Hierarchical Autoregressive Modeling for Neural Video Compression
ICLR(2021)
摘要
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 trans-form, 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.
更多查看译文
关键词
neural video compression,hierarchical autoregressive modeling
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络