The paper introduces a powerful conditional coding-based NVC that tackles critical issues through feature modulation. It addresses the limitations of existing models by supporting a wider quality range and enhancing rate control capabilities. The proposed DCVC-FM codec outperforms traditional codecs and previous SOTA NVC models, showcasing significant advancements in NVC technology.
The content discusses the challenges faced by traditional video codecs and the potential breakthroughs offered by neural video compression. It highlights the importance of addressing quality range limitations and long prediction chain issues in NVC evolution. The proposed method leverages feature modulation to enhance compression efficiency, support multiple colorspaces, and enable low-precision inference for faster processing.
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Overall, the paper presents a comprehensive approach to advancing neural video compression technology through innovative feature modulation techniques.
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by Jiahao Li,Bi... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.17414.pdfDeeper Inquiries