Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression. (arXiv:2210.06596v1 [cs.CV])

During the last four years, we have witnessed the success of end-to-end
trainable models for image compression. Compared to decades of incremental
work, these machine learning (ML) techniques learn all the components of the
compression technique, which explains their actual superiority. However,
end-to-end ML models have not yet reached the performance of traditional video
codecs such as VVC. Possible explanations can be put forward: lack of data to
account for the temporal redundancy, or inefficiency of latent's density
estimation in …

arxiv compression video video compression