High-Order Statistical Modeling Based on a Decision Tree for Distributed Video Coding
Aiming at low-complexity encoding, distributed video coding (DVC) based on the Wyner-Ziv theorem has attracted significant attention. However, there is still a compression performance gap between the state-of-the-art DVC and the conventional video coding. One of the most important factors is the efficient estimation of the source correlation statistics. The first-order Laplacian distribution has been widely used for source correlation modeling, but is not effective enough; high-order statistical modeling is necessary, but needs more context features for the estimation of a source symbol’s conditional probability. How to analyze the strength of the correlation between the source symbol and the context features in order to utilize the features effectively is crucial for such modeling. In this paper, the estimation of the source statistical distribution is first treated as a classification problem. The symbols of the source can be classified into different classes when the relevant context features are given. Then decision tree learning is introduced to analyze the strength of the correlation between the source symbol and the context features. Specifically, by constructing the decision trees composed of the selected context features, the selected context features can be organized effectively to derive the rules to estimate the current symbol’s conditional probability, upon which the high-order statistical modeling is designed. Experimental results show that the proposed model can achieve significant coding gain over existing DVC systems, especially for natural videos with high motion intensity.