Cross-Component Prediction Model Ranking for Lower Video Bitstream Overhead
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Solution Overview
Problem
Existing digital video compression technologies face challenges in reducing bandwidth and traffic pressure due to high bitstream overhead in digital video transmissions, particularly in handling cross-component prediction models.
Innovation Solution
The proposed solution involves sorting candidate cross-component prediction models in ascending order of prediction errors to select the most efficient model for intra prediction, thereby reducing the bitstream overhead by prioritizing models with smaller prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If candidate cross-component prediction models are transmitted with their original indices, then the decoder can identify the selected model, but the bitstream overhead increases bandwidth and traffic pressure
Solution Approach 1:
The encoder performs preliminary sorting of candidate cross-component prediction models according to prediction error before transmission. By pre-arranging models in ascending order of prediction error and transmitting only the index of the selected model, the decoder can automatically retrieve the correct model without requiring additional overhead for error information or complex signaling
Solution Approach 2:
The patent changes the parameter representation by using a simplified index value instead of transmitting complete model parameters or complex identification data. The sorting reorders the parameter sequence, allowing the index to directly correspond to the optimal model based on prediction error
2Measurement precision
If multiple candidate cross-component prediction models are considered, then prediction accuracy improves, but the complexity of model selection and processing increases
Solution Approach 1:
The encoder performs preliminary sorting of candidate cross-component prediction models according to prediction error before transmission. By pre-arranging models in ascending order of prediction error, the most accurate models are positioned at the beginning of the candidate list, allowing the decoder to efficiently select optimal models without complex evaluation processes
Solution Approach 2:
The sorting mechanism enables the system to automatically identify and prioritize the best prediction models based on their inherent prediction error characteristics. The models self-organize according to their performance, eliminating the need for external complex selection algorithms at the decoder side
Data Source
AI summary
Provided are codec methods and apparatuses, codec, bitstream, device, storage medium. The encoding method includes: determining a first candidate list, sorting candidate cross-component prediction models therein in ascending order of prediction errors of the models or a sum of prediction errors within a model group to which the models belong; selecting from the first candidate list a first cross-component prediction model of a first color component, performing intra prediction on the first color component of a current block according to the first cross-component prediction model and a reconstruction value of a second color component of the current block to obtain a first intra predicted value; determining a first residual value of the first color component according to the first intra predicted value and a sample value of the first color component; generating a bitstream according to the first residual value and an index value of the first cross-component prediction model.