Chained Motion Vector Prediction for Video Coding
Yoshitaka Kidani, Haruhisa Kato, Kei Kawamura
Abstract
Merge mode has been utilized in advanced video coding standards to facilitate the efficiency of Motion vector (MV) or Block Vector (BV) coding. Merge mode constructs multiple MVs or BVs as the merge candidate list from MV/BV storage and then specifies one within the list by signaling the merge index. Despite various merge candidate derivation methods in prior arts, they do not adequately reach MV/BV, pointing to reference pictures with low quantization noise, leaving room for improved coding performance. This paper proposes a Chained Motion Vector Prediction (CMVP) as a novel merge candidate derivation. The CMVP derives new candidates by accumulating the recursively traced MVs or BVs based on pre-derived merge candidates. Experimental results demonstrate that the proposed method achieves up to 1.01% coding gains with negligible complexity increases on version 12 of Enhanced Compression Model (ECM), a reference software for evaluating promising coding tools beyond Versatile Video Coding (VVC).
BibTeX
@inproceedings{icassp2025_chainedmotionvec,
title = {Chained Motion Vector Prediction for Video Coding},
author = {Yoshitaka Kidani and Haruhisa Kato and Kei Kawamura},
booktitle = {ICASSP 2025},
year = {2025}
}