Name
Abstract | ||
|---|---|---|
Objective image quality assessment (IQA) models aim to automatically predict human visual perception of image quality and are of fundamental importance in the field of image processing and computer vision. With an increasing number of IQA models proposed, how to fairly compare their performance becomes a major challenge due to the enormous size of image space and the limited resource for subjective testing. The standard approach in literature is to compute several correlation metrics between subjective mean opinion scores (MOSs) and objective model predictions on several well-known subject-rated databases that contain distorted images generated from a few dozens of source images, which however provide an extremely limited representation of real-world images. Moreover, most IQA models developed on these databases often involve machine learning and/or manual parameter tuning steps to boost their performance, and thus their generalization capabilities are questionable. Here we propose a novel methodology to compare IQA models. We first build a database that contains 4,744 source natural images, together with 94,880 distorted images created from them. We then propose a new mechanism, namely group MAximum Differentiation (gMAD) competition, which automatically selects subsets of image pairs from the database that provide the strongest test to let the IQA models compete with each other. Subjective testing on the selected subsets reveals the relative performance of the IQA models and provides useful insights on potential ways to improve them. We report the gMAD competition results between 16 well-known IQA models, but the framework is extendable, allowing future IQA models to be added into the competition. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.184 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer vision,Pattern recognition,Human visual perception,Computer science,Image quality,Image processing,Correlation,Artificial intelligence,Machine learning | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 13 |
PageRank | References | Authors |
0.56 | 10 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kede Ma | 1 | 773 | 27.93 |
| Qingbo Wu | 2 | 399 | 39.78 |
| Z Wang | 3 | 13331 | 630.91 |
| Zhengfang Duanmu | 4 | 171 | 8.24 |
| Hongwei Yong | 5 | 167 | 9.41 |
| Hongliang Li | 6 | 1833 | 101.92 |
| Lei Zhang | 7 | 16326 | 543.99 |