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Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

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Published on: January 5, 2024

Gradient match and side match fractal vector quantizers for images.

¹Department of Electrical Engineering, National Yunlin University of Science and Technology, Touliu Yunlin 640, Taiwan, R.O.C. htchang@pine.yuntech.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 5, 2008

Summary

This study introduces gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs) for image coding. These methods significantly reduce bit rates in fractal block coding by minimizing redundancy.

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Area of Science:

Digital Image Processing
Image Compression
Fractal Coding

Background:

Conventional fractal block coding (FBC) techniques face challenges in decoding speed and coding performance.
Previous work introduced noniterative FBC to enhance efficiency.
Reducing bit rates for fractal codes remains a key objective in image compression.

Purpose of the Study:

To propose novel finite state fractal vector quantizers (FSFVQs): gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs).
To integrate gradient match vector quantizers (GMVQs) and side match vector quantizers (SMVQs) into the noniterative FBC framework.
To improve image coding performance and compression ratios by reducing data redundancy.

Main Methods:

Developed GMFVQs and SMFVQs utilizing super codebooks generated from affine-transformed domain blocks.
Employed side-match and gradient-match criteria for dynamic codeword extraction from super codebooks into state codebooks.
Applied these techniques within the noniterative fractal block coding framework.

Main Results:

Demonstrated significant reduction in redundancy within affine-transformed domain blocks.
Achieved a substantial increase in the image compression ratio.
Simulation results indicate a 15%-20% saving in bit rates compared to the noniterative FBC technique using GMFVQs.

Conclusions:

GMFVQs and SMFVQs effectively enhance the noniterative FBC technique for image coding.
The proposed methods offer improved compression efficiency and reduced bit rates.
These advancements contribute to more efficient digital image compression strategies.