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Blind Image Quality Assessment Based on Classification Guidance and Feature Aggregation.
- Source :
- Electronics (2079-9292); Nov2020, Vol. 9 Issue 11, p1811-1811, 1p
- Publication Year :
- 2020
-
Abstract
- In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an image using Sub-Network I and then quantifies this distortion using Sub-Network II. Different from most deep neural networks, we extract hierarchical features as descriptors to enhance the image representation and design a feature aggregation layer in an end-to-end training manner applying Fisher encoding to visual vocabularies modeled by Gaussian mixture models (GMMs). Considering the authentic distortions and synthetic distortions, the hierarchical feature contains the characteristics of a CNN trained on the self-built dataset and a CNN trained on ImageNet. We evaluated our algorithm on four publicly available databases, and the results demonstrate that our CGFA-CNN has superior performance over other methods both on synthetic and authentic databases. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 9
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Electronics (2079-9292)
- Publication Type :
- Academic Journal
- Accession number :
- 147273647
- Full Text :
- https://doi.org/10.3390/electronics9111811