  The proliferation of high resolution videos posts great storage and bandwidth
pressure on cloud video services, driving the development of next-generation
video codecs. Despite great progress made in neural video coding, existing
approaches are still far from economical deployment considering the complexity
and rate-distortion performance tradeoff. To clear the roadblocks for neural
video coding, in this paper we propose a new framework featuring standard
compatibility, high performance, and low decoding complexity. We employ a set
of jointly optimized neural pre- and post-processors, wrapping a standard video
codec, to encode videos at different resolutions. The rate-distorion optimal
downsampling ratio is signaled to the decoder at the per-sequence level for
each target rate. We design a low complexity neural post-processor architecture
that can handle different upsampling ratios. The change of resolution exploits
the spatial redundancy in high-resolution videos, while the neural wrapper
further achieves rate-distortion performance improvement through end-to-end
optimization with a codec proxy. Our light-weight post-processor architecture
has a complexity of 516 MACs / pixel, and achieves 9.3% BD-Rate reduction over
VVC on the UVG dataset, and 6.4% on AOM CTC Class A1. Our approach has the
potential to further advance the performance of the latest video coding
standards using neural processing with minimal added complexity.
