Image quality assessment via spatial‐transformed domains multi‐feature fusion. Issue 4 (18th February 2020)

• Record Type: Journal Article
• Authors: Yu, Miaomiao; Zheng, Yuanlin; Liao, Kaiyang; Tang, Zhisen
• Is Part Of: IET image processing. Volume 14:Issue 4(2020)
• Page Start: 648
• Page End: 657
• Publication Date: 2020-02-18
• Subjects: regression analysis -- feature extraction -- gradient methods -- image fusion -- transforms -- vectors -- random forests -- learning (artificial intelligence); spatial‐transformed domains multifeature fusion -- image processing -- subjective methods -- image quality assessment tasks -- edge contour information -- mask scale -- cross‐database operation capability -- gradient information operators -- feature extraction -- 12‐dimensional feature vector generation -- random forest regression technique -- FVC‐G; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6417
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The basis of image processing is to evaluate and monitor image quality using algorithms rather than subjective methods. Conventional gradient operators have been popularly used in previous image quality assessment tasks to reflect the edge contour of an image, while there are some obvious defects in terms of the selection of mask scale and direction. Some improved versions are also less than ideal since they fail to consider the gradient information of the same pixel in different directions at the same time. The authors adopt a powerful gradient operator that can simultaneously capture edge information in all four directions at the same pixel point with more relevant values being considered instead of selecting the maximum in these four directions. Furthermore, four complementary types of features extracted from the spatial and transform domains are considered. A set of 12‐dimensional feature vectors is generated for each image by multi‐feature fusion. Ultimately, random forest regression technique is employed to train their model and then map the distortion effects to the prediction scores. The experimental results show that the proposed FVC‐G has better overall performance, more powerful cross‐database operation capability, and higher visual consistency than other advanced methods.