Standard compliant video coding using low complexity, switchable neural wrappers
Yueyu Hu, Chenhao Zhang, Onur G. Guleryuz, Debargha Mukherjee, Yao, Wang

TL;DR
This paper introduces a standard-compatible neural wrapper framework for video coding that improves rate-distortion performance with low complexity, enabling better integration of neural processing into existing codecs.
Contribution
It proposes a novel neural wrapper architecture with optimized pre- and post-processors, achieving significant BD-Rate reductions while maintaining low decoding complexity.
Findings
Achieves 9.3% BD-Rate reduction over VVC on UVG dataset.
Achieves 6.4% BD-Rate reduction on AOM CTC Class A1.
Post-processor complexity is only 516 MACs per pixel.
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…
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Taxonomy
TopicsDigital Filter Design and Implementation · Advanced Data Compression Techniques · Advanced Vision and Imaging
MethodsSparse Evolutionary Training
