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Mean Absolute Deviation

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Related Experiment Video

Updated: Jun 14, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Divisive normalization image quality metric revisited.

Valero Laparra¹, Jordi Muñoz-Marí, Jesús Malo

¹Image Processing Laboratory, Universitat de València, Catedrático A. Escardino, 46980 Paterna, València, Spain.

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

|April 3, 2010

Summary

This summary is machine-generated.

Traditional image quality metrics based on error visibility are not inferior to newer methods. Divisive normalization models offer a competitive, robust, and interpretable approach to image quality assessment.

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Area of Science:

Computer Vision
Image Processing
Perceptual Science

Background:

Traditional image quality metrics face criticisms regarding near-threshold performance, limited geometric meaning, and statistical justification.
Structural and information-theory-based metrics are often considered superior alternatives to error visibility approaches.
The perceived limitations of traditional metrics have driven research towards novel image quality assessment methods.

Purpose of the Study:

To re-evaluate the traditional error visibility approach in image quality assessment.
To demonstrate that criticisms against error visibility metrics do not apply to models using general divisive normalization.
To establish the continued relevance and competitiveness of divisive normalization masking models.

Main Methods:

Analytical evaluation of error visibility metrics with general divisive normalization.
Experimental validation using diverse image databases and a wide range of distortions.
Comparison of divisive normalization performance against structural and information-theory-based metrics.

Main Results:

Criticisms against traditional error visibility metrics are unfounded when using general divisive normalization.
Divisive normalization metrics demonstrate competitive performance compared to newer approaches.
Divisive normalization models are robust and offer linear interpretability.

Conclusions:

Traditional error visibility metrics, specifically those employing divisive normalization, remain viable and competitive.
Divisive normalization masking models should be reconsidered and included in discussions on image quality assessment.
The study challenges the notion of inherent inferiority of traditional error visibility approaches.