Image compression with neural networks - A survey

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

End-To-End Optimized Speech Coding with Deep Neural Networks (2017, background): “In the domain of image compression, there has been some interest in training ANN-based systems since the 1990s [9], but this has not yielded state-of-the-art results until fairly recently either (starting August 2016, when Toderici et al.”
Variable Rate Image Compression with Recurrent Neural Networks (2015, neutral): “The basic principles of using feed-forward neural networks for image compression have been known for some time (Jiang, 1999).”
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression (2022, methodology): “As a crucial technique in image processing, lossy image compression has been studied for an extended period [17, 20, 30, 38].”
Joint Autoregressive and Hierarchical Priors for Learned Image Compression (2018, methodology): “Considerable research followed these initial models, and Jiang provides a comprehensive survey covering methods published through the late 1990s [29].”
Medical image analysis with artificial neural networks (2010, background): “Another fundamental technique for medical imaging is registration, which plays important roles in many areas of medical applications [26-32].”