Deep Video Compression with Conditional Feature Coding

In the last years, deep video coding has attracted a lot of research interest. Usually, it employs the concept of inter coding by transmitting features in a latent space that represent a motion field or a residual. However, in such a setting there are still redundancies between the features of consecutive frames. In previous approaches, these redundancies are exploited for compression by adding an additional input at the encoder and decoder. However, this often comes at the cost of changing the whole network architecture. In this paper, we present a conditional coding for motion features which utilizes already transmitted features for coding the features of the current picture in a more effective way. This concept can be applied on top of any existing coding framework. Our coding experiments, which were conducted for JVET test sequences, demonstrate that the proposed conditional motion feature coding can yield bit-rate savings of up to 9% relative to an independent coding of the motion features for individual pictures.