For any video codecs, the coding efficiency highly relies on whether the current signal to be
encoded can find the relevant contexts from the previous reconstructed signals. Traditional …

Most of the existing neural video compression methods adopt the predictive coding
framework, which first generates the predicted frame and then encodes its residue with the …

We propose a novel neural representation for videos (NeRV) which encodes videos in
neural networks. Unlike conventional representations that treat videos as frame sequences …

Learning based video compression attracts increasing attention in the past few years. The
previous hybrid coding approaches rely on pixel space operations to reduce spatial and …

For neural video codec, it is critical, yet challenging, to design an efficient entropy model
which can accurately predict the probability distribution of the quantized latent …

We show how transformers can be used to vastly simplify neural video compression.
Previous methods have been relying on an increasing number of architectural biases and …

Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed methods for …

The previous deep video compression approaches only use the single scale motion
compensation strategy and rarely adopt the mode prediction technique from the traditional …

Recent years have witnessed an explosion of user-generated content (UGC) videos shared
and streamed over the Internet, thanks to the evolution of affordable and reliable consumer …

No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and
important problem for social and streaming media applications. Efficient and accurate video …