Motion Vector Selection for Video Encoding
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Solution Overview
Problem
Existing video encoding techniques fail to accurately capture and utilize motion information from reference frames, leading to inefficiencies in inter-prediction, particularly for frames with complex non-translational motion, and do not fully exploit available motion information for improved coding efficiency.
Innovation Solution
The method involves reconstructing reference frames and projecting motion vectors onto the current frame to select a weighted motion vector for prediction, using a co-located reference frame generated through motion field estimation and refinement, which captures true motion activities and improves prediction accuracy without significant computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion vectors from multiple reference frames are projected onto the current frame, then motion information accuracy is improved, but the number of projected motion vectors increases leading to increased computational complexity
Solution Approach 1:
The patent changes the parameter of motion vector selection by introducing magnitude-based weighting. Instead of treating all projected motion vectors equally, the patent assigns weights based on vector magnitudes and selects the optimal vector through weighted comparison, thereby improving motion accuracy while managing computational complexity through parameter optimization.
2Device complexity
If a single motion vector is selected from multiple projected motion vectors, then computational complexity is reduced, but motion information accuracy deteriorates
Solution Approach 1:
The patent introduces magnitude as a selection parameter to differentiate between multiple projected motion vectors. By calculating weights based on vector magnitudes and selecting the vector with optimal weight, the patent achieves accurate motion representation while maintaining single-vector simplicity for prediction operations.
Solution Approach 2:
The patent implements a feedback mechanism where multiple motion vectors are projected and evaluated, their magnitudes are compared, and the optimal vector is selected based on weighted evaluation. This feedback loop ensures that the selected vector best represents the actual motion, improving accuracy while still selecting only one vector for the final prediction.
3Productivity
If motion vectors are projected from both first and second reference frames, then coding efficiency is improved, but the amount of data to be processed increases
Solution Approach 1:
The patent extracts only the essential motion information from multiple reference frames by projecting motion vectors and then selecting the single most representative vector based on magnitude weighting. This extraction process captures the critical motion data needed for accurate prediction while discarding redundant information, thereby improving coding efficiency without proportionally increasing data processing requirements.
Data Source
AI summary
Methods, systems and apparatuses are disclosed including computer readable medium storing instructions used to encode or decode a video or a bitstream encodable or decodable using disclosed steps. The steps include reconstructing a first reference frame and a second reference frame for a current frame to be encoded or decoded, projecting motion vectors of the first reference frame and the second reference frame onto pixels of a current reference frame resulting in a first pixel in the current reference frame being associated with a plurality of projected motion vectors, and selecting a first projected motion vector from the plurality of projected motion vectors as a selected motion vector associated with the first pixel to be used for determining a pixel value of the first pixel, the selection based on magnitudes of the respective ones of the plurality of projected motion vectors.