Video Block Affine Candidate Regression for Coding Efficiency
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Solution Overview
Problem
Current video coding technologies face challenges in improving coding efficiency and effectiveness, particularly in determining affine candidates for video blocks during the conversion process between video blocks and bitstreams.
Innovation Solution
The proposed method involves determining motion information and location information of subblocks within a temporal or spatial block, and then applying a regression process to determine an affine candidate for the current video block based on this information, thereby enhancing coding efficiency and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video coding techniques are used, then the coding process is simpler, but coding efficiency is lower
Solution Approach 1:
The current video block is divided into multiple subblocks (e.g., 4×4 subblocks within a 16×16 block). Motion information is determined for each subblock independently, allowing finer-grained motion compensation and improving coding efficiency without requiring complex global motion models.
Solution Approach 2:
The patent changes the motion representation from single-block motion vectors to subblock-based motion information. By determining motion information for multiple subblocks and applying regression processes, the system achieves more accurate motion compensation while managing complexity through structured parameter organization.
2Measurement precision
If more motion information is used for affine candidate determination, then coding effectiveness improves, but computational complexity increases
Solution Approach 1:
Motion information is determined for multiple subblocks in advance before performing the regression process. This preliminary computation of subblock motion vectors allows the subsequent regression to focus only on combining these pre-computed values, improving affine candidate accuracy while managing computational load through staged processing.
Solution Approach 2:
The patent replaces complex mechanical motion modeling with a statistical regression approach. Instead of using sophisticated physics-based motion models, the system uses regression to derive affine candidates from subblock motion information, achieving high accuracy with reduced computational complexity.
Data Source
AI summary
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a current video block of a video and a bitstream of the video, motion information and location information of at least one subblock of a temporal block in a collocated frame of the current video block; determining an affine candidate of the current video block by applying a regression process to the current video block based on the motion information and the location information of the at least one subblock; and performing the conversion based on the affine candidate.