Crowdsourcing Based Cross Random Access Point Referencing For Video Coding

In video coding, Random Access Points (RAPs) are inserted in a bitstream to support flexible tune-in but divide it into multiple independent Random Access Segments (RASs) that may have similar contents. To reduce redundancy between RASs, this letter proposes a novel Cross Random-access-point Referencing (CRR) structure to provide inter prediction for RAP pictures by using multiple External Reference Pictures (ERPs) across RAPs that are selected from preceding or following RASs other than the current RAS. With ERPs shared by multiple RASs, a crowdsourcing method is proposed to optimize the joint rate distortion costs of RASs and ERPs to generate an optimal set of ERPs. Content preparation and bitstream splicing processes supported by system environments are also designed to ensure random access functionality of CRR coded RASs. Simulation results show that CRR achieves significant coding gain compared to Versatile Video Coding (VVC), i.e., 12.00% on sequences in common test condition and 25.48% on long drama sequences.