Image Quality Assessment

Kalpana Seshadrinathan*, Thrasyvoulos N. Pappas, Robert J. Safranek, Junqing Chen, Zhou Wang, Hamid R. Sheikh, Alan C. Bovik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Scopus citations

Abstract

This chapter examines objective criteria for the evaluation of image quality as perceived by an average human observer. The focus is on image fidelity, i.e., how close an image is to a given original or reference image. This paradigm of image quality assessment (QA) is also known as full reference image QA. Three classes of image QA algorithms that correlate with visual perception significantly better are discussed-human vision based metrics, Structural SIMilarity (SSIM) metrics, and information theoretic metrics. Each of these techniques approaches the image QA problem from a different perspective and using different first principles. In addition to these QA techniques, this chapter also highlights the similarities, dissimilarities, and interplay between these seemingly diverse techniques.

Original languageEnglish (US)
Title of host publicationThe Essential Guide to Image Processing
PublisherElsevier Inc
Pages553-595
Number of pages43
ISBN (Print)9780123744579
DOIs
StatePublished - Dec 1 2009

ASJC Scopus subject areas

  • Computer Science(all)

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    Seshadrinathan, K., Pappas, T. N., Safranek, R. J., Chen, J., Wang, Z., Sheikh, H. R., & Bovik, A. C. (2009). Image Quality Assessment. In The Essential Guide to Image Processing (pp. 553-595). Elsevier Inc. https://doi.org/10.1016/B978-0-12-374457-9.00021-4