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A Novel No-Reference Image Quality Metric For Assessing Sharpness In Satellite Imagery

Lucas Gonzalo Antonel

TL;DR

A novel no-reference image quality metric aimed at assessing image sharpness, designed to be robust against variations in noise, exposure, contrast, and image content, is introduced, offering an objective method for sharpness evaluation without reference images.

Abstract

This study introduces a novel no-reference image quality metric aimed at assessing image sharpness. Designed to be robust against variations in noise, exposure, contrast, and image content, it measures the normalized decay rate of gradients along pronounced edges, offering an objective method for sharpness evaluation without reference images. Primarily developed for satellite imagery to align with human visual perception of sharpness, this metric supports monitoring and quality characterization of satellite fleets. It demonstrates significant utility and superior performance in consistency with human perception across various image types and operational conditions. Unlike conventional metrics, this heuristic approach provides a way to score images from lower to higher sharpness, making it a reliable and versatile tool for enhancing quality assessment processes without the need for pristine or ground truth comparison. Additionally, this metric is computationally efficient compared to deep learning analysis, ensuring faster and more resource-effective sharpness evaluations.

A Novel No-Reference Image Quality Metric For Assessing Sharpness In Satellite Imagery

TL;DR

A novel no-reference image quality metric aimed at assessing image sharpness, designed to be robust against variations in noise, exposure, contrast, and image content, is introduced, offering an objective method for sharpness evaluation without reference images.

Abstract

This study introduces a novel no-reference image quality metric aimed at assessing image sharpness. Designed to be robust against variations in noise, exposure, contrast, and image content, it measures the normalized decay rate of gradients along pronounced edges, offering an objective method for sharpness evaluation without reference images. Primarily developed for satellite imagery to align with human visual perception of sharpness, this metric supports monitoring and quality characterization of satellite fleets. It demonstrates significant utility and superior performance in consistency with human perception across various image types and operational conditions. Unlike conventional metrics, this heuristic approach provides a way to score images from lower to higher sharpness, making it a reliable and versatile tool for enhancing quality assessment processes without the need for pristine or ground truth comparison. Additionally, this metric is computationally efficient compared to deep learning analysis, ensuring faster and more resource-effective sharpness evaluations.

Paper Structure

This paper contains 12 sections, 18 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Overview of No-Reference Image Sharpness Assessment Techniques
  3. Sharpness Metric Description
  4. Preprocessing
  5. Gradient Computation and Isolation
  6. Gaussian Blurring
  7. Percentage Degradation Calculation
  8. Final Sharpness Metrics
  9. Calculation of the Representativeness Indicator
  10. Metric Validation
  11. Simulated image benchmark
  12. Real image benchmark

Figures (7)

  • Figure 1: Sharpness Algorithm Diagram
  • Figure 2: Examples of images without enough edges
  • Figure 3: Sythetic Generation Diagram
  • Figure 4: Scatter plots of sharpness metric against $sigma$ levels. The size of each point corresponds to the block size, indicating its frequency content.
  • Figure 5: Filtered Sharpness X metric vs Sigma in X direction, for different noise levels
  • ...and 2 more figures