Bi-Prediction Weight Index Derivation for Affine Merge Coding
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Solution Overview
Problem
The increasing demand for high resolution and high quality images/videos, such as 4K and 8K Ultra High Definition, along with the growth of virtual reality and augmented reality content, necessitates a highly efficient image/video compression technique to reduce transmission and storage costs while effectively handling diverse image/video characteristics.
Innovation Solution
The method involves deriving weight index information for generating prediction samples using affine merge candidates, including control point motion vectors, to enhance image coding efficiency during inter prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution and high quality image/video data are transmitted, then image quality and detail are improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by using affine motion models to transform motion vectors between different reference frames and pictures. This allows the system to adapt motion information to different temporal and spatial parameters, enabling efficient prediction of high-resolution images from lower-resolution references, thus improving image quality while reducing transmission and storage costs
Solution Approach 2:
The patent implements dynamics by dynamically selecting between different motion models (affine motion model vs. conventional motion model) based on the inter prediction mode. The system adapts its behavior based on the specific coding situation, using affine motion models when beneficial for bi-directional prediction and conventional models otherwise, optimizing the balance between compression efficiency and computational complexity
2Ease of operation
If conventional motion models are used for inter prediction, then processing is simpler, but prediction accuracy for bi-directional prediction is insufficient
Solution Approach 1:
The patent implements dynamics by dynamically selecting between different motion models (affine motion model vs. conventional motion model) based on the inter prediction mode. The system adapts its behavior based on the specific coding situation, using affine motion models when beneficial for bi-directional prediction and conventional models otherwise, optimizing the balance between compression efficiency and computational complexity
Solution Approach 2:
The patent applies parameter changes by using affine motion models to transform motion vectors between different reference frames and pictures. This allows the system to adapt motion information to different temporal and spatial parameters, enabling efficient prediction of high-resolution images from lower-resolution references, thus improving image quality while reducing transmission and storage costs
3Measurement precision
If weight index information is derived for each candidate in merge candidate list, then prediction accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by deriving weight index information selectively based on the specific candidate type in the merge candidate list. Different weight index derivation methods are applied to different candidates (e.g., using motion vector differences for affine merge candidates vs. using reference picture indices for temporal merge candidates), optimizing prediction accuracy for each local case while avoiding unnecessary complexity for all candidates
Solution Approach 2:
The patent implements preliminary action by pre-defining the weight index derivation methods for different candidate types before the actual prediction process. The system has prepared multiple derivation approaches (using motion vector differences, using reference picture indices, using a combination) and selects the appropriate pre-defined method based on the candidate type, avoiding the need for complex real-time decision-making
Data Source
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AI summary
According to the disclosure of the present document, when the inter prediction type of a current block indicates bi-prediction, weight index information for candidates in a merge candidate list or a sub-block merge candidate list can be derived, and thus coding efficiency can be increased.