Journal
SIGNAL PROCESSING-IMAGE COMMUNICATION
Volume 40, Issue -, Pages 1-15Publisher
ELSEVIER
DOI: 10.1016/j.image.2015.10.005
Keywords
No reference (NR); Image quality assessment (IQA); Spatial correlation; Oriented gradient correlation; AdaBoosting neural network
Categories
Funding
- National Natural Science Foundation of China [61133008]
Ask authors/readers for more resources
The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both full- and no- reference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity. (C) 2015 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available