Chroma Prediction Model Selection for Video Decoding
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently encoding and decoding video sequences, particularly in managing the complexity of intra prediction modes and block partitioning strategies, which affect coding efficiency and computational resources.
Innovation Solution
The proposed solution involves a novel approach to chroma prediction that selects between linear and non-linear models based on coding parameters, such as block size and quantization parameters, to optimize chroma prediction efficiency while balancing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-linear prediction models are used for chroma prediction, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent implements dynamic model selection that adapts between linear and non-linear prediction models based on coding parameters such as block size and quantization parameters. This dynamic approach allows the system to use complex non-linear models only when necessary (for larger blocks or specific quantization scenarios) while using simpler linear models for other cases, thereby resolving the contradiction between prediction accuracy and computational complexity
Solution Approach 2:
The patent changes the operational parameters of the prediction system by selecting different models based on coding parameters. The decision rule evaluates parameters like block size and quantization parameters to determine whether to apply linear or non-linear modeling, allowing the system to optimize the balance between accuracy and complexity for different video content characteristics
2Adaptability or versatility
If multiple prediction models are maintained for different scenarios, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent uses parameter-based decision rules that evaluate coding parameters (block size, quantization parameters) to select appropriate models. This parameter-driven approach provides adaptability across different video scenarios while maintaining manageable complexity through systematic parameter evaluation rather than arbitrary model selection
Solution Approach 2:
The patent applies different prediction models to different local scenarios based on their specific characteristics. By evaluating local coding parameters and applying the most suitable model for each case, the system achieves high adaptability while keeping the overall system complexity manageable through localized model application
Data Source
AI summary
A video decoder may determine, based on comparing a coding parameter associated with a chroma block with a threshold, one or more candidate models from a first type of models and a second type of models. The decoder may select a chroma prediction model from the one or more candidate models and generate a prediction of the chroma block based on reference signals of the chroma block and the chroma prediction model. The decoder may determine a reconstruction of the chroma block based on the prediction of the chroma block and a residual of the chroma block