An image quality assessment algorithm based on 'global + local' feature fusion.

Yang Yang, Norisma Binti Idris, Ainuddin Wahid Abdul Wahab, Dingguo Yu, Chang Liu

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Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Yang YangFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Norisma Binti IdrisFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Ainuddin Wahid Abdul WahabFaculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Dingguo YuCollege of Media Engineering, Communication University of Zhejiang, Hang Zhou, China.

Chang LiuCollege of Media Engineering, Communication University of Zhejiang, Hang Zhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, there has been increasing research on image quality assessment. Among the existing mainstream approaches, image feature extraction tends to be simplistic, leading to insufficient quality information extraction and underutilization of the extracted data. Additionally, the correlation between different regions of the image is often neglected. This study proposes an image quality assessment algorithm based on global-local feature fusion (IQA-GL). First, the global and local features of the image are extracted separately, and irrelevant information in the local features is filtered out. Then, a global-local feature fusion model is constructed to enhance the interaction of feature information and gather image quality data across all feature channels. Finally, the relationship between individual image patches and the global image is modeled, adjusting the weights of each image patch to aggregate a quality score for the global image. Experimental results show the IQA-GL performs excellently on public datasets. This study innovatively combines global and local features, offering a new perspective for image quality assessment.

Indexed as

Convolutional networksFeature channelsFull-referenceGlobal-local feature fusionHierarchical perception mechanismHuman visual requirementsImage feature extractionImage quality assessmentStructural similarity indexSubjective biases

Identifiers

PMID40989329

DOI10.7717/peerj-cs.3074

PMCPMC12453738

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LicenceCC BY

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