Ranked Reference Framework Video Coding Context Tree
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Solution Overview
Problem
Existing video encoding and decoding technologies face inefficiencies in managing reference frames, particularly due to label-based identification methods that do not accurately reflect the actual reference frame qualities, leading to suboptimal coding efficiency and increased complexity.
Innovation Solution
A ranked reference framework is introduced, where a list of available reference frames is ranked based on heuristic scores calculated using characteristics such as temporal distance, quality level, and usage history, allowing for more accurate selection of reference frames for inter-prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If label-based identification methods are used for reference frames, then the implementation is simpler, but the coding efficiency deteriorates because labels do not accurately reflect actual reference frame qualities
Solution Approach 1:
The patent changes the identification parameter from simple labels to ranked positions based on quality scores. Reference frames are assigned ranks according to their calculated quality metrics (temporal distance, quality level, usage history), allowing the system to select the most suitable reference frames while maintaining a straightforward ranking-based selection process that balances simplicity with efficiency.
Solution Approach 2:
The patent replaces the label-based identification mechanism with a score-based ranking mechanism. Instead of using arbitrary labels that don't reflect quality, the system calculates heuristic scores for each reference frame and ranks them accordingly, substituting the mechanical label assignment with a more intelligent scoring and ranking system that improves coding efficiency.
2Productivity
If more reference frames are considered for inter-prediction, then the coding efficiency improves, but the signaling complexity and device complexity increase
Solution Approach 1:
The patent segments the reference frame selection process into a hierarchical context tree structure. Instead of considering all reference frames simultaneously, the system organizes them in a tree where nodes represent decision points for selecting reference frames at different levels of detail. This segmentation allows efficient exploration of reference frame options without overwhelming complexity.
Solution Approach 2:
The patent introduces dynamic ranking of reference frames based on calculated scores that reflect temporal distance, quality level, and usage history. The context tree structure adapts to different coding scenarios by dynamically determining which reference frames to consider at each node, allowing the system to optimize for coding efficiency while managing complexity through adaptive, rather than static, reference frame selection.
3Loss of information
If reference frames are selected based on arbitrary labels, then the signaling is simpler, but the selection accuracy deteriorates leading to suboptimal inter-prediction
Solution Approach 1:
The patent replaces arbitrary label-based selection with a score-based ranking system that calculates heuristic scores for each reference frame. The scoring mechanism considers multiple factors including temporal distance, quality level, and usage history, providing accurate reflection of reference frame suitability. This substitution maintains reasonable signaling complexity while dramatically improving selection accuracy and reducing information loss.
Solution Approach 2:
The patent incorporates feedback mechanisms where the usage history of reference frames influences their ranking scores. Reference frames that have proven useful in previous inter-prediction operations receive boosted scores, creating a feedback loop that continuously improves selection accuracy. This feedback-based ranking system ensures that the most effective reference frames are prioritized while maintaining efficient signaling.
Data Source
AI summary
A new reference framework is described that ranks reference frames based on a normative procedure (e.g., a calculated score) and signals the reference frames based on their ranks. The bitstream syntax is simplified by using a context tree that relies on the ranking. Moreover, mapping reference frames to buffers does not have to be signaled and can be determined at the decoder. In an example, the identifier of a reference frame used to code a current block can include identifying a syntax element corresponding to the identifier, determining context information for the syntax element, determining a node of a context tree that includes the syntax element, and coding the syntax element according to a probability model using the context information associated with the node. The context tree is a binary tree that includes, as nodes, the available reference frames arranged in the ranking.