Motion Vector Prediction via Temporal Distance Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The high data volume of digital video streams requires efficient compression methods, particularly in predicting motion vectors to reduce bandwidth and storage needs, as existing techniques often fail to effectively leverage temporal correlations across frames.
Innovation Solution
The method involves determining predicted motion vectors for a current frame based on collocated blocks from previous frames, weighting these vectors by temporal distances to create current motion vectors, and combining them with the video signal for encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If motion vectors are predicted using collocated blocks from previous frames, then the number of bits required for transmission is reduced, but the complexity of the prediction process increases
Solution Approach 1:
The patent performs preliminary weighting of motion vectors based on temporal distances before the actual prediction process. By pre-calculating weights for motion vectors from multiple reference frames and storing them in a buffer, the system prepares prediction data in advance, reducing the computational burden during real-time encoding/decoding while maintaining accurate temporal correlation-based predictions
Solution Approach 2:
The patent introduces a buffer as an intermediary component that stores weighted motion vectors from multiple reference frames. This buffer acts as a mediator between the reference frames and the current frame prediction process, allowing the system to efficiently access and combine historical motion information without directly processing all reference frames simultaneously, thus reducing computational complexity
2Measurement precision
If temporal distances are used to weight motion vectors, then prediction accuracy is improved, but the computational resources required increase
Solution Approach 1:
The patent changes the parameter of motion vectors by applying temporal distance-based weighting factors. Instead of using raw motion vectors directly, the system transforms them by multiplying with weights derived from temporal distances between reference frames and current frame. This parameter transformation improves prediction accuracy by emphasizing more relevant temporal information while the buffering mechanism manages the computational cost efficiently
3Quantity of substance
If multiple reference frames are combined with weighting, then video quality is maintained at lower bitrates, but the device complexity increases
Solution Approach 1:
The patent segments the motion prediction process into distinct stages: (1) calculating temporal distances, (2) computing weights for each reference frame, (3) weighting motion vectors from multiple reference frames, and (4) combining weighted vectors. This segmentation allows the complex process of combining multiple reference frames to be broken down into manageable steps, reducing implementation complexity while maintaining the ability to leverage temporal correlations for high-quality compression
Data Source
AI summary
Motion vectors are predicted for a current block of a current frame using motion vectors from previous frames in the video stream. Temporal distances between a current frame and the one or more reference frames used to predict a current block and temporal distances between the previous frame and its reference frames are determined. Temporal distances for current frames and previous frames can be combined to weight the motion vectors and improve motion vector prediction.