Motivation and Context
A significant part of planetary data is corrupted by noise, bad pointing, exposure time or gain, i.e. beyond the point that they have much scientific meaning. The available planetary datasets include many such "bad data", which both occupy valuable scientific storage resources and create false impressions about planetary data availability for specific planetary objects or target areas.

Generic Problem Description
The development of techniques for automated assessment of planetary image quality with the minimum possible amount of a priori information.

Project Goals

Image Descriptors Used for Quality Assessment (in Italics the novel descriptors)

  1. Power Spectrum Slope
    R. Liu, Z. Li and J. Jia, Image Partial Blur Detection and Classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2008.
  2. Image Anisotropy
    S. Gabarda and G. Cristobal, Blind Image Quality Assessment Through Anisotropy, Journal of the Optical Society of America, Vol. 24, No. 12, pp. B42-B51, 2007.
  3. Edge Profile Kurtosis
    J. Caviedes and S. Gurbuz, No-reference sharpness metric based on local edge kurtosis, IEEE International Conference on Image Processing (ICIP), Vol. 3, pp. 53-56, 2002.
  4. Image Self-Similarity
  5. Local Contrast
  6. Image Pixel Pairwise Statistics

Classification and Assessment
The above six descriptors are combined into a 36-dimensional feature vector per image which forms the input of two distinct classification schemes:

Experimental Dataset
Planetary visual spectrum images that were acquired from Viking Orbiter missions.

Experimental Results
Manual Annotation: 250 "high-quality" and 250 "low-quality" images

Viking Orbiter images automatically assessed to be of high-quality
Viking Orbiter images automatically assessed to be of low-quality