Inter Prediction Mode Image Processing for Non-Linear Motion
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Solution Overview
Problem
Existing methods for video image compression, such as the Bi-directional Optical Flow (BIO) method, assume linear motion of objects, which restricts the representation of non-linear motion and degrades prediction accuracy.
Innovation Solution
A method that generates bi-directional predictors for each pixel by deriving motion vectors from specific window areas in reference pictures and performing a weighted sum of predictors to accurately represent both linear and non-linear object motions, using gradient components and interpolation filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear motion assumption is used in BIO method, then device complexity is reduced, but prediction accuracy deteriorates for non-linear motions
Solution Approach 1:
The current block is divided into multiple sub-blocks, and separate motion vectors are derived for each sub-block. This segmentation allows the system to handle non-linear motions by applying different linear motion models to different regions, thereby improving prediction accuracy while maintaining computational efficiency.
Solution Approach 2:
Different motion characteristics are applied to different regions of the current block. By deriving motion vectors locally for each sub-block rather than using a single global motion model, the system adapts to local motion variations and accurately represents non-linear motions in specific areas.
2Measurement precision
If non-linear motion representation is implemented, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The current block is divided into multiple sub-blocks, and separate motion vectors are derived for each sub-block. This segmentation allows the system to handle non-linear motions by applying different linear motion models to different regions, thereby improving prediction accuracy while maintaining computational efficiency.
Solution Approach 2:
Instead of applying a full non-linear motion model to the entire block, the system applies linear motion models partially to each sub-block. This partial action approach achieves sufficient prediction accuracy for non-linear motions without the computational complexity of a complete non-linear model.
3Measurement precision
If additional motion vectors are transmitted, then non-linear motion representation improves, but compression performance deteriorates
Solution Approach 1:
The current block is divided into multiple sub-blocks, and separate motion vectors are derived for each sub-block. This segmentation allows the system to handle non-linear motions by applying different linear motion models to different regions, thereby improving prediction accuracy while maintaining computational efficiency.
Data Source
AI summary
Disclosed is a method for processing an image based on an inter prediction and an apparatus for the same. Particularly, the method may include generating a bi-directional predictor of each pixel of a current block by performing a bi-directional inter prediction based on a motion vector of the current block; deriving a first motion vector from a window area of a specific size in a first reference picture for a current pixel, and deriving a second motion vector from a window area of a specific size in a second reference picture for the current pixel; deriving a first predictor for the current pixel using the first motion vector, and deriving a second predictor for the current pixel using the second motion vector; and generating a third predictor of each pixel of the current block by performing a weighted sum of the first predictor and the second predictor.