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Fast Linde-Buzo-Gray (FLBG) Algorithm for Image Compression through Rescaling Using Bilinear Interpolation.

Muhammmad Bilal1, Zahid Ullah2, Omer Mujahid3

  • 1Department of Information Engineering Technology, University of Technology, Nowshera 24170, Pakistan.

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Summary

This study introduces an enhanced Linde-Buzo-Gray (LBG) algorithm for vector quantization (VQ) image compression. The improved method significantly reduces computational complexity and memory size while maintaining image quality.

Keywords:
Linde–Buzo–Graybat algorithmcodebookcomputational timefirefly algorithmimage compressionpeak signal to noise ratiovector quantization

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Area of Science:

  • Digital image processing
  • Data compression algorithms
  • Information theory

Background:

  • Vector quantization (VQ) offers high compression ratios and simple implementation.
  • Linde-Buzo-Gray (LBG) is a standard VQ codebook design technique.
  • Existing LBG-based optimization algorithms (PSO, CS, FA) suffer from high computational time due to exhaustive searching.

Purpose of the Study:

  • To enhance the Linde-Buzo-Gray (LBG) algorithm for vector quantization (VQ).
  • To minimize computational complexity and memory requirements in image compression.
  • To maintain high image quality (PSNR, SSIM) during compression.

Main Methods:

  • Developed a novel algorithm that enhances LBG by reducing comparisons between codebook and training vectors.
  • Utilized a match function and rescaling via bilinear interpolation with the nearest neighborhood method.
  • Implemented image downsizing at the encoder and upscaling at the decoder.

Main Results:

  • Achieved a 50.2% reduction in computational complexity compared to standard LBG.
  • Reduced computational complexity by over 97% compared to other LBG-based optimization algorithms.
  • Obtained a 20% reduction in memory size with no significant loss in image quality.

Conclusions:

  • The proposed enhanced LBG algorithm offers significant improvements in computational efficiency and memory usage for VQ image compression.
  • The method effectively balances compression performance with image fidelity.
  • This approach presents a practical advancement for efficient image compression techniques.