Motion Compensated Frame Interpolation Using SAD and BAD Classification
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Solution Overview
Problem
Existing video decoding techniques face challenges in achieving high-quality frame interpolation with minimal computing complexity, particularly when using motion compensated frame interpolation (MCFI), as they often introduce artifacts due to inaccurate motion vector estimation.
Innovation Solution
The proposed method limits motion estimation to blocks with embedded motion vectors that represent true motion, using sum of absolute differences (SAD) and boundary absolute difference (BAD) to classify motion vectors, and employs overlapped block bi-directional motion estimation and vector smoothing to generate interpolated frames with reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion compensated frame interpolation is performed using traditional motion estimation on all blocks, then interpolation quality improves, but computing complexity increases significantly
Solution Approach 1:
The patent segments the frame into different block types (first type blocks with accurate motion vectors and second type blocks requiring motion estimation) based on motion characteristics. This segmentation allows motion estimation to be performed only on necessary blocks rather than all blocks, reducing computing complexity while maintaining interpolation quality where needed.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the frame based on local motion characteristics. Blocks with accurate embedded motion vectors use simpler processing, while blocks identified as having inaccurate motion vectors undergo full motion estimation. This local quality approach optimizes the balance between overall interpolation quality and computing complexity.
2Device complexity
If frame repetition is used for interpolation, then computing complexity is minimized, but visible jerky artifacts are introduced
Solution Approach 1:
Instead of applying full motion estimation to all blocks (excessive action), the patent applies motion estimation only partially to blocks where it is truly needed (those with inaccurate embedded motion vectors). This partial action approach avoids the computational overhead of universal motion estimation while still preventing artifacts in critical regions.
3Device complexity
If frame averaging is used for interpolation, then computing complexity is reduced, but blurry artifacts are introduced due to ignoring motion
Solution Approach 1:
The patent performs preliminary classification of blocks to identify which ones have accurate embedded motion vectors and which require motion estimation. This preliminary action allows the system to prepare motion-compensated data in advance for blocks where it is needed, preventing blurry artifacts while avoiding unnecessary computations for blocks where simple averaging suffices.
Data Source
AI summary
Video decoding that employs motion compensated frame interpolation (MCFI) is described. The MCFI technique minimizes computations by limiting motion estimation to blocks having motion vectors that do not represent true motion. The MCFI determines these blocks using a sum of absolute differences (SAD) and a boundary absolute difference (BAD) between a current block and a previous block. The current block and previous block are obtained based on a selected interpolated block and an embedded motion vector for a block within the current frame having the same position as the selected interpolated block. If the SAD and BAD measurements do not meet certain thresholds, the embedded motion vector is classified as not representing true motion. Overlapped block bi-directional motion estimation is then performed to obtain a new motion vector. Overlapped block motion compensation is employed to generate the interpolated frame. Vector smoothing may be performed before generating the interpolated frame.