Weighted Bi-Prediction Decoding for High-Resolution Video Compression
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Solution Overview
Problem
The increasing demand for high-resolution, high-quality images and immersive media formats such as VR and AR has led to higher data transmission and storage costs due to the increased amount of information required, necessitating a more efficient image/video compression technique.
Innovation Solution
The method involves deriving weight index information for bi-prediction and affine merge candidates, using control points from neighboring blocks to generate prediction samples through weighted averaging, enhancing image coding efficiency and compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution and high quality image/video are transmitted and stored, then image quality is improved, but transmission and storage costs are increased
Solution Approach 1:
The image block is divided into multiple sub-blocks, and each sub-block is processed independently with its own motion vector and prediction. This segmentation allows for more precise local compression while maintaining overall image quality, reducing the total data amount needed for high-quality transmission
Solution Approach 2:
Different weighting factors are applied to different sub-blocks based on their local characteristics and motion patterns. This local quality approach ensures that important regions maintain high quality while less critical regions use more aggressive compression, optimizing the balance between image quality and data reduction
2Quantity of substance
If conventional compression techniques are used, then transmission costs are reduced, but compression efficiency is insufficient for high resolution images
Solution Approach 1:
The patent employs dynamic motion vector prediction where motion vectors are derived from neighboring blocks and refined based on local motion characteristics. This dynamic adaptation allows the compression technique to respond to varying motion patterns in different regions, significantly improving compression efficiency for high-resolution content with complex motion
Solution Approach 2:
The patent changes multiple parameters including motion vector precision, prediction block sizes, and weighting factors to optimize compression for different content types. By dynamically adjusting these parameters based on scene complexity and motion characteristics, the system achieves higher compression efficiency while maintaining quality
3Device complexity
If simple averaging is used for bi-prediction, then computation is simplified, but prediction accuracy is reduced
Solution Approach 1:
Different weighting factors are applied to different sub-blocks based on their local motion characteristics and reliability. This local quality approach maintains computational simplicity by using basic weighted averaging while improving prediction accuracy by adapting weights to local conditions, such as giving higher weight to sub-blocks with more reliable motion vectors
Data Source
AI summary
According to the disclosure of the present document, weight index information for sub-block merge candidates of a current block can be derived and coding efficiency can be increased.