sbTMVP Motion Vector Prediction for Efficient Image and Video Coding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images and videos, particularly in immersive media formats like VR and AR, has led to higher transmission and storage costs due to the increased amount of information required, necessitating a high-efficiency compression technology.
Innovation Solution
The implementation of subblock-based temporal motion vector prediction (sbTMVP) to derive motion vectors efficiently by using available reference subblocks and base motion vectors, unified at the sub-coding block and coding block levels, improving inter prediction and reducing calculation complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality image/video data are transmitted or stored using existing methods, then image quality is maintained, but transmission costs and storage costs increase significantly
Solution Approach 1:
The current block is divided into multiple subblocks, and motion vector prediction is performed separately for each subblock. This segmentation allows for more precise local motion compensation, improving prediction accuracy while maintaining manageable data complexity through localized processing.
Solution Approach 2:
Different motion prediction strategies are applied to different subblocks based on their local characteristics. By deriving sbTMVP candidates specific to each subblock's motion patterns, the system achieves locally optimized prediction quality that adapts to varying motion complexity across the image block.
2Measurement precision
If subblock-based temporal motion vector prediction is implemented, then prediction performance is improved, but calculation complexity increases
Solution Approach 1:
The system performs preliminary checks to determine whether sbTMVP can be applied to each subblock before executing the full prediction process. By evaluating availability conditions in advance (such as checking if reference subblocks exist and are valid), the system avoids unnecessary complex calculations for subblocks where sbTMVP cannot be applied, thus reducing overall computational burden.
Solution Approach 2:
The system applies sbTMVP only to subblocks where it is beneficial and feasible, rather than uniformly to all subblocks. By selectively applying the technique based on availability flags and reference block validity, the system achieves partial action that balances prediction improvement with computational efficiency.
3Measurement precision
If reference subblocks are derived for all subblocks, then motion vector prediction accuracy is improved, but memory access and processing time increase
Solution Approach 1:
The system performs preliminary availability checks to determine whether reference subblocks exist and are valid for each subblock before attempting to derive motion vectors. This early evaluation prevents wasted processing time on subblocks where reference information is unavailable or invalid, allowing the system to skip problematic subblocks and proceed efficiently.
Solution Approach 2:
The system uses already-decoded reference picture data and existing motion information from neighboring blocks to self-determine the availability of reference subblocks. By leveraging previously processed information rather than requiring additional external queries or complex searches, the system reduces processing overhead while maintaining prediction accuracy where possible.
Data Source
AI summary
According to the disclosure of the present document, in a subblock-based temporal motion vector prediction (sbTMVP), a position of a subblock for deriving a motion vector per subblock unit can be efficiently calculated, and thus video/image coding efficiency can be increased and hardware implementation can be simplified.