Objective assessment of image quality

effects of quantum noise and object variability.

Research output: Contribution to journalArticle

278 Citations (Scopus)

Abstract

A number of task-specific approaches to the assessment of image quality are treated. Both estimation and classification tasks are considered, but only linear estimators or classifiers are permitted. Performance on these tasks is limited by both quantum noise and object variability, and the effects of postprocessing or image-reconstruction algorithms are explicitly included. The results are expressed as signal-to-noise ratios (SNR's). The interrelationships among these SNR's are considered, and an SNR for a classification task is expressed as the SNR for a related estimation task times four factors. These factors show the effects of signal size and contrast, conspicuity of the signal, bias in the estimation task, and noise correlation. Ways of choosing and calculating appropriate SNR's for system evaluation and optimization are also discussed.

Original languageEnglish (US)
Pages (from-to)1266-1278
Number of pages13
JournalJournal of the Optical Society of America. A, Optics and image science
Volume7
Issue number7
StatePublished - Jul 1990

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Quantum noise
Signal-To-Noise Ratio
Image quality
Noise
Signal to noise ratio
Computer-Assisted Image Processing
Task Performance and Analysis
Image reconstruction
Classifiers

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

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title = "Objective assessment of image quality: effects of quantum noise and object variability.",
abstract = "A number of task-specific approaches to the assessment of image quality are treated. Both estimation and classification tasks are considered, but only linear estimators or classifiers are permitted. Performance on these tasks is limited by both quantum noise and object variability, and the effects of postprocessing or image-reconstruction algorithms are explicitly included. The results are expressed as signal-to-noise ratios (SNR's). The interrelationships among these SNR's are considered, and an SNR for a classification task is expressed as the SNR for a related estimation task times four factors. These factors show the effects of signal size and contrast, conspicuity of the signal, bias in the estimation task, and noise correlation. Ways of choosing and calculating appropriate SNR's for system evaluation and optimization are also discussed.",
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