Picture Decoding Candidate Selection for Lower Inter Prediction Load
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Solution Overview
Problem
Existing picture coding technologies, such as those using affine transforms for inter prediction, face high processing loads due to complex computations.
Innovation Solution
A picture decoding device and method that utilizes spatial and history-based candidate derivation units to manage candidate lists efficiently, allowing for inter prediction with reduced processing load by optimizing candidate selection and list management based on prediction modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If affine transform is applied for inter prediction to handle object deformation, then coding efficiency is improved, but processing load becomes great
Solution Approach 1:
The patent segments the motion prediction process into multiple candidate lists (first candidate list with spatial candidates, second candidate list with history-based candidates, and third candidate list with template-based candidates). Instead of applying a single complex affine transform, the system divides the prediction into discrete candidate options, each representing a simpler transformation model, thereby reducing processing load while maintaining coding efficiency.
Solution Approach 2:
The patent dynamically selects the appropriate candidate list and prediction mode based on the specific block characteristics and context. The system adapts between different prediction approaches (spatial, history-based, template-based) depending on what is most suitable for each block, optimizing the balance between coding efficiency and processing complexity for each specific case.
2Measurement precision
If multiple candidate lists are maintained for inter prediction, then prediction accuracy is improved, but candidate management complexity increases
Solution Approach 1:
The patent segments candidate management into three distinct lists with specific purposes: first candidate list for spatial predictions, second candidate list for history-based predictions, and third candidate list for template-based predictions. Each list has its own derivation rules and update mechanisms, which simplifies the management of multiple candidates by organizing them into structured groups rather than handling them as a single complex set.
Solution Approach 2:
The system automatically manages candidate list updates based on predefined rules. When a block is decoded, the appropriate candidate lists are automatically derived and updated without manual intervention. The history-based candidate list is updated using previously decoded block information, and spatial candidates are derived from neighboring blocks, allowing the system to self-manage candidate generation and selection.
Data Source
AI summary
A picture decoding device includes a spatial candidate derivation unit configured to derive a spatial candidate from inter prediction information of a block neighboring a decoding target block and register the derived spatial candidate as a candidate to a first candidate list, a history-based candidate derivation unit configured to generate a second candidate list by adding a history-based candidate included in a history-based candidate list as a candidate to the first candidate list, a candidate selection unit configured to select a selection candidate from candidates included in the second candidate list, and an inter prediction unit configured to perform inter prediction using the selection candidate.