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A distortion measure for blocking artifacts in images based on human visual sensitivity.

S A Karunasekera¹, N G Kingsbury

¹Dept. of Eng., Cambridge Univ.

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

|January 1, 1995

Summary

This study introduces a visual model to measure blocking artifacts in images. The model accurately predicts the visibility of blocking errors, correlating well with human perception.

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Area of Science:

Computer Vision
Image Processing
Human Visual Perception

Background:

Blocking artifacts are common distortions in digital images.
Existing methods may not accurately reflect human perception of these artifacts.

Purpose of the Study:

To develop a visual model for quantifying blocking artifact visibility.
To align image distortion measures with human visual sensitivity.

Main Methods:

Deriving a model based on human visual sensitivity to edge artifacts.
Conducting psychovisual experiments to measure sensitivity variations.
Estimating model parameters using experimental data.
Testing the model on real-world images.

Main Results:

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The model quantifies blocking error visibility numerically.
Psychovisual experiments revealed sensitivity variations based on luminance, activity, edge length, and amplitude.
The model's predictions showed strong correlation with subjective rankings of error visibility.

Conclusions:

The developed visual model effectively measures blocking artifact visibility.
The model's accuracy is validated by its correlation with human perception.
This model can improve image quality assessment by incorporating visual perception.