CABAC-Based Entropy Coding with QT-BT-TT Block Splits
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Solution Overview
Problem
Existing image compression techniques face challenges in efficiently encoding high-resolution and high-quality images, leading to increased transmission and storage costs due to the large amount of information required.
Innovation Solution
The method involves splitting a region into a quad tree (QT) structure and using a context model during Context-Based Adaptive Binary Arithmetic Coding (CABAC) based on binary tree (BT) and ternary tree (TT) structures to enhance encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement 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 are further segmented into coding units using quadtree, binary tree, and ternary tree structures. This hierarchical segmentation allows selective processing and coding of different regions based on their characteristics, reducing the overall data volume while preserving quality.
Solution Approach 2:
Different coding strategies are applied to different regions of the image based on their complexity and characteristics. The context model adapts to local patterns, allowing more efficient coding in regions with similar characteristics and reducing redundancy in homogeneous areas.
2Productivity
If CABAC entropy coding is performed without context model adaptation, then coding speed is improved, but coding efficiency deteriorates
Solution Approach 1:
The context model is dynamically adapted during the coding process based on the actual image data and block structures. The context index is derived from block split availability information, allowing the coding system to adjust its probability models in real-time to match the specific characteristics of each region.
Solution Approach 2:
The context model uses feedback from previously coded blocks and the derived context index to refine its probability estimates for current blocks. This feedback mechanism allows the system to learn from past coding patterns and improve efficiency in subsequent coding operations.
3Device complexity
If block splitting is performed using only simple tree structures, then device complexity is reduced, but coding precision deteriorates
Solution Approach 1:
The coding system employs nested tree structures where quadtree, binary tree, and ternary tree structures are combined in a hierarchical manner. Smaller tree structures are nested within larger ones, allowing complex block partitions to be represented through a unified framework that maintains manageable complexity.
Solution Approach 2:
The system changes the parameters of the tree structures dynamically based on the image data. The derivation of context index from block split availability information allows the system to adjust the complexity and configuration of tree structures adaptively, using more complex structures only when necessary to achieve optimal coding precision.
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.