Improving view random access via increasing hierarchical levels for multi-view video coding

Multi-view video has attracted considerable interest due to its wide use in a growing market including 3D television, free viewpoint video and intelligent surveillance. Besides the efficiency of compression methods, a vital requirement for multi-view video coding procedures is the view random access which is described as the capability to navigate quickly to any arbitrary view at any given time. In this paper, a new prediction structure based on the increase of the hierarchical level of B-views is proposed. This approach involves reducing the number of images needed for the prediction of B pictures for specific B-views. To show the effectiveness of the proposed prediction structure, a new metric for evaluating the view random access is described. In contrast to the metric proposed by the JVT group which is limited to consider only the image having the maximum number of frames needed for decoding, the key basis of the proposed metric is to consider evaluating all the images contained within a Group of Group Of Pictures. Experimental results have shown that compared with the IBP prediction structure of the reference model JMVM, the proposed algorithm improves the view random access by up to 33.5% with significant improvement in terms of bit rate.