Video Decoding With CCP Merge for Chroma Block Prediction
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Solution Overview
Problem
Existing video coding technologies inadequately predict chroma blocks based on luma blocks due to insufficient derivation of prediction models from neighboring samples, requiring improved model refinement modes for precise chroma block reconstruction.
Innovation Solution
A method and device for predicting a block unit using a cross-component prediction (CCP) merge list, combining intra prediction modes with weighted prediction blocks, and selecting from various CCP merge candidates including spatial, non-spatial, historical, and temporal candidates to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If prediction models of other blocks are used to derive chroma prediction models, then the prediction process can be simplified, but the prediction accuracy deteriorates because the models are not derived from multiple neighboring samples
Solution Approach 1:
The patent segments the prediction process into two independent parts: (1) using prediction models from other blocks to establish a baseline prediction, and (2) refining this prediction by deriving additional models from multiple neighboring samples. This segmentation allows the system to maintain simplicity while improving accuracy through the refinement stage.
Solution Approach 2:
The patent applies preliminary action by first deriving prediction models from other blocks before incorporating neighboring sample data. This preliminary model derivation provides a foundation that can be subsequently refined, allowing the system to build accuracy incrementally rather than requiring all data to be processed simultaneously.
2Measurement precision
If multiple model refinement modes are implemented to improve chroma block prediction, then prediction accuracy improves, but device complexity increases
Solution Approach 1:
The patent implements dynamics by making the model refinement process adaptive rather than fixed. The system dynamically selects which refinement modes to apply based on the specific characteristics of the current block and available neighboring samples, allowing the complexity to vary according to actual needs rather than always operating at maximum complexity.
Solution Approach 2:
The patent changes parameters in the prediction model refinement process by adjusting model coefficients and selection criteria based on neighboring sample characteristics. This parameter adjustment allows the system to optimize accuracy for different scenarios without requiring completely different prediction mechanisms, thereby managing complexity through continuous optimization rather than discrete mode switching.
Data Source
AI summary
A method of decoding video data performed by an electronic device is provided. The method receives the video data and determines a block unit from a current frame included in the video data. The method further determines an intra prediction mode from a plurality of intra default modes, determine a cross-component prediction (CCP) merge list of the block unit including a plurality of CCP merge candidates; and selecting one of the CCP merge candidates for the block unit to determine a prediction model of the selected CCP merge candidate. The method then predicts the block unit using the prediction model of the selected CCP merge candidate to generate a first prediction block, predicts the block unit based on the intra prediction mode to generate a second prediction block, and reconstructs the block unit based on the first prediction block and the second prediction block.