Segmentation-based perceptual image quality assessment (SPIQA)

Bernard Ghanem*, Esther Resendiz, Narendra Ahuja

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


Computational representation of perceived image quality is a fundamental problem in computer vision and image processing, which has assumed increased importance with the growing role of images and video in human-computer interaction. It is well-known that the commonly used Peak Signal-to-Noise Ratio (PSNR), although analysis-friendly, falls far short of this need. We propose a perceptual image quality measure (IQM) in terms of an image's region structure. Given a reference image and its "distorted" version, we propose a "full-reference" IQM, called Segmentation-based Perceptual Image Quality Assessment (SPIQA), which quantifies this quality reduction, while minimizing the disparity between human judgment and automated prediction of image quality. One novel feature of SPIQA is that it enables the use of inter- and intraregion attributes in a way that closely resembles how the human visual system (HVS) perceives distortion. Experimental results over a number of images and distortion types demonstrate SPIQA's performance benefits.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Number of pages4
StatePublished - Dec 1 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: Oct 12 2008Oct 15 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2008 IEEE International Conference on Image Processing, ICIP 2008
Country/TerritoryUnited States
CitySan Diego, CA


  • HVS
  • Iqm
  • QA
  • Saliency
  • Segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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