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.
Highlights
The emergence of 5G [1] period has brought great innovation to communications industry, and the demand for information transmission has increased under the bombardment of high-speed information streams
To solve the problem on feature redundancy resulting in a large number of model parameters, we propose a new scheme that combines the explicit features represented by manually extracted wavelet features and the implicit features represented by the spatial features extracted by the deep learning network
That is, adding the extraction of frequency domain information on the basis of deep network, using the implicit features extracted by the deep network model VGGNet and the explicit features extracted by wavelet transform, we propose our deep image quality assessment [47] method based on EI-IQA
Summary
The emergence of 5G [1] period has brought great innovation to communications industry, and the demand for information transmission has increased under the bombardment of high-speed information streams. Different regions have different abilities to receive information due to their geographic location and other factors. Image is the main carrier of visual information [2] because it can intuitively reflect information, which is important in the information transmission process. During image acquisition and transmission, different degrees of distortion [3] are caused by various factors, such as processing system and environmental noise. Image quality directly affects the subjective perception of the human eye and the
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