CABAC Entropy Coding for Adaptive QT, BT, and TT Block Splitting
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images, such as HD and UHD images, leads to a significant increase in the amount of information to be transmitted and stored, resulting in higher transmission and storage costs.
Innovation Solution
The method involves determining a block structure and block split based on a quad tree (QT) structure, binary tree (BT) structure, and ternary tree (TT) structure during image compression, and using a context model for entropy encoding with context-based adaptive binary arithmetic coding (CABAC).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality images are transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The image is divided into multiple blocks that can be processed and encoded independently. Each block is further segmented into prediction units and transform units, allowing for efficient compression by processing only relevant portions of the image data.
Solution Approach 2:
Different encoding strategies are applied to different regions of the image based on local characteristics. Intra-prediction is used for regions with spatial redundancy, while inter-prediction is applied to regions with temporal redundancy, optimizing compression efficiency for each local area.
2Productivity
If CABAC entropy coding is performed without context model optimization, then encoding speed is maintained, but coding efficiency is reduced
Solution Approach 1:
The context model is made dynamic and adaptive, automatically adjusting probability estimates based on previously decoded symbols and syntax elements. This allows the encoder to adapt to local statistical properties of the data, improving compression efficiency without requiring manual configuration.
Solution Approach 2:
The CABAC encoder uses feedback from previously decoded symbols to update context probabilities. The decoded values of split flags and other syntax elements are fed back into the context model to refine probability estimates for subsequent encoding decisions, creating a self-improving encoding process.
3Productivity
If multiple tree structures (QT, BT, TT) are used for block splitting, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
A unified multi-tree structure is designed that can function as quadtree, binary tree, or ternary tree based on encoding needs. The same data structure and processing logic handle all three splitting modes, reducing the need for separate implementation paths and minimizing the increase in device complexity while maintaining compression efficiency benefits.
Data Source
AI summary
A picture decoding method performed by a decoding device according to an embodiment of the present disclosure comprises the steps of: deriving a context index for a split flag syntax element on the basis of split availability information of a current block; determining a context model on the basis of the derived context index; decoding a value of the split flag syntax element on the basis of a CABAC, using the determined context model; deriving a current coding unit from the current block on the basis of the value of the split flag syntax element; deriving a predicted block on the basis of inter prediction or intra prediction for the current coding unit; and generating a reconstructed block on the basis of the predicted block.