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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

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Published on: November 17, 2016

No-reference image quality assessment through the von Mises distribution.

Salvador Gabarda¹, Gabriel Cristóbal

¹Instituto de Óptica (CSIC), Madrid, Spain.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|December 4, 2012

Summary

This summary is machine-generated.

This study introduces a new method using the von Mises distribution to assess image quality, analyzing its concentration and fitness parameters for distortions like blur and noise.

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Area of Science:

Digital Image Processing
Statistical Modeling
Image Quality Assessment

Background:

Image quality assessment is crucial for digital imaging.
Traditional methods often require reference images.
Developing no-reference quality metrics for various distortions is an active research area.

Purpose of the Study:

To introduce a novel method for calculating the von Mises distribution of image entropy.
To evaluate the suitability of the von Mises distribution's concentration and fitness parameters as image quality assessment measures.
To analyze performance under specific distortions like Gaussian blur and additive Gaussian noise.

Main Methods:

Calculating local Rényi entropy in four orientations.
Determining von Mises distribution parameters from local entropy.
Analyzing concentration and fitness parameters for image quality evaluation.

Main Results:

Highest concentration parameter values correlate with best-in-focus, noise-free contextual images.
High approximation to the von Mises model indicates superior image quality.
The defined von Mises fitness parameter shows promise as a no-reference quality indicator for non-contextual images.

Conclusions:

The von Mises distribution offers a viable framework for image quality assessment.
Concentration and fitness parameters can serve as effective no-reference image quality metrics.
The proposed method demonstrates potential for evaluating image quality under specific distortions.