Inter-Component Prediction for Chroma Blocks in Video Encoding
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Solution Overview
Problem
The challenge is to enhance the encoding efficiency of video signals, particularly for high-resolution and ultra-high-resolution images, by improving inter-component prediction methods in image encoding and decoding, especially for chroma component blocks, while reducing the need for index information transmission and minimizing visual artifacts.
Innovation Solution
The method involves determining an inter-component prediction mode for chroma component blocks based on an inter-component prediction mode list and predetermined index information, deriving a linear prediction model using suitable reference samples, and performing downsampling to improve prediction accuracy, thereby reducing signaling bits and visual artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inter-component prediction is performed for chroma component blocks using conventional methods, then prediction accuracy is improved, but encoding efficiency is insufficient and requires more signaling bits
Solution Approach 1:
The patent changes the parameters of the linear prediction model by selecting appropriate reference samples from luma component blocks and adjusting prediction coefficients. This allows the system to achieve high prediction accuracy for chroma component blocks while maintaining efficient encoding by adapting the prediction model to specific block characteristics and content types.
Solution Approach 2:
The patent applies different prediction strategies to different chroma component blocks based on their specific characteristics. By analyzing the local properties of each block (such as its position, size, and content), the system selects the most suitable reference samples and prediction parameters, thereby improving prediction accuracy without requiring uniform high-complexity processing across all blocks.
2Adaptability or versatility
If multiple inter-component prediction modes are transmitted with index information, then prediction flexibility is improved, but signaling overhead increases
Solution Approach 1:
Instead of transmitting complete index information for all possible inter-component prediction modes, the patent uses a selective approach where only necessary mode information is signaled. The system pre-defines a set of prediction modes and uses compact indexing or implicit derivation methods that provide sufficient flexibility without requiring transmission of all mode parameters, thereby reducing signaling overhead.
Solution Approach 2:
The decoding system derives the appropriate inter-component prediction mode autonomously based on the received index information and pre-stored prediction mode lists, without requiring explicit transmission of all mode parameters. This self-derivation mechanism reduces the information loss in signaling while maintaining prediction flexibility.
3Speed
If reference samples are used without downsampling, then prediction speed is improved, but prediction accuracy decreases due to mismatched resolution
Solution Approach 1:
The patent applies downsampling operations to reference samples to adjust their resolution to match the chroma component block size. By changing the spatial parameters of the reference samples through controlled downsampling, the system achieves accurate prediction while maintaining efficient processing speeds. The downsampling is performed selectively based on the specific prediction requirements and block characteristics.
Data Source
AI summary
An image encoding and decoding method comprises the steps of: determining an inter-component prediction mode of a chrominance component block on the basis of an inter-component prediction mode list and predetermined index information; determining a reference sample for inter-component prediction of the chrominance component block on the basis of the determined inter-component prediction mode; deriving the parameters of a linear prediction model by using the reference sample; and performing inter-component prediction for the chrominance component block by using the parameters of the linear prediction model.