The recent progress in artificial intelligence has led to an ever-increasing usage of images and videos by machine analysis algorithms, mainly neural networks. Nonetheless, compression, storage and transmission of media have traditionally been designed considering human beings as the viewers of the content. Recent research on image and video coding for machine analysis has progressed mainly in two almost orthogonal directions. The first is represented by end-to-end (E2E) learned codecs which, while offering high performance on image coding, are not yet on par with state-of-the-art conventional video codecs and lack interoperability. The second direction considers using the Versatile Video Coding (VVC) standard or any other conventional video codec (CVC) together with pre- and post-processing operations targeting machine analysis. While the CVC-based methods benefit from interoperability and broad hardware and software support, the machine task performance is often lower than the desired level, particularly in low bitrates. This paper proposes a hybrid codec for machines called NN-VVC, which combines the advantages of an E2E-learned image codec and a CVC to achieve high performance in both image and video coding for machines. Our experiments show that the proposed system achieved up to -43.20% and -26.8% Bj{\o}ntegaard Delta rate reduction over VVC for image and video data, respectively, when evaluated on multiple different datasets and machine vision tasks. To the best of our knowledge, this is the first research paper showing a hybrid video codec that outperforms VVC on multiple datasets and multiple machine vision tasks.

本研究提出了一种名为NN-VVC的混合编解码器，结合了E2E-learned图像编解码器和传统视频编解码器(CVC)，在图像和视频编码领域为机器实现高性能。实验证明，该系统在多个数据集和机器视觉任务上较VVC实现了高达-43.20%和-26.8%的Bjøntegaard Delta速率减小。据我们所知，这是第一篇在多个数据集和多个机器视觉任务上展示了优于VVC的混合视频编解码器的研究论文。

NN-VVC：通过自我监督学习的图像编码来增强通用视频编码能力