Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics
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Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics Xiaohong Wang 1 & Yunjie Pang 1
& Xiangcai Ma
2
Received: 14 August 2019 / Revised: 3 June 2020 / Accepted: 15 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. Keywords IQA . Real distorted . K-means . High-level semantics
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11042-02009222-9) contains supplementary material, which is available to authorized users.
* Xiaohong Wang [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction In the era of big data, image has become the main carrier of information. During the acquiring, processing, encoding, storing, transmitting and reconstructing of digital images, image distortion can’t be avoided. Being able to automatically predict the quality of digital images can help to provide a satisfying end-user experience, which has become one of the hot research topics in the field of digital image processing and computer vision [1, 3, 21, 37]. The objective IQA mainly uses mathematical methods to find an ideal model to evaluate the quality of the image and to simulate the perception of human visual system. In recent years, a large number of IQA algorithms have been proposed.According to the image distortion type of IQA and the different
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