Consistent Disparity Synthesis for Inter-View Prediction in Lightfield Compression

Abstract

For efficient compression of lightfields that involve many views, it has been found preferable to explicitly communicate disparity/depth information at only a small subset of the view locations. In this study, we focus solely on inter-view prediction, which is fundamental to multi-view imagery compression, and itself depends upon the synthesis of disparity at new view locations. Current HDCA standardization activities consider a framework known as WaSP, that hierarchically predicts views, independently synthesizing the required disparity maps at the reference views for each prediction step. A potentially better approach is to progressively construct a unified multi-layered base-model for consistent disparity synthesis across many views. This paper improves significantly upon an existing base-model approach, demonstrating superior performance to WaSP. More generally, the paper investigates the implications of texture warping and disparity synthesis methods.