Video Encoding and Decoding With Control-Point Predictors for Affine Motion
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Solution Overview
Problem
Existing video encoding and decoding technologies face challenges in achieving high compression efficiency due to limitations in predicting complex motion patterns, particularly when using affine motion models, as they often rely on translational motion vectors, which limit the number of candidate predictors and hinder optimal compression performance.
Innovation Solution
The introduction of a non-affine motion model, specifically a translational motion model, which allows for an increased number of candidate predictors by determining control point generator motion vectors based on motion information, thereby improving the prediction of affine motion models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If affine motion models use translational motion vectors, then the model structure is simple, but the number of candidate predictors is limited
Solution Approach 1:
The block is divided into sub-blocks, and each sub-block is assigned its own motion vector independently. This segmentation allows multiple candidate predictors to be formed from different sub-block motion vectors, increasing the number of available predictors while keeping each individual motion vector simple (translational).
Solution Approach 2:
The patent transitions from using only block-level motion vectors to incorporating sub-block level motion vectors. This dimensional change from block-level to sub-block-level provides additional degrees of freedom, enabling more candidate predictors to be generated without increasing the complexity of the basic motion model structure.
2Productivity
If more candidate predictors are used for affine motion modeling, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
Motion vectors for sub-blocks are determined in advance during the encoding process, and these pre-computed sub-block motion vectors are stored and reused to generate multiple candidate predictors. This preliminary computation avoids redundant calculations when generating different predictor candidates, reducing computational complexity while maintaining high compression efficiency.
Solution Approach 2:
The patent creates multiple candidate predictors by copying and combining motion information from sub-blocks. Instead of computing entirely new motion models, the system copies existing sub-block motion vectors and combines them to form affine motion predictors, significantly reducing computational complexity compared to deriving affine parameters from scratch.
Data Source
AI summary
Implementations are described for determining, for a block being encoded in a picture, at least one predictor candidate, determining for the at least one predictor candidate, one or more corresponding control point generator motion vectors, based on motion information associated to the at least one predictor candidate, determining for the block being encoded, one or more corresponding control point motion vectors, based on the one or more corresponding control point generator motion vectors determined for the at least one predictor candidate, determining, based on the one or more corresponding control point motion vectors determined for the block, a corresponding motion field, and encoding the block based on the corresponding motion field.