Rate-distortion optimization for scalable multi-view video coding

In recent years, multi-view/3D video applications, such as three-dimensional television (3DTV) and free-viewpoint television (FTV), have drawn increasing attention. Since the amount of data that has to be stored or transmitted increases proportionally with the number of cameras, efficient compression of multi-view/3D video is crucial. Scalable multi-view video coding is one of the methods to address this challenge. But, in streaming multi-view/3D video over a network to heterogeneous receivers, efficient video compression while maintaining a high quality of received video is very challenging. This paper presents a novel method for rate-distortion optimization in scalable multiview video. We apply the Karush-Kuhn-Tucker (KKT) conditions in minimizing the perceptual distortion of decoded video under the conditions that the sum of bits generated from different views is constrained within a given bit budget. Since the constraint-based optimization problem is usually computational intensive, our proposed approach considers the concept of disparity between layers and disparity between views to reduce this computational complexity. Simulation results indicate that the proposed approach is able to meet network bandwidth limitations with acceptable overall video quality.