Image quality assessment: from error visibility to structural similarity
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
PDF Abstract
Datasets
MSU FR VQA Database
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Video Quality Assessment | MSU FR VQA Database | SSIM | SRCC | 0.8999 | # 11 | |
| PLCC | 0.9253 | # 6 | ||||
| KLCC | 0.7615 | # 7 | ||||
| Video Quality Assessment | MSU SR-QA Dataset | SSIM | SROCC | 0.22468 | # 52 | |
| PLCC | 0.20670 | # 53 | ||||
| KLCC | 0.17175 | # 52 | ||||
| Type | FR | # 1 |
