Image Quality Assessment Using Kernel Sparse Coding

One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparse coding has emerged as a promising approach to analyzing image structures for IQA. However, existing sparse-coding-b...

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Bibliographic Details
Published in:IEEE transactions on multimedia Vol. 23; pp. 1592 - 1604
Main Authors: Zhou, Zihan, Li, Jing, Quan, Yuhui, Xu, Ruotao
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Construction
Dictionaries
dictionary learning
Encoding
Image coding
Image quality
Image quality assessment
Kernel
kernel sparse coding
Kernels
Mathematical model
Measurement
Nonlinearity
Quality assessment
sparse representation
Stability analysis
Visualization
ISSN:1520-9210, 1941-0077
Online Access:Get full text
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Summary:One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparse coding has emerged as a promising approach to analyzing image structures for IQA. However, existing sparse-coding-based IQA approaches use linear coding models, which ignore the nonlinearities of manifolds of image patches and thus cannot analyze complex image structures well. To overcome such a weakness, in this paper, we introduce nonlinear sparse coding to IQA. A kernel dictionary construction scheme is proposed, which combines analytic dictionaries and learnable dictionaries to guarantee both the stability and effectiveness of kernel sparse coding in the context of IQA. Built upon the kernel dictionary construction, an effective full-reference IQA metric is developed. Benefiting from the considerations on nonlinearities during sparse coding, the proposed IQA metric not only characterizes image distortions better, but also achieves improvement on the consistency with subjective perception, when compared to the metrics built upon linear sparse coding. Such benefits are demonstrated with the experimental results on eight benchmark datasets in terms of common criteria.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2020.3001472