articleJan 1, 2004Closed access

Image Quality Assessment: From Error Measurement to Structural Similarity

The University of Texas at Austin · Supélec · +2 more institutions

Abstract

Abstract—Objective methods for assessing perceptual im-age quality traditionally attempt to quantify the visibility of errors (differences) between a distorted image and a ref-erence image using a variety of known properties of the hu-man visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative com-plementary framework for quality assessment based on the degradation of structural information. As a specific exam-ple of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive ex-amples, as well as comparison to both subjective ratings and state-of-the-art…

Citation impact

886
total citations
FWCI
22.91
Percentile
100%
References
49
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • JPEG
  • Artificial intelligence
  • Image quality
  • Human visual system model
  • Visibility
  • Set (abstract data type)
  • Similarity (geometry)
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