Neural-Network Entropy Decoding for Adaptive 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
Integrate an artificial neural network to produce a context index that dynamically adjusts the entropy decoder's context, allowing for flexible and adaptive decoding by combining an entropy decoder with an artificial neural network to parameterize the decoding process based on previously decoded values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional entropy decoding methods are used with predefined contexts, then decoding process is simple and fast, but flexibility and adaptability to non-standard data formats are poor
Solution Approach 1:
An artificial neural network is introduced as an intermediary component between the data stream and the entropy decoder. The neural network takes previously decoded values as input and produces context indices as output, which then parameterize the entropy decoder. This intermediary layer enables flexible adaptation to various data formats while maintaining the efficiency of traditional entropy decoding.
Solution Approach 2:
The decoding system transitions from static predefined contexts to dynamic context parameterization. The context index produced by the neural network can adapt to different data formats and statistical characteristics in real-time, allowing the entropy decoder to dynamically adjust its behavior based on the actual data being decoded.
2Productivity
If context-based adaptive binary arithmetic coding is used, then compression efficiency is improved, but flexibility when data format is not predefined is reduced
Solution Approach 1:
The system implements a feedback mechanism where previously decoded values are fed back into the artificial neural network. This feedback loop allows the neural network to learn from and adapt to the statistical patterns in the data, producing appropriate context indices that optimize compression efficiency for the specific data format being decoded.
Solution Approach 2:
The context parameter of the entropy decoder is changed dynamically based on the output of the artificial neural network. Instead of using fixed predefined contexts, the system changes the context parameter adaptively according to the data characteristics, thereby maintaining high compression efficiency across different data formats.
3Adaptability or versatility
If an artificial neural network is integrated to produce context indices, then adaptability and flexibility are enhanced, but computational complexity and processing time increase
Solution Approach 1:
The artificial neural network is pre-configured with weights and architecture suitable for producing context indices. By preparing the neural network in advance with appropriate structure and initialization, the system reduces the computational burden during actual decoding operations, minimizing the time overhead while maintaining high adaptability.
Data Source
AI summary
A decoding method for decoding a data stream including a sequence of binary elements. The method includes: applying, at an input of an artificial neural network obtained by decoding a piece of data indicative of an artificial neural network from the data stream, at least one previously decoded value obtained by an entropy decoder; producing a context index at an output of said artificial neural network, as a result of the applying the previously decoded values; and obtaining at least one new decoded value by applying part of the sequence of binary elements to the entropy decoder parameterized in a context identified by the produced context index. An electronic decoding device and an associated computer program are also provided.