Open AccessImproved side information generation algorithm based on naive Bayesian theory for distributed video coding
Author(s) -
Cao Ying,
Sun Lijuan,
Han Chong,
Guo Jian
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0892
Subject(s) - computer science , bayesian probability , coding (social sciences) , algorithm , naive bayes classifier , artificial intelligence , mathematics , statistics , support vector machine
In Wyner–Ziv (WZ) video coding, side information (SI), which is a decoder estimation of the original frame, plays a key role in overall compression performance. Many researchers have focused on SI in the past decade to develop efficient SI generation algorithms. In this study, the authors propose an algorithm combined with naive Bayesian theory to create a generic model that can complete the generation of SI in the WZ video coding framework. The proposed scheme first utilises samples to build the initial model, after which the algorithm filters the samples and models according to the threshold T 1 . Then, the algorithm takes the filtered samples and models as conditions to build the generic model. Finally, the proposed scheme completes the generation of SI with the motion vectors obtained from the generated model. Experimental results show that the proposed algorithm achieves better rate‐distortion performance and improves peak signal‐to‐noise ratio by up to 0.5 and 2 dB compared with state‐of‐the‐art techniques.