Deep multi-scale features learning for distorted image quality assessment

Zhou, Wei and Chen, Zhibo 2021. Deep multi-scale features learning for distorted image quality assessment. Presented at: International Symposium on Circuits and Systems (ISCAS), Daegu, Korea, 22-28 May 2021. 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, pp. 1-5. 10.1109/ISCAS51556.2021.9401285

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Abstract

Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN- based quality assessment models by exploiting efficient multi- scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	978-1-7281-9201-7
ISSN:	2158-1525
Last Modified:	27 Sep 2023 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/162064

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