# Efficient Nonlinear Transforms for Lossy Image Compression

arXiv: https://arxiv.org/abs/1802.00847 · HTML: https://mlsanity.com/paper/1802.00847/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 20 of 20)

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- [Quality and Complexity Assessment of Learning-Based Image Compression Solutions](https://mlsanity.com/paper/2107.09136/) (2021)
- [Transformations in Learned Image Compression from a Modulation Perspective](https://mlsanity.com/paper/2203.02158/) (2022)
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## Abstract (arXiv)

We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN. Both techniques have been successfully used in state-of-the-art image compression methods, but their performance has not been individually assessed to this point. Together, the techniques stabilize the training procedure of nonlinear image transforms and increase their capacity to approximate the (unknown) rate-distortion optimal transform functions. Besides comparing their performance to established alternatives, we detail the implementation of both methods and provide open-source code along with the paper.