Kavli Affiliate: Wei Gao
| First 5 Authors: Chenhao Zhang, Chenhao Zhang, , ,
| Summary:
Neural Video Compression (NVC) has achieved remarkable performance in recent
years. However, precise rate control remains a challenge due to the inherent
limitations of learning-based codecs. To solve this issue, we propose a dynamic
video compression framework designed for variable bitrate scenarios. First, to
achieve variable bitrate implementation, we propose the Dynamic-Route
Autoencoder with variable coding routes, each occupying partial computational
complexity of the whole network and navigating to a distinct RD trade-off.
Second, to approach the target bitrate, the Rate Control Agent estimates the
bitrate of each route and adjusts the coding route of DRA at run time. To
encompass a broad spectrum of variable bitrates while preserving overall RD
performance, we employ the Joint-Routes Optimization strategy, achieving
collaborative training of various routes. Extensive experiments on the HEVC and
UVG datasets show that the proposed method achieves an average BD-Rate
reduction of 14.8% and BD-PSNR gain of 0.47dB over state-of-the-art methods
while maintaining an average bitrate error of 1.66%, achieving
Rate-Distortion-Complexity Optimization (RDCO) for various bitrate and
bitrate-constrained applications. Our code is available at
https://git.openi.org.cn/OpenAICoding/DynamicDVC.
| Search Query: ArXiv Query: search_query=au:”Wei Gao”&id_list=&start=0&max_results=3