Sliding Window Context Derivation for Transform Coefficients
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Solution Overview
Problem
Existing video coding techniques face inefficiencies in determining context for transform coefficients due to the computational expense of position-based context support neighborhoods, which require determining relative locations and accessing extensive data, especially when implementing parallel processing and handling large blocks or sub-blocks.
Innovation Solution
The approach involves defining a context derivation neighborhood based on the transform coefficient scan order, using a sliding window that slides with the scan order, allowing context determination without needing to calculate relative locations and reducing data access, enabling efficient parallel context calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position-based context support neighborhood is used, then coding accuracy is improved, but computational complexity increases
Solution Approach 1:
The context support neighborhood is segmented into a fixed-size sliding window that processes only a limited number of neighboring coefficients. This segmentation approach maintains coding accuracy by focusing on the most relevant neighbors while reducing computational complexity through bounded processing scope.
Solution Approach 2:
The patent applies local quality by using a sliding window that adapts to the local characteristics of transform coefficients. The context derivation is performed locally within the window boundaries, allowing accurate context modeling for each coefficient based on its immediate neighbors without requiring global position calculations.
2Measurement precision
If position-based context support neighborhood is used, then coding accuracy is improved, but data access requirements increase
Solution Approach 1:
The sliding window segments the data access scope to a fixed, manageable size. Instead of accessing extensive data based on relative positions, the patent limits data access to coefficients within the sliding window boundaries, reducing memory access requirements while maintaining coding accuracy through focused local context analysis.
3Productivity
If parallel processing is implemented, then processing speed is improved, but context determination complexity increases
Solution Approach 1:
The sliding window approach segments the context determination process into independent, fixed-size units that can be processed in parallel. Each coefficient within the sliding window can have its context determined independently using the same windowing logic, enabling parallel processing without increasing overall system complexity.
Data Source
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AI summary
A video coding process that includes defining a context derivation neighborhood for one of a plurality of transform coefficients based on a transform coefficient scan order. The process also includes determining a context for the one of the plurality of transform coefficients based on the context derivation neighborhood. The process also includes coding the one of the plurality of transform coefficients based on the determined context.