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
- FWCI
- 22.91
- Percentile
- 100%
- References
- 49
Authors
4Topics & keywords
- Computer science
- JPEG
- Artificial intelligence
- Image quality
- Human visual system model
- Visibility
- Set (abstract data type)
- Similarity (geometry)