SbTMVP Motion Refinement With DMVR and BDOF for Video Decoding
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently refining motion vectors and optical flow in subblock-based temporal motion vector prediction, leading to suboptimal compression efficiency and quality in video encoding and decoding processes.
Innovation Solution
Implementing decoder-side motion vector refinement (DMVR) and bi-directional optical flow (BDOF) modes for subblock-based temporal motion vector prediction (SbTMVP) to enhance motion information accuracy and reduce redundancy in video data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subblock-based temporal motion vector prediction (SbTMVP) is used to improve motion vector accuracy, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The current block is divided into multiple subblocks, and motion vector prediction is performed independently for each subblock. This segmentation allows the system to achieve higher motion accuracy for individual subblocks while managing computational complexity by processing smaller units rather than treating the entire block uniformly.
Solution Approach 2:
Different motion compensation techniques are applied to different regions (subblocks) based on their local characteristics. By performing motion vector refinement at the subblock level, the system adapts to local motion variations, improving overall accuracy without requiring complex global optimization.
2Measurement precision
If decoder-side motion vector refinement (DMVR) and bi-directional optical flow (BDOF) are applied to enhance motion information accuracy, then compression performance is improved, but processing time and computational load increase
Solution Approach 1:
Motion vector refinement and optical flow calculations are performed during the decoding process itself, rather than requiring separate post-processing steps. This preliminary action during decoding integrates the refinement operations into the existing workflow, reducing overall processing time despite the added computational steps.
Solution Approach 2:
The system dynamically selects and applies refinement techniques (DMVR, BDOF) based on block characteristics and available resources. This dynamic approach allows the decoder to adapt computational effort to actual needs, improving motion accuracy where necessary while avoiding unnecessary processing in simpler cases.
Data Source
AI summary
Aspects of the disclosure provide a method and an apparatus including processing circuitry that obtaining prediction information indicating whether a current block is coded in a subblock-based temporal motion vector prediction (SbTMVP) mode. If the current block is coded in the SbTMVP mode, whether a subblock in a plurality of subblocks of the current block is bi-predicted is determined. If the subblock is bi-predicted, motion information of the subblock is determined based on the SbTMVP mode. At least one of (i) a bilateral matching (BM)-based motion vector (MV) refinement and (ii) a bi-directional optical flow (BDOF) mode is applied to the subblock to refine the motion information of the subblock. The current block is reconstructed based on refined motion information corresponding to one or more subblocks in the plurality of subblocks. The refined motion information corresponding to the one or more subblocks includes the refined motion information of the subblock.