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Optimal autoregressive model based medical image compression using genetic algorithm.

M Sasikala1, N Kumaravel

  • 1Center for Medical Electronics, School of ECE, Anna University, Chennai, India.

Biomedical Sciences Instrumentation
|June 2, 2000
PubMed
Summary
This summary is machine-generated.

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This study introduces a new predictive image compression method using 2D multiplicative autoregressive models and a genetic algorithm for parameter optimization. The approach effectively reduces data for storage and communication savings.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Image compression is crucial for reducing storage and communication costs by removing data redundancy.
  • Predictive coding is an effective image compression technique that leverages data correlation.

Purpose of the Study:

  • To propose a novel predictive coding method for image compression.
  • To utilize two-dimensional multiplicative autoregressive models for enhanced compression.
  • To employ a genetic algorithm for optimizing autoregressive parameters.

Main Methods:

  • Implementation of a new predictive coding scheme for image compression.
  • Application of two-dimensional multiplicative autoregressive models.
  • Utilization of a genetic algorithm to compute autoregressive parameters.

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Main Results:

  • The proposed method demonstrates efficient image compression capabilities.
  • The genetic algorithm effectively determined the autoregressive parameters.
  • Performance was evaluated against existing multiplicative autoregressive model-based methods.

Conclusions:

  • The novel predictive coding method offers a viable approach for image compression.
  • The integration of genetic algorithms enhances the optimization of autoregressive models for compression.
  • This technique contributes to more efficient storage and transmission of image data.