Affine Prediction Sub-Block Gradient For Video Coding
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Solution Overview
Problem
Current video coding technologies face challenges in efficiently processing and compressing digital video data, particularly in handling motion compensation and prediction techniques, which impact bandwidth usage and encoding efficiency.
Innovation Solution
The proposed solution involves advanced video processing methods, including gradient-based prediction, optical flow refinement, and affine motion compensation, which determine the application of coding tools based on specific conditions such as motion vectors, block sizes, and prediction modes to optimize video encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If affine prediction with optical flow refinement is applied to improve motion compensation accuracy, then prediction precision is improved, but computational complexity increases
Solution Approach 1:
The video block is divided into multiple sub-blocks, and affine prediction is applied independently to each sub-block. This segmentation allows the complex optical flow refinement to be distributed across smaller regions, improving local motion compensation accuracy while managing computational complexity through localized processing
Solution Approach 2:
Different prediction methods are applied to different regions based on local characteristics. Optical flow refinement is selectively applied to sub-blocks where it provides the most benefit, while simpler methods are used elsewhere, optimizing the balance between accuracy and complexity
2Loss of energy
If advanced prediction techniques are used to reduce bandwidth usage, then data transmission efficiency is improved, but encoding complexity increases
Solution Approach 1:
Motion compensation and prediction are performed in advance during the encoding phase using advanced techniques like affine transformation and optical flow. This preliminary processing reduces the amount of data that needs to be transmitted, achieving bandwidth savings while concentrating complexity in the encoding stage
Solution Approach 2:
Prediction models are created and transmitted instead of actual video data. By copying motion patterns and prediction parameters rather than full frame data, bandwidth is significantly reduced while maintaining reconstruction quality
3Measurement precision
If sub-block level gradient calculation is applied to all samples, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The video block is divided into multiple sub-blocks, and gradient calculation is performed at the sub-block level rather than for the entire block. This segmentation reduces the computational burden per processing unit while maintaining high prediction accuracy through localized gradient analysis
Solution Approach 2:
Gradient calculation is applied selectively to sub-blocks rather than uniformly to all samples in the entire video block. This partial application of the computationally intensive operation reduces processing time while maintaining accuracy in critical regions
Data Source
AI summary
Examples of video encoding methods and apparatus and video decoding methods and apparatus are described. An example method of processing video data includes determining, for a conversion between a video block of a video and a bitstream of the video, a gradient of a prediction vector at a sub-block level for the video block according to a rule, wherein the rule specifies to use a same gradient value is assigned for all samples within a sub-block of the video block; and performing the conversion based on the determining.