Hierarchical Independent Coding Scheme for Varifocal Multiview Images Based on Angular-Focal Joint Prediction

Varifocal multiview (VFMV) images are dense views that focus on variable focal planes. Thus, VFMV images are highly redundant in the angular, spatial and focal dimensions. In this article, the redundancies of VFMV images are analyzed and represented by full parallaxes and focal inconsistency. To exploit these distinctive redundancies, we propose a hierarchical independent coding scheme based on angular-focal joint prediction. The scheme is constructed by hierarchical independent prediction structure (HIPS) and angular-focal joint prediction (AFJP). The HIPS separates all views into several independent subdivisions and assigns different hierarchies inside each subdivision, which enhances random access capability and scalability. The AFJP conducts motion estimation and focal approximation simultaneously to predict parallaxes and focal inconsistency. Therefore, the redundancies in the angular and focal dimensions can be exploited by the proposed coding scheme. We construct a VFMV dataset with 10 test sequences for different acquisition methods. The experimental results on these test sequences demonstrate that the proposed scheme outperforms all comparison schemes in objective quality, subjective quality and random access capability. Specifically, the proposed coding scheme achieves up to 2.661 dB PSNR gains and 52.817% bitrate savings compared with the HEVC random access benchmark scheme.