Arithmetic Entropy Coding with Parallel Context Normalization
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Solution Overview
Problem
Traditional arithmetic coding methods, such as those used in JPEG 2000, require high and complex hardware resources and have low processing efficiency, making them undesirable for efficient image coding.
Innovation Solution
A method and system for arithmetic entropy coding that updates and normalizes probability interval values and lower limits for context-decision pairs in parallel, using lookup tables to determine coding parameters and perform shift processing to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional arithmetic coding method is used, then coding accuracy is maintained, but hardware resource consumption increases and processing efficiency decreases
Solution Approach 1:
The patent divides the arithmetic coding process into separate modules: probability interval value updating module, shift processing module, probability interval lower limit updating module, and normalization module. Each module handles specific operations independently, allowing parallel processing of multiple contexts while maintaining coding accuracy and reducing hardware resource conflicts.
Solution Approach 2:
The patent introduces parallel processing dimension by simultaneously updating probability interval values and lower limits for multiple context-decision pairs in parallel. This dimensional change from sequential to parallel operations significantly improves processing efficiency without proportionally increasing hardware complexity.
2Loss of time
If traditional arithmetic coding method is used, then coding precision is maintained, but processing time increases
Solution Approach 1:
The patent performs shift processing on probability interval values before updating probability interval lower limits. This preliminary action prepares the data in advance, enabling faster subsequent operations and reducing overall processing time while maintaining the precision required for accurate arithmetic coding.
Solution Approach 2:
The patent implements continuous parallel updating of probability interval values and lower limits across multiple clock cycles. By maintaining continuous useful action through parallel processing pipelines, the system reduces total processing time without sacrificing coding precision through the systematic normalization process.
Data Source
AI summary
The present disclosure relates to systems and methods for coding. The methods may include receiving at least two contexts, for each of the at least two contexts, obtaining at least one coding parameter corresponding to the context from at least one lookup table, determining a probability interval value corresponding to the context based on a previous probability interval value and the at least one coding parameter, determining a normalized probability interval value corresponding to the context by performing a normalization operation on the probability interval value, determining a probability interval lower limit corresponding to the context based on a previous probability interval lower limit and the at least one coding parameter, determining a normalized probability interval lower limit corresponding to the context by performing the normalization operation on the probability interval lower limit, and outputting at least one byte based on the normalized probability interval lower limit.