Evaluation of Image Quality Features Via Monotonic Analysis

Abstract

This work introduces novel monotonic analysis to determine whether or not proposed image quality (IQ) measures are consistent with human measured perceptual quality scores. Specifically, the analysis performs a generalized likelihood ratio test over the H1 hypothesis that the IQ measures and the corresponding perceptual measurements are related via a monotonic function versus the null hypothesis that the functional relationship is arbitrary. This paper evaluates six proposed IQ measures against mean opinion scores using the new monotonic analysis. The next generation of night vision goggles and night scopes will fuse image intensified (I2) and long wave infrared (LWIR) to create a hybrid image that will enable soldiers to better interpret their surroundings during nighttime missions. The key to such systems is the determination of the best image fusion algorithm for a specific task. A number of image fusion algorithms have been proposed in the literature. Currently, a scientific evaluation of such algorithms requires extensive and expensive human perception studies to determine how well soldiers can perform a specific task. What is needed is an image quality (IQ) measure than can automatically quantify the utility of image fusion algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2008
Accession Number
ADA503515

Entities

People

  • Lance Kaplan
  • Rick Blum

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Classification
  • Contrast
  • Correlation Analysis
  • Data Analysis
  • Data Compression
  • Detection
  • Governments
  • Gray Scale
  • Image Compression
  • Measurement
  • Military Research
  • Motor Skills
  • Night Vision
  • Perception
  • Test And Evaluation

Readers

  • Computer Vision.
  • Instructional Design and Training Evaluation.