SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
<|reference_start|>SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression: 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.<|reference_end|>
@article{cao2024sg-jnd:,
    title={SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image
  Compression},
    author={Linhan Cao, Wei Sun, Xiongkuo Min, Jun Jia, Zicheng Zhang, Zijian
  Chen, Yucheng Zhu, Lizhou Liu, Qiubo Chen, Jing Chen, Guangtao Zhai},
    journal={arXiv preprint arXiv:2408.04273},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04273},
    primaryClass={eess.IV cs.CV}
}