  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 the neural model. The latter problem can be defined by the
discrepancy between the latent's marginal distribution and the learned prior
distribution. This mismatch, known as amortization gap of entropy model,
enlarges the file size of compressed data. In this paper, we propose to
evaluate the amortization gap for three state-of-the-art ML video compression
methods. Second, we propose an efficient and generic method to solve the
amortization gap and show that it leads to an improvement between $2\%$ to
$5\%$ without impacting reconstruction quality.
