Parameterized FSM for Binary Arithmetic Coding
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Solution Overview
Problem
Current video coding technologies face inefficiencies in adaptive probability estimation for binary arithmetic coding, leading to suboptimal compression efficiency due to the use of universal adaptation techniques that fail to account for variability in data statistics across different contexts.
Innovation Solution
The implementation of parameterized state updating functions and finite state machines (FSMs) that modify FSM parameters based on past probability variation and context-specific conditions to improve binary probability estimation, enabling more precise adaptation to changing probability estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If universal adaptation techniques are used for binary arithmetic coding, then the coding process is simple, but compression efficiency deteriorates due to inability to account for variability in data statistics across different contexts
Solution Approach 1:
The patent divides the universal adaptation technique into multiple context-specific Finite State Machines (FSMs), where each FSM handles a specific context or type of data. This segmentation allows the system to account for variability in data statistics across different contexts while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces parameterized state updating functions that dynamically adjust FSM parameters based on past probability variation and context-specific conditions. This dynamic adaptation enables the system to optimize compression efficiency for each context while keeping the overall structure flexible and adaptable.
2Productivity
If context-specific probability estimation is implemented, then compression efficiency improves, but device complexity increases due to multiple FSM parameters and state updating functions
Solution Approach 1:
The patent modifies FSM parameters based on measured past probability variation rather than maintaining completely separate complex models for each context. By adjusting parameters of existing FSMs dynamically, the system achieves context-specific adaptation with reduced complexity compared to implementing entirely separate estimation systems for each context.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system measures past probability variation and uses this information to modify FSM parameters for future predictions. This feedback loop enables the system to adapt to context-specific conditions automatically, reducing the need for manual configuration and simplifying the overall system architecture.
3Measurement precision
If FSM parameters are modified based on past probability variation, then accuracy of probability estimation improves, but computational overhead increases
Solution Approach 1:
The patent modifies only the necessary FSM parameters based on measured probability variation rather than completely recalculating all probability estimates from scratch. This partial action approach maintains high accuracy where needed while reducing computational overhead by leaving unchanged parameters as they are.
Data Source
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AI summary
For at least one respective bin of the bin stream, a decoder may determine, based on a state for the respective bin, an interval for the respective bin, and the offset value, a value of the respective bin. Additionally, the decoder determines one or more Finite State Machine (FSM) parameters for a next bin of the bin stream. The one or more FSM parameters for the next bin controls how probability estimates for the next bin are computed from a state for the respective bin. The decoder determines using a parameterized state updating function that takes as input the state for the respective bin, the one or more FSM parameters for the next bin of the bin stream, and the value of the respective bin, a state for the next bin of the bin stream. The decoder may debinarize the bin stream to form a decoded syntax element.