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Neural video compression with diverse contexts
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 …
encoded can find the relevant contexts from the previous reconstructed signals. Traditional …
Neural video compression with feature modulation
The emerging conditional coding-based neural video codec (NVC) shows superiority over
commonly-used residual coding-based codec and the latest NVC already claims to …
commonly-used residual coding-based codec and the latest NVC already claims to …
Hybrid spatial-temporal entropy modelling for neural video compression
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 …
which can accurately predict the probability distribution of the quantized latent …
Temporal context mining for learned video compression
Applying deep learning to video compression has attracted increasing attention in recent
few years. In this work, we address end-to-end learned video compression with a special …
few years. In this work, we address end-to-end learned video compression with a special …
Coarse-to-fine deep video coding with hyperprior-guided mode prediction
The previous deep video compression approaches only use the single scale motion
compensation strategy and rarely adopt the mode prediction technique from the traditional …
compensation strategy and rarely adopt the mode prediction technique from the traditional …
Transformer-based transform coding
Neural data compression based on nonlinear transform coding has made great progress
over the last few years, mainly due to improvements in prior models, quantization methods …
over the last few years, mainly due to improvements in prior models, quantization methods …
Hnerv: A hybrid neural representation for videos
Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …
vision tasks such as video compression and denoising. With frame index and/or positional …
VCT: A video compression transformer
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 …
Previous methods have been relying on an increasing number of architectural biases and …
C3: High-performance and low-complexity neural compression from a single image or video
Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …
to generalize to unseen data. Such generalization typically requires large and expressive …
Dnerv: Modeling inherent dynamics via difference neural representation for videos
Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal
redundancies in videos. Index-based INRs ignore the content-specific spatial features and …
redundancies in videos. Index-based INRs ignore the content-specific spatial features and …