Affine Motion Estimation Using Epipolar Constraint
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Solution Overview
Problem
Complex motion models for video encoding in vehicles result in high processing, memory, and time requirements, leading to increased bitrate and degraded prediction quality due to challenges in determining optimal parameters.
Innovation Solution
Implementing epipolar-based constraints to reduce the complexity of affine motion estimation by determining an epipolar curve from block corners, using known camera geometry and minimizing block matching errors to constrain the search to a single parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex motion models are used for video encoding, then prediction quality is improved, but processing complexity and memory requirements increase
Solution Approach 1:
The patent changes the parameter space by constraining the affine motion model search from multiple parameters to a single parameter along the epipolar curve. This reduces the complexity of determining optimal motion parameters while maintaining prediction accuracy for vehicles with known geometry
Solution Approach 2:
The patent introduces the epipolar curve as an intermediary constraint that connects the current block to the reference picture. This curve, derived from known vehicle camera geometry, serves as a mediator that simplifies the motion search process while preserving prediction quality
2Measurement precision
If complex motion models are used for video encoding, then prediction quality is improved, but encoding time increases
Solution Approach 1:
The patent reduces the number of parameters to be optimized from multiple affine parameters to a single parameter representing displacement along the epipolar curve. This parameter reduction significantly decreases encoding time while maintaining prediction quality through the geometric constraint
3Measurement precision
If complex motion models are used for video encoding, then prediction quality is improved, but memory requirements increase
Solution Approach 1:
The patent reduces memory requirements by changing from storing and searching multiple affine parameters to storing and searching a single parameter along the epipolar curve. The known camera geometry is stored once and reused, reducing overall memory usage
Data Source
AI summary
An apparatus configured to encode video data is configured to receive a first picture captured at a first time and a first location, receive a second picture captured at a second time and a second location, wherein the second time is different from the first time, and wherein the second location is different than the first location, and encode a current block of the second picture, relative to the first picture, using affine motion prediction with a single parameter search, wherein the single parameter search is performed along an epipolar curve, and wherein the single parameter search is used to determine parameters of an affine motion model.