Data Stream Compression Using PDF-Matched Entropy Encoding
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Solution Overview
Problem
The challenge in data transmission over high-speed communication networks and system interconnects is to efficiently manage the increasing data rates and volumes, requiring effective compression and decompression methods to reduce traffic and ensure accurate data delivery.
Innovation Solution
The implementation of a data compression accelerator (DCA) that uses probability distribution functions (PDFs) to compress data streams by matching calculated PDFs to predefined templates, selecting the closest match, and employing entropic encoders for lossless compression, along with a corresponding decompression process at the receiver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is applied to reduce traffic over system interconnects, then data transmission efficiency is improved, but device complexity increases due to the need for PDF computation, matching, and encoder selection circuits
Solution Approach 1:
The compression apparatus is divided into distinct functional modules: a PDF computation module that calculates probability distributions, a matching module that compares PDFs to templates, and an encoder selection module that chooses appropriate encoders. This segmentation allows each module to be optimized independently and facilitates parallel processing, improving overall compression efficiency while managing complexity through modular design.
Solution Approach 2:
Probability distribution functions and encoder templates are pre-computed and stored in memory before actual data compression occurs. The system prepares multiple encoder templates with different characteristics in advance, allowing the matching module to quickly select the most appropriate encoder for the given data characteristics without performing complex computations during the compression of actual data streams.
2Adaptability or versatility
If multiple encoder templates are stored in memory for matching, then compression adaptability is improved, but memory requirements and device complexity increase
Solution Approach 1:
The encoder templates are designed to be universal and reusable across different data types and compression scenarios. Each template represents a general compression strategy that can be applied to various data characteristics. The matching module selects from these universal templates based on the observed PDF characteristics, allowing the system to achieve high adaptability without needing separate encoders for every possible data type.
Solution Approach 2:
Instead of storing complete encoder configurations for every possible data scenario, the system varies parameters within a limited set of templates. The matching module adjusts template selection based on PDF parameters such as entropy, distribution shape, and data characteristics. This parameter-based approach allows the system to adapt to different data types by selecting and tuning existing templates rather than storing numerous complete encoder sets.
3Measurement precision
If PDF computation and matching is performed for every data stream, then compression accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The system performs PDF computation and template matching to a sufficient degree of accuracy rather than exhaustive precision. The matching module compares PDF characteristics against templates and selects the best match based on predefined thresholds and similarity metrics. This partial action approach achieves adequate compression accuracy for most practical applications without performing overly complex or unnecessary computations, thereby reducing processing time while maintaining acceptable compression quality.
Data Source
AI summary
Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a decomposition of matrix data to generate eigenvectors and associated eigenvalues, determining clusters of the eigenvectors based on weighting the eigenvalues, calculating an eigenvector centroid for each cluster so that a dictionary of centroids is generated, and tagging the eigenvectors with tags, respectively, that identify an associated eigenvector centroid for each eigenvector. The method also includes counting a number of eigenvectors associated with each eigenvector centroid to construct a probability distribution function (PDF) of centroids, matching the PDF of centroids to PDF templates to determine a closest matching PDF template, determining an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is identified, encoding the tags with the encoder to generate an encoded data stream, and transmitting the encoded data stream, the encoder identifier, the dictionary of centroids, and the eigenvalues.