Context-Based Motion Estimation Vector Refinement
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Solution Overview
Problem
The high computational complexity of motion estimation in video encoders, particularly in the H.265/MPEG-H HEVC standard, due to the need for sub-pel resolution refinement, poses challenges for real-time encoding and efficient coding efficiency, especially with the increased demand for High Definition (HD) and Ultra High Definition (UHD) content.
Innovation Solution
A context-based refinement method that determines a context from a set of lower-pel resolution vectors to sequentially test higher-pel resolution candidate vectors, reducing computational complexity while maintaining coding efficiency by selecting the motion estimation vector with the lowest coding cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sub-pel resolution motion estimation refinement is performed using conventional interpolation-and-search methods, then coding efficiency is improved, but computational complexity increases significantly
Solution Approach 1:
The patent divides the motion estimation process into two distinct stages: integer-pel resolution search followed by sub-pel resolution refinement. This segmentation allows the computationally intensive integer-pel search to be performed first, establishing a coarse motion vector, and then a more efficient context-based refinement is applied only to determine the sub-pel position. This segmentation reduces overall computational complexity while maintaining precision.
Solution Approach 2:
The patent performs preliminary integer-pel resolution motion estimation before conducting sub-pel refinement. By establishing the integer-pel motion vector first, the subsequent sub-pel search can be constrained to a smaller neighborhood around this preliminary result, significantly reducing the number of candidate positions that need to be evaluated and thus lowering computational complexity.
2Reliability
If full search space evaluation is performed for sub-pel refinement, then optimal motion vector is guaranteed, but processing time increases
Solution Approach 1:
The patent applies context-based adaptive refinement where the search strategy and refinement process are tailored to local characteristics of the video content and motion patterns. By analyzing local context information and adapting the refinement process accordingly, the patent achieves near-optimal motion vectors with significantly reduced processing time compared to exhaustive full-search methods.
3Productivity
If high-precision sub-pel motion estimation is implemented, then coding efficiency improves, but real-time encoding capability deteriorates
Solution Approach 1:
The patent implements a dynamic two-stage motion estimation approach where the first stage performs integer-pel search and the second stage performs context-based sub-pel refinement. This dynamic process adapts to different video content characteristics, achieving high coding efficiency when needed while maintaining real-time encoding capability by avoiding unnecessary computational overhead in all cases.
Data Source
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AI summary
The refinement of a lower-pel resolution motion estimation vector is made more effective by preceding the actual refinement with a determination of a context out of a set of context candidates based on coding costs of a set of lower-pel resolution vectors within a neighborhood of the lower-pel resolution motion estimation vector, wherein each context candidate is associated with a ranking among a set of higher-pel resolution candidate vectors surrounding the lower-pel resolution motion estimation vector. The actual refinement of the lower-pel resolution motion estimation vector to obtain the higher-pel resolution motion estimation vector may thus use the ranking associated with the context determined: sequentially arranged accordingly, the set of higher-pel resolution candidate vectors may be tested sequentially.