A CNN-Based Quality Assessment Method for Pseudo 4K Contents

Lü, Wei; Sun, Weiqiang; Zhu, Wenhan; Min, Xiongkuo; Zhang, Zicheng; Wang, Tao; Zhai, Guangtao

doi:10.1007/978-981-19-2266-4_13

Cited by 5 publications

(1 citation statement)

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“…With the rapid development of machine learning techniques, a large number of learning-based NR IQA metrics have been developed in the last few years [31][32][33]. COR-NIA [34] is based on an unsupervised feature learning framework that uses raw image patches as local descriptors and uses soft-assignment for encoding.…”

Section: Nr Iqa Metricsmentioning

confidence: 99%

Hybrid No-Reference Quality Assessment for Surveillance Images

Zhao

2022

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Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics.

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