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 language||English (US)|
|Title of host publication||The Essential Guide to Image Processing|
|Number of pages||43|
|State||Published - Jan 1 2009|
ASJC Scopus subject areas
- Computer Science(all)