Selective Feature Entropy Encoding for Lower Decoding Complexity
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Solution Overview
Problem
Conventional entropy encoding and decoding techniques involve significant computational complexity, leading to reduced efficiency in data processing operations.
Innovation Solution
A data encoding method that involves obtaining auxiliary encoding information, determining scale parameter values, performing quantization processing, selecting a subset of scale parameter quantization values, and performing entropy encoding on a subset of feature elements, while a data decoding method performs quantization processing, selects a subset of scale parameter quantization values, and combines decoded feature elements with replacement elements to restore the original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional entropy encoding and decoding techniques are applied to all feature elements, then complete data reconstruction is achieved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the set of all feature elements into two subsets: those requiring entropy encoding/decoding and those that can be reconstructed through alternative methods. This segmentation allows the system to apply computationally intensive entropy operations only where necessary while using simpler operations elsewhere, thereby reducing overall computational complexity while maintaining reconstruction accuracy.
Solution Approach 2:
The patent applies entropy encoding and decoding only to a subset of feature elements rather than all elements. By performing partial action on only those elements that require it, the system achieves the necessary reconstruction accuracy without the excessive computational burden of processing every single feature element through the full entropy encoding/decoding pipeline.
2Loss of information
If entropy encoding is performed on all feature elements, then data compression is achieved, but processing time increases
Solution Approach 1:
The patent divides feature elements into segments based on their encoding requirements, applying entropy encoding only to the necessary subset. This segmentation reduces the total number of elements processed through the time-consuming entropy encoding pipeline, thereby reducing overall processing time while maintaining compression efficiency for the critical elements.
Solution Approach 2:
The patent performs entropy encoding as a partial action on only those feature elements that require it, rather than applying it universally. This approach maintains data compression efficiency for the encoded elements while avoiding the time penalty of unnecessarily encoding elements that could be handled through alternative, faster methods.
3Productivity
If selective encoding of subset of feature elements is performed, then computational efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and categorizing feature elements into subsets that require entropy encoding versus those that do not. This preliminary classification, done before the actual encoding process, simplifies the subsequent encoding operations by providing a clear roadmap of what needs to be encoded, thereby improving encoding efficiency without adding significant complexity to the overall process.
Data Source
AI summary
A data encoding and decoding system, method, and apparatus for efficient feature data processing. The method obtains auxiliary encoding information of feature data and determines scale parameter values corresponding to feature elements. After quantizing these values to integers, the method selects a subset of scale parameter quantization values exceeding a preset threshold. Feature elements corresponding to these selected values are extracted from the feature data and undergo entropy encoding based on their corresponding quantization values. For unencoded feature elements, the method determines appropriate replacement elements. The process combines entropy encoded elements with replacement elements to create an efficient encoded data stream, which can later be decoded through a reverse process. This selective encoding approach significantly reduces data size while maintaining essential information for accurate reconstruction.