A new sharpness metric based on local kurtosis, edge and energy information

Jorge Caviedes, Franco Oberti

Research output: Contribution to journalArticlepeer-review

127 Scopus citations

Abstract

Sharpness metrics that use the whole frequency spectrum of the image cannot separate the sharpness information from the scene content. The sharpness metrics that use spatial gradients of the edges work only for comparisons among versions of the same image. We have developed a content independent, no-reference sharpness metric based on the local frequency spectrum around the image edges. In this approach, we create an edge profile by detecting edge pixels and assigning them to 8 × 8 pixel blocks. Then we compute sharpness using the average 2D kurtosis of the 8 × 8 DCT blocks. However, average kurtosis is highly sensitive to asymmetry in the DCT, e.g. different amounts of energy and edges in the x and y directions, therefore causing problems with different content and asymmetric sharpness enhancement. Thus we compensate the kurtosis using spatial edge extent information and the amount of vertical and horizontal energy in the DCT. The results show high correlation with subjective quality for sharpness-enhanced video and high potential to deal with asymmetric enhancement. For compressed, extremely sharpened and noisy video, the metric correlates with subjective scores up to the point where impairments become strongly noticeable in the subjective quality evaluation. The metric can be used by itself as a control variable for high-quality image capture and display systems, high-quality sharpness enhancement algorithms, and as a key component of a more general overall quality metric.

Original languageEnglish (US)
Pages (from-to)147-161
Number of pages15
JournalSignal Processing: Image Communication
Volume19
Issue number2
DOIs
StatePublished - Feb 1 2004
Externally publishedYes

Keywords

  • Edge profile sharpness
  • No-reference sharpness metric
  • Objective image quality

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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