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We study causal, low-latency, sequential video compression when the output issubjected to both a mean squared-error (MSE) distortion loss as well as aperception loss to target realism. Motivated by prior approaches, we considertwo different perception loss functions (PLFs). The first, PLF-JD, considersthe joint distribution (JD) of all the video frames up to the current one,while the second metric, PLF-FMD, considers the framewise marginaldistributions (FMD) between the source and reconstruction. Using informationtheoretic analysis and deep-learning based experiments, we demonstrate that thechoice of PLF can have a significant effect on the reconstruction, especiallyat low-bit rates. In particular, while the reconstruction based on PLF-JD canbetter preserve the temporal correlation across frames, it also imposes asignificant penalty in distortion compared to PLF-FMD and further makes it moredifficult to recover from errors made in the earlier output frames. Althoughthe choice of PLF decisively affects reconstruction quality, we alsodemonstrate that it may not be essential to commit to a particular PLF duringencoding and the choice of PLF can be delegated to the decoder. In particular,encoded representations generated by training a system to minimize the MSE(without requiring either PLF) can be {\em near universal} and can generateclose to optimal reconstructions for either choice of PLF at the decoder. Wevalidate our results using (one-shot) information-theoretic analysis, detailedstudy of the rate-distortion-perception tradeoff of the Gauss-Markov sourcemodel as well as deep-learning based experiments on moving MNIST and KTHdatasets.

On the Choice of Perception Loss Function for Learned Video Compression