Adjustable Per-Symbol Entropy Coding Probability Updating
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Solution Overview
Problem
Existing image and video compression techniques face inefficiencies due to non-stationary symbol frequency variations in multimedia data, leading to reduced accuracy in entropy coding probabilities and increased bandwidth utilization.
Innovation Solution
Implementing adjustable per-symbol entropy coding probability updating, which allows for dynamic adaptation of entropy coding probabilities based on real-time data, enabling or disabling updates on a frame or block basis to optimize compression efficiency and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If per-symbol entropy coding probability updating is continuously performed, then coding efficiency is improved, but processor utilization increases and complexity rises
Solution Approach 1:
The patent implements dynamic probability updating where the entropy coding probabilities are continuously adapted based on the actual symbol frequencies observed in the data being coded. This allows the system to transition from static to dynamic probability models, improving coding efficiency while managing complexity through adaptive control mechanisms that update probabilities only when beneficial.
Solution Approach 2:
The system changes the probability parameters of the entropy coder based on observed symbol frequencies. By monitoring actual symbol occurrences and adjusting probability estimates accordingly, the system optimizes compression ratios without requiring excessive computational resources, balancing coding efficiency with processor utilization.
2Productivity
If per-symbol entropy coding probability updating is performed, then compression efficiency is improved, but bandwidth utilization increases due to transmitting probability update information
Solution Approach 1:
The patent applies probability updating selectively to different portions of the data rather than uniformly across all data. By identifying regions where symbol frequency variations are significant and applying updated probabilities only in those local regions, the system improves compression efficiency while minimizing the overhead of transmitting probability update information.
Solution Approach 2:
The system performs partial probability updating by selecting specific symbols or symbol ranges that benefit most from updated probability estimates. Rather than updating all probability values, the system focuses computational and transmission resources on the most impactful updates, reducing bandwidth utilization while maintaining compression efficiency.
3Device complexity
If probability updating is disabled for certain data portions, then processor utilization decreases, but coding efficiency deteriorates due to non-stationary symbol frequency variations
Solution Approach 1:
The patent introduces dynamic control of probability updating through a probability update indicator that adapts to the characteristics of different data portions. This indicator dynamically determines whether probability updating should be applied based on measures of symbol frequency stability, allowing the system to switch between static and dynamic modes as needed.
Solution Approach 2:
The system changes the updating behavior based on detected symbol frequency characteristics. When symbol frequencies are stable (stationary), the system uses fixed probabilities to reduce processing. When frequencies vary significantly (non-stationary), the system activates probability updating to maintain coding efficiency, thus adapting processor utilization to actual data characteristics.
Data Source
AI summary
Generating encoded image data using adjustable per-symbol entropy coding probability updating may include generating a portion of the encoded image data in accordance with a value of a probability update indicator for the portion indicating whether per-symbol entropy coding probability updating is disabled for the portion, and including the value of the probability update indicator and the entropy coded image data in an output bitstream. Generating decoded image data using adjustable per-symbol entropy coding probability updating may include obtaining a value of a probability update indicator for a portion of the decoded image data, the value of the probability update indicator for the portion indicating whether per-symbol entropy coding probability updating is disabled for the portion, and generating decoded image data for the portion in accordance with the value of the probability update indicator for the portion.