Search Results for author: Fabian Mentzer

Found 11 papers, 8 papers with code

Towards Generative Video Compression

no code implementations26 Jul 2021 Fabian Mentzer, Eirikur Agustsson, Johannes Ballé, David Minnen, Nick Johnston, George Toderici

We present a neural video compression method based on generative adversarial networks (GANs) that outperforms previous neural video compression methods and is comparable to HEVC in a user study.

Video Compression

Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

3 code implementations24 Jun 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.

MS-SSIM SSIM +1

High-Fidelity Generative Image Compression

3 code implementations NeurIPS 2020 Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson

We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system.

Image Compression

Learning Better Lossless Compression Using Lossy Compression

1 code implementation CVPR 2020 Fabian Mentzer, Luc van Gool, Michael Tschannen

We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system.

Image Compression

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

3 code implementations CVPR 2020 Ren Yang, Fabian Mentzer, Luc van Gool, Radu Timofte

In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.

Image Compression MS-SSIM +2

Practical Full Resolution Learned Lossless Image Compression

2 code implementations CVPR 2019 Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.

Image Compression

Towards Image Understanding from Deep Compression without Decoding

1 code implementation ICLR 2018 Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced by these compression methods.

Classification General Classification +1

Conditional Probability Models for Deep Image Compression

1 code implementation CVPR 2018 Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc van Gool

During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.

Image Compression MS-SSIM +2

Deep Structured Features for Semantic Segmentation

no code implementations26 Sep 2016 Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini

We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms.

Classification General Classification +1

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