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arXiv · 2407.07395

Standard compliant video coding using low complexity, switchable neural wrappers

Yueyu Hu·Chenhao Zhang·Onur G. Guleryuz·Debargha Mukherjee·Yao Wang
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Abstract

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.

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Yueyu Hu, Chenhao Zhang, Onur G. Guleryuz, Debargha Mukherjee, Yao Wang. 2024-07-10. Standard compliant video coding using low complexity, switchable neural wrappers. https://arxiv.org/abs/2407.07395

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