Journal Article

CSPP-IQA: a multi-scale spatial pyramid pooling-based approach for blind image quality assessment

Zhou, Xiaokang, Chen, Jingjing, Yan, Ke, Guo, Lingling, Lu, Fangfang, Qin, Feng, Li, Chao

2022

Overview

The traditional image quality assessment (IQA) methods are usually based on convolutional neural networks (CNNs). For these IQA methods using CNNs, limited by the feature size of the fully connected layer, the input image needs be tailored to a pre-defined size, which usually results in destroying the original structure and content of the input image and thus reduces the accuracy of the quality assessment. In this paper, a blind image quality assessment method (named CSPP-IQA), which is based on multi-scale spatial pyramid pooling, is proposed. CSPP-IQA allows inputting the original image when assessing the image quality without any image adjustment. Moreover, by facilitating the convolutional block attention module and image understanding module, CSPP-IQA achieved better accuracy, generalization and efficiency than traditional IQA methods. The result of experiments running on real-scene IQA datasets in this study verified the effectiveness and efficiency of CSPP-IQA.

• Publisher: Springer London,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Artificial neural networks / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Coronaviruses / Data Mining and Knowledge Discovery / Deep learning / Image Processing and Computer Vision / Image quality / Methods / Modules / Neural networks / Probability and Statistics in Computer Science / Quality assessment / S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing / Semantics / Special Issue on Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing