Context-Based Entropy Decoding for Variable Data Streams
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Solution Overview
Problem
Existing entropy decoding methods are inflexible and lack adaptability when dealing with data formats that are not fully predefined, particularly in contexts where the technical nature of data depends on machine learning processes like neural networks, leading to suboptimal compression and decoding efficiency.
Innovation Solution
A method and device for decoding data streams that utilize identifiers to determine contexts for entropy decoding, allowing for flexible configuration of entropy decoders based on data stream information, including machine learning methods like deep learning, and incorporate context-specific parameterization to enhance adaptability and 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 standardized, but the method lacks flexibility and adaptability when dealing with data from machine learning processes or non-standard formats
Solution Approach 1:
The patent applies preliminary action by including context identification information and context parameter data in advance within the compressed data stream. The entropy decoding device retrieves and configures the entropy decoder with appropriate contexts before performing decoding operations, eliminating the need for runtime adaptation and enabling flexible handling of machine learning-generated data while maintaining efficient decoding performance
2Adaptability or versatility
If fixed context parameterization is used in entropy decoding, then the decoding process is fast and efficient, but it cannot adapt to varying data formats and characteristics
Solution Approach 1:
The patent resolves this contradiction by performing context configuration in advance - the entropy decoding device retrieves context identification information and corresponding context parameters from the compressed data stream before decoding begins. This preliminary configuration enables rapid adaptation to different data formats during decoding without requiring time-consuming runtime adjustments, thus maintaining decoding speed while achieving format adaptability
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting entropy decoder parameters based on the retrieved context identification information. The device selects and applies appropriate context parameters (such as probability models and syntax element mappings) that match the specific data format being decoded, enabling optimal decoding performance for machine learning-generated data and variable formats without sacrificing decoding efficiency
Data Source
AI summary
A method for decoding a data stream, including a plurality of identifiers and a bit sequence, into a sequence of data of respective predetermined types includes the following operations for obtaining each item of data of the sequence: determining a context on the basis of an identifier, from among the plurality of identifiers, with the type of the relevant item of data; and decoding one portion of the bit sequence by an entropic decoder which receives the bit sequence as an input and is parameterized in the determined context. An electronic decoding device and an associated computer program are also provided.