Separable Motion Vector Predictor Components
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently compressing video with complex spatial and temporal characteristics, as they often rely on non-separable motion vector predictors that fail to optimize compression efficiency in intricate motion patterns.
Innovation Solution
The approach involves separately selecting optimal predictors for the horizontal and vertical components of motion vectors, allowing for more accurate prediction and reducing residual vectors, thereby enhancing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If non-separable motion vector predictors are used, then the prediction process is simple, but compression efficiency deteriorates for complex motion patterns
Solution Approach 1:
The motion vector prediction process is segmented into separate horizontal and vertical component predictions. Instead of using a single non-separable predictor, the system independently predicts horizontal motion components and vertical motion components using separate predictor candidates, allowing each component to be optimized for its specific motion characteristics.
Solution Approach 2:
Different predictor candidates are selected for horizontal and vertical components based on their local motion characteristics. The system evaluates and selects optimal predictors separately for each component, allowing the prediction to adapt to local motion patterns in the video data rather than using a uniform prediction approach.
2Productivity
If separate motion vector predictor components are used, then compression efficiency improves, but prediction process complexity increases
Solution Approach 1:
The prediction process is divided into independent horizontal and vertical component processing stages. Each component has its own predictor candidates and selection mechanism, which simplifies the overall complexity management by breaking down the prediction task into manageable, independent parts rather than handling the full motion vector as a single complex unit.
3Device complexity
If a single motion vector predictor is used for both components, then the system is simple, but residual vectors increase leading to higher bit rates
Solution Approach 1:
The prediction process segments the motion vector into independent horizontal and vertical components, each predicted using dedicated predictor candidates. This segmentation allows for more accurate prediction of each component, reducing the residual error and resulting in smaller residual vectors that require fewer bits to encode.
Solution Approach 2:
The system applies local quality by selecting optimal predictors specifically tailored to the motion characteristics of each component. Horizontal and vertical components are predicted using their respective optimal predictors rather than a single universal predictor, improving prediction accuracy and reducing information loss in the residual vectors.
Data Source
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AI summary
In the process of block-prediction in block-based video compression, a vector for translating a reference block to produce a predicted block is not encoded directly in an encoded bitstream. Rather, a residual vector, which is the difference between the vector and a selected vector predictor candidate, is encoded to achieve higher compression efficiency. A selected vector predictor candidate can have the smallest Euclidean distance to the vector to ensure the residual vector is small. For compressing video that has complex spatial and/or temporal characteristics, the selected vector predictor candidate may not result in the smallest residual vector. To address this concern, vector predictor candidates are selected separately for the horizontal component and the vertical component of the vector to obtain smaller residual vectors. An effective and efficient signaling scheme can be implemented to indicate whether the predictor is based on components from two different predictor candidates.