SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917744175218688 |
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| author | Cao, Linhan Sun, Wei Min, Xiongkuo Jia, Jun Zhang, Zicheng Chen, Zijian Zhu, Yucheng Liu, Lizhou Chen, Qiubo Chen, Jing Zhai, Guangtao |
| author_facet | Cao, Linhan Sun, Wei Min, Xiongkuo Jia, Jun Zhang, Zicheng Chen, Zijian Zhu, Yucheng Liu, Lizhou Chen, Qiubo Chen, Jing Zhai, Guangtao |
| contents | Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compression algorithms to achieve a trade-off between transmission bit rate and image quality. However, traditional JND prediction methods only rely on pixel-level or sub-band level features, lacking the ability to capture the impact of image content on JND. To bridge this gap, we propose a Semantic-Guided JND (SG-JND) network to leverage semantic information for JND prediction. In particular, SG-JND consists of three essential modules: the image preprocessing module extracts semantic-level patches from images, the feature extraction module extracts multi-layer features by utilizing the cross-scale attention layers, and the JND prediction module regresses the extracted features into the final JND value. Experimental results show that SG-JND achieves the state-of-the-art performance on two publicly available JND datasets, which demonstrates the effectiveness of SG-JND and highlight the significance of incorporating semantic information in JND assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04273 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression Cao, Linhan Sun, Wei Min, Xiongkuo Jia, Jun Zhang, Zicheng Chen, Zijian Zhu, Yucheng Liu, Lizhou Chen, Qiubo Chen, Jing Zhai, Guangtao Image and Video Processing Computer Vision and Pattern Recognition Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compression algorithms to achieve a trade-off between transmission bit rate and image quality. However, traditional JND prediction methods only rely on pixel-level or sub-band level features, lacking the ability to capture the impact of image content on JND. To bridge this gap, we propose a Semantic-Guided JND (SG-JND) network to leverage semantic information for JND prediction. In particular, SG-JND consists of three essential modules: the image preprocessing module extracts semantic-level patches from images, the feature extraction module extracts multi-layer features by utilizing the cross-scale attention layers, and the JND prediction module regresses the extracted features into the final JND value. Experimental results show that SG-JND achieves the state-of-the-art performance on two publicly available JND datasets, which demonstrates the effectiveness of SG-JND and highlight the significance of incorporating semantic information in JND assessment. |
| title | SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.04273 |