Picture Block Prediction Using Merged Non-Translational Motion
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Solution Overview
Problem
Conventional video coding/decoding solutions based on translational motion models face high computational complexity due to the assumption of uniform object motion, which does not accurately represent real-world irregular motions like scaling, rotation, or parabolic motion.
Innovation Solution
A picture prediction method that selects a merged motion information unit set from candidate units across multiple pixel samples using a non-translational motion model, reducing computational complexity by merging motion information units that meet specific conditions, such as similar motion modes or reference frames, to predict pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a non-translational motion model is used to accurately describe irregular motion, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the picture block into multiple pixel samples (K1 pixel samples) and processes motion information for each sample separately. By segmenting the motion estimation into discrete pixel sample units, the system can apply non-translational motion models to specific regions while managing overall computational complexity through localized processing.
Solution Approach 2:
The patent determines a merged motion information unit set including K1 motion information units, where each unit is selected from candidate units corresponding to different pixel samples. This partial action approach processes only the necessary motion information units rather than all possible combinations, reducing computational complexity while maintaining prediction accuracy through selective processing of key motion data.
2Measurement precision
If motion information from multiple pixel samples is merged to improve prediction accuracy, then prediction quality is improved, but processing complexity increases
Solution Approach 1:
The patent merges motion information units from multiple pixel samples into a merged motion information unit set. This merging process combines motion data from different spatial locations and temporal references to create a comprehensive motion description, improving prediction accuracy by capturing complex motion patterns that single-sample approaches cannot represent.
Solution Approach 2:
The merged motion information unit set serves multiple functions: it provides motion compensation for the current picture block, enables prediction for multiple pixel samples simultaneously, and can be adapted to different motion models (translational and non-translational). This multi-functionality reduces processing complexity by consolidating multiple motion estimation operations into a unified framework.
Data Source
AI summary
A picture prediction method and a related apparatus are disclosed. A picture prediction method includes: determining K1 pixel samples in a picture block x, and determining a candidate motion information unit set corresponding to each pixel sample in the K1 pixel samples, where the candidate motion information unit set corresponding to each pixel sample includes at least one candidate motion information unit; determining a merged motion information unit set i including K1 motion information units, where each motion information unit in the merged motion information unit set i is selected from at least a part of motion information units in candidate motion information unit sets corresponding to different pixel samples in the K1 pixel samples; and predicting a pixel value of the picture block x by using a non-translational motion model and the merged motion information unit set i.