29 January 2007 Rate-prediction structure complexity analysis for multi-view video coding using hybrid genetic algorithms
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Abstract
Efficient exploitation of the temporal and inter-view correlation is critical to multi-view video coding (MVC), and the key to it relies on the design of prediction chain structure according to the various pattern of correlations. In this paper, we propose a novel prediction structure model to design optimal MVC coding schemes along with tradeoff analysis in depth between compression efficiency and prediction structure complexity for certain standard functionalities. Focusing on the representation of the entire set of possible chain structures rather than certain typical ones, the proposed model can given efficient MVC schemes that adaptively vary with the requirements of structure complexity and video source characteristics (the number of views, the degrees of temporal and interview correlations). To handle large scale problem in model optimization, we deploy a hybrid genetic algorithm which yields satisfactory results shown in the simulations.
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Yebin Liu, Yebin Liu, Qionghai Dai, Qionghai Dai, Zhixiang You, Zhixiang You, Wenli Xu, Wenli Xu, } "Rate-prediction structure complexity analysis for multi-view video coding using hybrid genetic algorithms", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 650804 (29 January 2007); doi: 10.1117/12.703849; https://doi.org/10.1117/12.703849
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