论文标题
神经视频压缩的分层自回归建模
Hierarchical Autoregressive Modeling for Neural Video Compression
论文作者
论文摘要
Marino等人的最新工作。 （2020）通过将掩盖自回归流量与分层潜在可变模型相结合，在顺序密度估计中的性能提高了。我们在此类自回旋生成模型和有损视频压缩的任务之间建立了联系。具体而言，我们将最近的神经视频压缩方法（Lu等，2019; Yang等人，2020b; Agustssonet al。，2020）视为广义随机时间自动回归转换的实例，并提出了基于这种见解的途径。对大规模视频数据的全面评估显示，与最新的神经和常规视频压缩方法相比，率延伸性能提高了。
Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods.
