Back to Search
Start Over
Explicit-implicit dual stream network for image quality assessment
- Source :
- EURASIP Journal on Image and Video Processing, Vol 2020, Iss 1, Pp 1-13 (2020)
- Publication Year :
- 2020
- Publisher :
- SpringerOpen, 2020.
-
Abstract
- Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA.
- Subjects :
- Feature fusion
EI dual stream network
Image quality
business.industry
Computer science
Deep learning
Feature extraction
Wavelet feature extraction
lcsh:Electronics
Wavelet transform
lcsh:TK7800-8360
Pattern recognition
Convolutional neural network
Wavelet
IQA
Frequency domain
Signal Processing
Pattern recognition (psychology)
Artificial intelligence
Electrical and Electronic Engineering
business
CNN
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 16875281
- Volume :
- 2020
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- EURASIP Journal on Image and Video Processing
- Accession number :
- edsair.doi.dedup.....6d4d56294532d51e0a4bf6811581e081