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A fuzzy image metric with application to fractal coding.
Summary
A new fuzzy image metric (FIM) offers superior objective image quality assessment compared to peak signal-to-noise ratio (PSNR). FIM closely matches human perception, improving applications like fractal coding.
Area of Science:
- Image Processing
- Computer Vision
- Artificial Intelligence
Background:
- Objective image quality assessment is crucial for image processing applications like compression and reconstruction.
- Peak Signal-to-Noise Ratio (PSNR) is a common metric but often fails to align with human visual perception.
- Existing metrics lack sufficient correlation with subjective evaluations.
Purpose of the Study:
- To introduce a novel objective image quality metric, the Fuzzy Image Metric (FIM).
- To evaluate FIM's performance against traditional metrics like PSNR.
- To demonstrate FIM's effectiveness in improving image processing tasks, specifically fractal coding.
Main Methods:
- Development of the Fuzzy Image Metric (FIM) based on Sugeno's fuzzy integral.
- Comparison of FIM's correlation with subjective Mean Opinion Scores (MOS) against PSNR.
- Application and evaluation of FIM in fractal image coding.
Main Results:
- FIM achieved a high correlation coefficient of approximately 0.94 with subjective MOS.
- PSNR achieved a lower correlation coefficient of 0.82 with subjective MOS.
- Fractal coding demonstrated improved performance when utilizing the proposed FIM compared to the L(2)-metric.
Conclusions:
- The Fuzzy Image Metric (FIM) provides a more accurate objective evaluation of image quality than PSNR.
- FIM aligns better with human visual perception, making it a valuable tool for image processing.
- FIM enhances the performance of image processing techniques such as fractal coding.
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