Two-Level Intra Prediction Using High-Order Macropixel Neighbors For Plenoptic Video Coding
Vinh Van Duong, Thuc Nguyen Huu, Jonghoon Yim, Byeungwoo Jeon · 2024
This paper introduces a novel intra-prediction scheme for coding plenoptic video which can effectively exploit large correlation between current and neighboring macropixel images. While the intra block copy method is well recognized as a promising coding tool for plenoptic video, it has fundamental issues like much searching time for block vectors (BVs) and more bits to encode these BVs into a bitstream. Our method can effectively solve them by pre-defining the prediction candidates to save encoding time and signaling only the index of prediction location instead of BVs to reduce the overhead bits for encoding BVs. Compared to HEVC, our method is experimentally shown to achieve an average bitrate gain of about $19.70 \%$ and $11.99 \%$ respectively under the AI-Main and RA-Main conditions. Moreover, better trade-off can be made between complexity and coding performance than existing methods.