A neural video codec with spatial rate-distortion control

Noor Fathima Ghouse, Jens Petersen, Guillaume Sautière, Auke Wiggers, Reza Pourreza

Abstract: Neural video compression algorithms are nearly competitive with hand-crafted codecs in terms of rate-distortion performance and subjective quality. However, many neural codecs are inflexible black boxes, and give users little to no control over the reconstruction quality and bitrate. In this work, we present a flexible neural video codec that combines ideas from variable-bitrate codecs and region-of-interest-based coding. By conditioning our model on a global rate-distortion tradeoff parameter and a region-of-interest (ROI) mask, we obtain dynamic control over the per-frame bitrate and the reconstruction quality in the ROI at test time. The resulting codec enables practical use cases such as coding under bitrate constraints with fixed ROI quality, while taking a negligible hit in performance compared to a fixed-rate model. We find that our codec performs best on sequences with complex motion, where we substantially outperform non-ROI codecs in the region of interest with Bjøntegaard-Delta rate savings exceeding 60%.