Motion Vector Range Constraints in Video Coding
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently encoding and decoding motion vectors (MVs) due to limitations in precision and range constraints, particularly in high-resolution video applications where fractional MV precision is insufficient, leading to suboptimal compression ratios and increased bandwidth requirements.
Innovation Solution
The proposed solution involves processing circuitry that decodes prediction information from a coded video bitstream to determine motion information for a current block, including a first motion vector with fractional precision, and stores this information in a memory space optimized for efficient representation and reconstruction, using specific bit allocations for MV components, reference indices, and prediction directions to ensure accurate and efficient MV handling within defined ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fractional MV precision is increased to improve motion vector representation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by introducing a fractional MV precision parameter (N) that can be configured independently. The system changes the precision parameter from fixed to variable, allowing adaptive adjustment based on video content and coding requirements. This enables higher precision when needed while maintaining lower precision for simpler cases, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent implements dynamics by making the MV precision adaptive rather than static. The fractional precision N is dynamically adjusted based on the motion characteristics of the video content and the requirements of the coding application. This dynamic adjustment allows the system to optimize between precision and complexity in real-time, avoiding fixed high precision that would always increase device complexity.
2Device complexity
If MV range constraints are applied to limit motion vector values, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent resolves this contradiction by changing the parameter of MV range constraints from fixed to variable. Instead of applying a fixed constraint that would always reduce precision, the system adjusts the constraint range based on the fractional precision N and the specific motion characteristics. This allows the range constraint to adapt to different precision requirements, maintaining precision while limiting complexity only when necessary.
Solution Approach 2:
The patent applies dynamics by making the MV range constraint adaptive. The constraint is dynamically adjusted based on the fractional precision parameter N and the motion vector magnitude. When motion is small, the constraint is tighter, reducing complexity. When motion is large or precision is higher, the constraint is relaxed, maintaining precision. This dynamic approach resolves the contradiction between complexity reduction and precision maintenance.
3Productivity
If higher compression ratios are pursued through lossy compression, then loss of information increases, but productivity is improved
Solution Approach 1:
The patent applies parameter changes by introducing a fractional precision parameter N that can be adjusted to optimize the compression-distortion tradeoff. By changing the precision parameter, the system can achieve higher compression ratios when acceptable distortion is tolerated, while maintaining higher precision when information fidelity is critical. This resolves the contradiction by allowing adaptive optimization rather than fixed compromise.
Solution Approach 2:
The patent implements dynamics by making the compression characteristics adaptive through fractional precision adjustment. The system dynamically adjusts the precision parameter N based on the application requirements, allowing higher compression efficiency when distortion is acceptable and maintaining information fidelity when needed. This dynamic approach resolves the contradiction between productivity and information loss by enabling flexible optimization.
Data Source
AI summary
Aspects of the disclosure provide a method and an apparatus for video coding. In some examples, the apparatus includes processing circuitry. The processing circuitry determines, for a current block in a current picture, motion information including a first motion vector (MV) that has a x component and a y component. Each of the x and y components has a fractional MV precision that is 2−N of a sample size in the current block and has one of 2L+1 MV values with the fractional MV precision, N being 4 and indicating the fractional MV precision, and a magnitude of the first MV being represented by L bits. At least one sample of the current block is encoded based on the motion information.