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Inter prediction based on spatio-temporal adaptive localized learning model

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4 Author(s)
Hao Chen ; Nat. Eng. Res. Center for Multimedia software, Wuhan Univ., Wuhan, China ; Ruimin Hu ; Zhongyuan Wang ; Rui Zhong

Inter prediction based on block matching motion estimation is important for video coding. But this method suffers from the additional overhead in data rate representing the motion information that needs to be transmitted to the decoder. To solve this problem, we present an improved implicit motion information inter prediction algorithm for P slice in H.264/AVC based on the spatio-temporal adaptive localized learning (STALL) model. According to 4 × 4 block transform structure in H.264/AVC, we first adaptively choose nine spatial neighbors and nine temporal neighbors, and a localized 3D casual cube is designed as training window. By using these information, the model parameters could be adaptively computed based on the Least Square Prediction (LSP) method. Finally, we add a new inter prediction mode into H.264/AVC standard for P slice. The experimental results show that our algorithm improves encoding efficiency compared with H.264/AVC standard, with relatively increases in complexity.

Published in:

Picture Coding Symposium (PCS), 2010

Date of Conference:

8-10 Dec. 2010