Smooth Sub-Block Motion Vector Prediction from Neighboring Blocks
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently reducing redundancy in video signals, particularly in determining sub-block motion vectors, which affects storage, data processing, and transmission bandwidth in applications like video streaming and conferencing.
Innovation Solution
The method involves determining sub-block motion vectors based on motion information from spatial and temporal neighboring blocks, using syntax elements and reference motion vectors to derive motion compensation for video blocks, enhancing the efficiency of video encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional motion compensation is used for video blocks, then the encoding process is simpler, but the compression efficiency and redundancy reduction are insufficient
Solution Approach 1:
The video block is divided into multiple sub-blocks, and each sub-block is assigned its own motion vector derived from neighboring blocks. This segmentation allows for more precise motion compensation at the sub-block level, improving compression efficiency without requiring a complete redesign of the encoding framework.
Solution Approach 2:
Different motion vectors are applied to different sub-blocks based on their local motion characteristics. By deriving motion vectors from spatial and temporal neighboring blocks specific to each sub-block, the method achieves local optimization of motion compensation, thereby improving overall compression efficiency while managing complexity through localized processing.
2Measurement precision
If sub-block motion vectors are determined using available candidate motion vectors from neighboring blocks, then the motion compensation accuracy is improved, but the computational complexity increases
Solution Approach 1:
Candidate motion vectors from spatial and temporal neighboring blocks are collected and prepared in advance before the actual motion compensation process. This preliminary gathering of candidate vectors allows for more accurate selection without significantly increasing the complexity during the main encoding/decoding phase, as the candidate set is ready for reference.
Solution Approach 2:
The system uses its own previously decoded neighboring blocks to generate candidate motion vectors for current blocks. This self-service mechanism leverages already-available data within the same video stream, improving motion vector accuracy without requiring external information or additional complex processing systems.
3Measurement precision
If more candidate motion vectors are considered from spatial and temporal blocks, then the prediction accuracy improves, but the data processing time increases
Solution Approach 1:
The processing is segmented into independent sub-block operations, where each sub-block can have its motion vector determined separately using its own neighboring blocks. This segmentation allows for parallel processing potential and prevents the need to process the entire video block sequentially, thereby reducing overall data processing time while maintaining high prediction accuracy through localized candidate selection.
Data Source
AI summary
This disclosure relates generally to video coding and particularly to smooth-sub-block motion compensation, disclosing methods and systems for determining subblock motion vectors of a video block based on motion information of its spatial neighboring block, temporal blocs spatially co-located with the neighboring blocks, or co-located temporal block of the video block.