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首页> 外文期刊>Information Theory, IEEE Transactions on >Rate Distortion Theory for Causal Video Coding: Characterization, Computation Algorithm, and Comparison
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Rate Distortion Theory for Causal Video Coding: Characterization, Computation Algorithm, and Comparison

机译：因果视频编码的速率失真理论：表征，计算算法和比较

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摘要

Causal video coding is considered from an information theoretic point of view, where video source frames $X_1, X_2, ldots, X_N$ are encoded in a frame by frame manner, the encoder for each frame $X_k$ can use all previous frames and all previous encoded frames while the corresponding decoder can use only all previous encoded frames, and each frame $X_k$ itself is modeled as a source $X_k = {X_{k}(i) }_{i=1}^{infty}$ . A novel computation approach is proposed to analytically characterize, numerically compute, and compare the minimum total rate of causal video coding $R_{c}^*(D_1, ldots,D_N)$ required to achieve a given distortion (quality) level $D_1, ldots,D_N > 0$. Among many other things, the computation approach includes an iterative algorithm with global convergence for computing $R_{c}^*(D_1, ldots,D_N)$ . The global convergence of the algorithm further enables us to demonstrate a somewhat surprising result (dubbed the more and less coding theorem)—under some conditions on source frames and distortion, the more frames need to be encoded and transmitted, the less amount of data after encoding has to be actually sent. With the help of the algorithm, it is also shown by example that $R_{c}^*(D_1, ldots,D_N)$ is in general much smaller than the total rate offered by the traditional greedy coding method. As a by-product, an extended Markov lemma is established for correlated ergodic sources.
机译：从信息理论的角度考虑因果视频编码，其中视频源帧$ X_1，X_2，ldots，X_N $以逐帧方式编码，每个帧$ X_k $的编码器可以使用所有先前的帧以及所有先前的编码帧，而相应的解码器只能使用所有先前的编码帧，并且每个帧$ X_k $本身都被建模为源$ X_k = {X_ {k}（i）} _ {i = 1} ^ {infty} $ 。提出了一种新颖的计算方法，用于分析表征，数值计算和比较达到给定失真（质量）级别$ D_1所需的因果视频编码$ R_ {c} ^ *（D_1，ldots，D_N）$的最小总速率，ldots，D_N> 0 $。除其他外，计算方法还包括一种具有全局收敛性的迭代算法，用于计算$ R_ {c} ^ *（D_1，ldots，D_N）$。该算法的全局收敛性还使我们能够展示出令人惊讶的结果（被称为越来越少的编码定理）-在源帧和失真的某些条件下，需要编码和传输的帧越多，获得的数据量就越少编码必须实际发送。借助于该算法，还通过示例示出了$ R_ {c} ^ *（D_1，ldots，D_N）$通常比传统贪婪编码方法所提供的总速率小得多。作为副产品，为相关遍历源建立了扩展的马尔可夫引理。

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