Adaptation and Attention for Neural Video Coding

no code implementations • 16 Dec 2021 • Nannan Zou, Honglei Zhang, Francesco Cricri, Ramin G. Youvalari, Hamed R. Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu

In this work, we propose an end-to-end learned video codec that introduces several architectural novelties as well as training novelties, revolving around the concepts of adaptation and attention.

Decoder Image Compression +1

Lossless Image Compression Using a Multi-Scale Progressive Statistical Model

no code implementations • 24 Aug 2021 • Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Nannan Zou, Emre Aksu, Miska M. Hannuksela

Recently, multi-scale autoregressive models have been proposed to address this limitation.

Image Compression

Learning to Learn to Compress

no code implementations • 31 Jul 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Miska Hannuksela, Emre Aksu, Esa Rahtu

In a second phase, the Model-Agnostic Meta-learning approach is adapted to the specific case of image compression, where the inner-loop performs latent tensor overfitting, and the outer loop updates both encoder and decoder neural networks based on the overfitting performance.

Decoder Image Compression +2

End-to-End Learning for Video Frame Compression with Self-Attention

no code implementations • 20 Apr 2020 • Nannan Zou, Honglei Zhang, Francesco Cricri, Hamed R. -Tavakoli, Jani Lainema, Emre Aksu, Miska Hannuksela, Esa Rahtu

One of the core components of conventional (i. e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations.

Decoder MS-SSIM +2