Image Reconstruction with Adaptive Prediction Granularity
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Solution Overview
Problem
The existing image coding methods face inefficiencies in prediction due to the use of a unified prediction method that does not adapt to varying content within coding units, leading to low prediction efficiency.
Innovation Solution
The method involves acquiring a quantization parameter and prediction mode for a current coding block, adjusting the prediction granularity accordingly, and performing prediction based on the adjusted granularity to obtain a prediction sample block, followed by reconstructing the sample block using the quantization parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a unified prediction method is used for the entire coding unit, then the hardware implementation is simple and the processing is efficient, but the prediction accuracy deteriorates when different contents exist in the coding unit
Solution Approach 1:
The coding unit is divided into multiple prediction units based on content characteristics. Each prediction unit can be processed with appropriate prediction methods, allowing the system to handle different contents within the coding unit separately, thereby improving prediction accuracy while maintaining reasonable complexity
Solution Approach 2:
Different prediction methods or granularities are applied to different regions within the coding unit based on local content characteristics. This allows the prediction process to adapt to local variations in the image data, improving overall prediction accuracy without requiring complex global processing
2Productivity
If the prediction granularity is fixed, then the processing speed is high and the hardware complexity is low, but the prediction efficiency deteriorates when adapting to different content regions
Solution Approach 1:
The prediction granularity is made dynamic rather than fixed. The system can adjust the prediction granularity based on the content characteristics of different regions, allowing faster processing for simple regions and more detailed processing for complex regions, thus balancing speed and adaptability
Solution Approach 2:
The prediction granularity parameter is changed adaptively based on content analysis. By modifying this parameter according to the actual content requirements, the system achieves both high processing speed for simple cases and high prediction efficiency for complex cases
Data Source
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AI summary
The present application belongs to the technical field of image coding and discloses an image reconstruction method and apparatus, device, and storage medium. The image reconstruction method includes: acquiring a quantization parameter and a prediction mode corresponding to a current coding block; adjusting a prediction granularity corresponding to the prediction mode; predicting according to the adjusted prediction granularity to obtain a prediction sample block; obtaining a reconstructed sample block based on the prediction sample block and the quantization parameter.