Adversarial complementary learning for just noticeable difference estimation

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...

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Bibliographic Details
Main Authors: Yu, Dong, Jin, Jian, Meng, Lili, Chen, Zhipeng, Zhang, Huaxiang
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Computer and Information Science
Convolutional neural networks
Just noticeable difference
Online Access:https://hdl.handle.net/10356/178981

Internet

https://hdl.handle.net/10356/178981

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