CSPP-IQA: a Multi-scale Spatial Pyramid Pooling-based Approach for Blind Image Quality Assessment

Overview

Journal Neural Comput Appl

Date 2022 Oct 24

PMID 36276656

Authors

Jingjing Chen

Feng Qin

Fangfang Lu

Lingling Guo

Chao Li

Ke Yan

Xiaokang Zhou

Affiliations

Soon will be listed here.

Abstract

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.

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