Arithmetic Image Coding with Hierarchical Context Probability Modeling
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Solution Overview
Problem
Conventional arithmetic coding methods face difficulties in accurately classifying image types, leading to reduced coding efficiency due to either overly fine or broad classification, which affects the prediction of symbol occurrence probabilities.
Innovation Solution
The proposed method employs a hierarchical context classification structure that uses both high-order and low-order contexts to calculate coding probability information, updating both to improve prediction accuracy and coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine classification of image types is performed to improve coding efficiency, then prediction accuracy of symbol occurrence probabilities improves, but device complexity increases due to multiple context classifications
Solution Approach 1:
The patent segments the context classification into two distinct parts: high-order context (broad image type classification) and low-order context (detailed signal type classification). This segmentation allows the system to maintain both broad statistical patterns and fine-grained predictions without creating an overly complex unified classification structure, thereby resolving the contradiction between prediction accuracy and device complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the context classification by organizing contexts into multiple levels (high-order and low-order). This dimensional organization allows the system to manage complex classifications systematically, improving prediction accuracy while keeping the device structure manageable through hierarchical arrangement rather than flat complexity.
2Device complexity
If broad classification of image types is used to reduce device complexity, then device complexity decreases, but coding efficiency deteriorates due to reduced prediction accuracy
Solution Approach 1:
By segmenting the classification into high-order and low-order contexts, the patent enables the system to use simple broad classification for general patterns while maintaining detailed classification capabilities for specific cases, thus preserving coding efficiency without requiring uniformly complex classification throughout the entire system.
Solution Approach 2:
The patent applies local quality by using detailed low-order context classification only where needed (for specific signal types requiring fine-grained prediction) while using broader high-order context for general cases. This localized application of fine classification maintains coding efficiency for critical cases while keeping overall device complexity manageable.
3Ease of operation
If only single context classification is used to simplify the system, then ease of operation improves, but coding efficiency deteriorates due to inability to capture both broad and detailed statistical information
Solution Approach 1:
The patent segments the probability calculation into two distinct stages: first calculating high-order probability from broad context, then calculating low-order probability from detailed context. This segmentation allows each stage to operate with relative simplicity while the combination of both stages achieves high coding efficiency, thus resolving the contradiction between operational simplicity and performance.
Solution Approach 2:
The patent introduces an intermediary calculation step where high-order probability serves as a foundation that is then refined by low-order probability. This intermediary approach allows the system to build complex predictions from simpler components, maintaining ease of operation at each stage while achieving high overall coding efficiency through the combination.
Data Source
AI summary
An image coding method for performing compression-coding on image data includes: binarizing a signal to be coded of the image data, to generate a binary signal; determining a low-order context which is a context associated with a type of the signal to be coded, and a high-order context that is common to the low-order context and a context associated with another type; calculating coding probability information to be used in arithmetic coding of the binary signal, using (i) high-order probability information corresponding to the determined high-order context and (ii) low-order probability information corresponding to the determined low-order context; performing the arithmetic coding on the binary signal using the coding probability information; and updating the high-order probability information and the low-order probability information based on the binary signal.