Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding
arXiv (Cornell University)(2023)
摘要
Conditional coding is a new video coding paradigm enabled by
neural-network-based compression. It can be shown that conditional coding is in
theory better than the traditional residual coding, which is widely used in
video compression standards like HEVC or VVC. However, on closer inspection, it
becomes clear that conditional coders can suffer from information bottlenecks
in the prediction path, i.e., that due to the data processing inequality not
all information from the prediction signal can be passed to the reconstructed
signal, thereby impairing the coder performance. In this paper we propose the
conditional residual coding concept, which we derive from information
theoretical properties of the conditional coder. This coder significantly
reduces the influence of bottlenecks, while maintaining the theoretical
performance of the conditional coder. We provide a theoretical analysis of the
coding paradigm and demonstrate the performance of the conditional residual
coder in a practical example. We show that conditional residual coders
alleviate the disadvantages of conditional coders while being able to maintain
their advantages over residual coders. In the spectrum of residual and
conditional coding, we can therefore consider them as “the best from both
worlds”.
更多查看译文
关键词
Video Compression,Conditional Coding,Conditional Autoencoder,Information Theory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要