Motion Vector Predictor Selection for Variable Block Motion
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Solution Overview
Problem
Existing video coding techniques face challenges in efficiently predicting motion vectors, particularly in adapting to varying block sizes and motion patterns within video sequences.
Innovation Solution
The proposed method involves adaptively selecting a motion vector predictor from two lists of candidates, derived at the sub-prediction unit and prediction unit levels, based on an active sub-prediction unit motion flag. This allows for more accurate motion vector prediction by considering spatial and temporal neighbors of the current block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single prediction unit level motion vector predictor is used, then the device complexity is reduced, but the motion vector prediction accuracy deteriorates for varying block sizes and motion patterns
Solution Approach 1:
The prediction unit is divided into sub-prediction units, and motion vector predictors are derived at both the prediction unit level and sub-prediction unit level. This segmentation allows the system to handle varying block sizes and motion patterns more accurately by selecting appropriate granularity for different regions.
Solution Approach 2:
The system dynamically selects between prediction unit level and sub-prediction unit level motion vector predictors based on the sub-prediction unit motion flag and actual motion characteristics. This dynamic adaptation optimizes prediction accuracy for different block sizes and motion patterns without requiring a fixed complex structure.
2Measurement precision
If sub-prediction unit level motion prediction candidates are derived, then the motion vector prediction accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system uses the sub-prediction unit motion flag to dynamically determine whether to derive sub-prediction unit level motion vector predictors. When the flag indicates uniform motion, the simpler prediction unit level predictors are used, reducing computational complexity while maintaining accuracy when needed.
Solution Approach 2:
Sub-prediction unit level motion vector predictors are derived only for regions where motion variation is detected (indicated by the sub-prediction unit motion flag). This local application of complex prediction methods optimizes the balance between accuracy and computational complexity by avoiding unnecessary calculations in uniform motion regions.
3Adaptability or versatility
If motion vector predictors are derived from multiple lists, then the adaptability to different motion patterns is improved, but the device complexity increases
Solution Approach 1:
The motion vector predictors are segmented into two distinct lists: prediction unit level candidates and sub-prediction unit level candidates. This segmentation allows the system to manage different types of predictors separately, improving adaptability to various motion patterns while organizing the complexity in a structured manner.
Solution Approach 2:
The system dynamically selects which candidate list to use based on the sub-prediction unit motion flag and motion characteristics. This dynamic selection mechanism provides adaptability to different motion patterns without requiring the system to permanently maintain and process all candidate types simultaneously, thus managing device complexity effectively.
Data Source
AI summary
A method of decoding video data comprising parsing a sub-prediction unit motion flag from received encoded video data, deriving a list of sub-prediction unit level motion prediction candidates if the sub-prediction unit motion flag is active, deriving a list of prediction unit level motion prediction candidates if the sub-prediction unit motion flag is not active, and decoding the encoded video data using a selected motion vector predictor.