Adaptive Entropy Decoding With Neural Context Selection
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Solution Overview
Problem
Existing entropy decoding methods in audio and video content encoding are inflexible and lack adaptability when the data format is not fully predefined, leading to suboptimal compression and decoding performance.
Innovation Solution
Employing an artificial neural network to determine a context index for entropy decoding, allowing dynamic adaptation of the decoding process based on previously decoded values, and using a parallel processing unit to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional entropy decoding methods are used with predefined contexts, then the decoding process is simple and fast, but the system lacks flexibility and adaptability when data format is not fully predefined
Solution Approach 1:
The patent applies dynamics by making the context selection process adaptive rather than static. An artificial neural network dynamically determines context indices based on previously decoded values, allowing the decoding system to adapt to varying data formats and patterns. This transforms the fixed, predefined context approach into a dynamic, data-driven context selection mechanism that improves versatility while managing complexity through learned patterns.
Solution Approach 2:
The artificial neural network serves as an intermediary between the decoded data and the entropy decoding process. It takes previously decoded values as input and produces context indices that guide the entropy decoder, acting as a mediator that translates raw decoded data into meaningful context information. This intermediary layer enables adaptive context selection without requiring complex rule-based systems.
2Adaptability or versatility
If an artificial neural network is introduced to determine context indices dynamically, then flexibility and adaptability improve, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by having the neural network process and learn from previously decoded values before they are needed for context determination. The network is pre-trained on data patterns, so when deployed, it can quickly infer context indices without extensive real-time computation. This preliminary processing and training phase enables faster runtime performance while maintaining high adaptability.
Solution Approach 2:
The system changes parameters by using the neural network to output context indices that directly parameterize the entropy decoding process. Instead of complex adaptive algorithms, the system transforms the adaptability requirement into a parameter selection problem, where the neural network learns optimal context index mappings. This parameter-based approach simplifies the decoding process while maintaining flexibility.
3Manufacturing precision
If context indices are determined based on previously decoded values through neural network, then compression performance improves, but synchronization between neural network and entropy decoder becomes critical
Solution Approach 1:
The patent implements feedback by using previously decoded values as input to the neural network, which then determines context indices for the current decoding step. This creates a feedback loop where past decoding results influence future context selection, enabling the system to adapt to data patterns and improve compression performance. The feedback mechanism ensures that context selection is continuously optimized based on actual decoded data.
Solution Approach 2:
The patent applies segmentation by separating the decoding system into distinct functional components: the neural network module that determines context indices and the entropy decoder module that performs actual decoding. This segmentation allows each component to be optimized independently and facilitates clear synchronization protocols, where the neural network outputs context indices that the entropy decoder consumes, maintaining reliability through well-defined interfaces.
Data Source
AI summary
A method for decoding a bit sequence includes applying, at the input of an artificial neural network, previously decoded values; as a result of the previously decoded values being applied, producing a context index at the output of the artificial neural network; and obtaining a new decoded value by applying a portion of the bit sequence to an entropic decoder parameterized in the context identified by the produced context index. An electronic decoding device and an associated computer program are also provided.