Adversarial complementary learning for just noticeable difference estimation
Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts...
Main Authors: | Yu, Dong, Jin, Jian, Meng, Lili, Chen, Zhipeng, Zhang, Huaxiang |
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Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/178981 |
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