & Akhil Gupta & Sudeep Tanwar & Gulshan Sharma & Pitshou N. Bokoro & Fayez Alqahtani & Amr Tolba & Maria Simona Raboaca, 2023. "Performance Augmentation of Cuckoo Search Optimization Technique Using Vector Quantization in Image Compression," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2364-:d:1150778
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