Blind image quality assessment based on the multiscale and dual‐domains features fusion
- 22 February 2021
- journal article
- research article
- Published by Wiley in Concurrency and Computation: Practice and Experience
- Vol. 35 (18), e6177
- https://doi.org/10.1002/cpe.6177
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61901436, 61904173)
This publication has 37 references indexed in Scilit:
- U-Net: Convolutional Networks for Biomedical Image SegmentationPublished by Springer Science and Business Media LLC ,2015
- Difference of Gaussian statistical features based blind image quality assessment: A deep learning approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual RecognitionIEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
- Image database TID2013: Peculiarities, results and perspectivesSignal Processing: Image Communication, 2015
- Blind image quality assessment on real distorted images using deep belief netsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Automatic Prediction of Perceptual Image and Video QualityProceedings of the IEEE, 2013
- No-Reference Image Quality Assessment in the Spatial DomainIEEE Transactions on Image Processing, 2012
- DCT statistics model-based blind image quality assessmentPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Most apparent distortion: full-reference image quality assessment and the role of strategyJournal of Electronic Imaging, 2010
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004