Context Model Selection for Video Encoding Efficiency
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently processing increasing amounts of digital video data, particularly in selecting appropriate context models for prediction modes applied to neighboring blocks.
Innovation Solution
An encoder and decoder that determine the availability of neighboring blocks and select context models based on the prediction modes applied to these blocks, allowing for the selection of more appropriate context models than conventional systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional context model selection is used, then device complexity is reduced, but encoding efficiency deteriorates due to inability to select appropriate context models for different prediction modes
Solution Approach 1:
The patent applies local quality by selecting different context models based on the specific prediction mode (intra or inter) and availability of neighboring blocks. Instead of using a uniform context model for all blocks, the system adapts the context model selection to local characteristics of each block's prediction mode and neighboring block configuration, thereby improving encoding efficiency without excessive complexity increase
Solution Approach 2:
The patent implements dynamics by making context model selection adaptive and conditional rather than static. The context model is dynamically selected based on runtime conditions including whether neighboring blocks are available and what prediction mode is applied, allowing the system to respond to varying encoding scenarios and improve overall efficiency
2Manufacturing precision
If context model selection is based on neighboring block availability and prediction mode, then encoding precision is improved, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by determining the availability of neighboring blocks and identifying the prediction mode before proceeding with context model selection. This pre-assessment allows the system to prepare the appropriate context model in advance, improving encoding precision while managing processing complexity through structured conditional logic
Solution Approach 2:
The patent utilizes parameter changes by varying the context model selection based on changes in neighboring block availability status and prediction mode type. When these parameters change, the system adjusts the selected context model accordingly, enabling precise encoding adaptation to different block characteristics without requiring complete reprocessing
Data Source
AI summary
An encoder includes circuitry and memory coupled to the circuitry. The circuitry, in operation, determines whether a first block is available and whether a second block is available, the first block and the second block being defined relative to a current block to be processed; selects a context model based on whether the first block is available, whether the second block is available, which of inter prediction and intra prediction is to be applied to the first block, and which of inter prediction and intra prediction is to be applied to the second block; and encodes, using the context model selected, a parameter indicating which of intra prediction and inter prediction is to be applied to the current block.