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Related Experiment Video

Updated: Jun 16, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Cross-spectrum error criterion as an image quality measure.

A G Tescher, J R Parsons

    Applied Optics
    |February 4, 2010
    PubMed
    Summary

    This study introduces a cross-spectrum error criterion to measure distortion in undersampled images. This statistical method effectively analyzes aliasing effects, proving useful for image quality assessment.

    Area of Science:

    • Image processing
    • Signal analysis
    • Digital imaging

    Background:

    • Image quality assessment traditionally uses subjective evaluations or statistical methods.
    • Undersampling and aliasing introduce distortions in band-limited images.
    • Existing distortion measures may not fully capture the complexities of undersampled image artifacts.

    Purpose of the Study:

    • To introduce and evaluate a cross-spectrum error criterion as a distortion measure for undersampled band-limited images.
    • To demonstrate the impact of undersampling and aliasing in the frequency domain.
    • To compare the effectiveness of the proposed spectral error criterion with subjective evaluations.

    Main Methods:

    • Frequency domain manipulation to simulate undersampling and aliasing effects.

    Related Experiment Videos

    Last Updated: Jun 16, 2026

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

  • Digital simulation of image distortions.
  • Calculation of a cross-spectrum error criterion.
  • Subjective comparison of simulated results with calculated spectral errors.

    • Main Results:

    • The cross-spectrum error criterion effectively quantifies distortion in undersampled images.
    • Aliasing artifacts are clearly demonstrated through frequency domain analysis.
    • The proposed spectral error measure shows strong correlation with subjective assessments of image quality.
    • The analysis provides insights into spatial filtering and mean square error relevance.

    Conclusions:

    • The cross-spectrum error criterion is a valuable tool for analyzing distortion in undersampled band-limited images.
    • This statistical approach offers an objective measure complementing human visual evaluation.
    • The findings have implications for improving image reconstruction and quality assessment techniques.