Correlated Data Compression With Common-Private Channel Splitting
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Solution Overview
Problem
Current telecommunication and information systems face challenges in efficiently encoding and transmitting information from multiple correlated sources over multiple channels, particularly in noisy environments, where existing methods struggle to optimize resource usage and maintain data fidelity.
Innovation Solution
A computer-implemented method that compressively encodes two correlated data vectors by transforming them into canonical vectors, separating common and private information, and routing these components through different channels at optimized rates to achieve efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is compressed to improve transmission efficiency, then channel resource usage improves, but data fidelity deteriorates
Solution Approach 1:
The patent segments the transmission task into three distinct components: common information transmitted over a public channel, and private information transmitted over two private channels. This segmentation allows optimized compression for each component while maintaining overall data fidelity through the combination of all three channels at the receiver.
Solution Approach 2:
The patent transforms the original correlated data vectors into canonical vectors through linear transformations, changing the parameter representation of the data. This transformation separates the correlated components into distinct canonical forms that can be independently compressed and transmitted, resolving the contradiction between compression efficiency and fidelity preservation.
2Reliability
If channel encoding redundancy is added to combat noise, then reliability improves, but channel resource usage deteriorates
Solution Approach 1:
The patent applies source encoding and compression before channel transmission, performing the redundancy removal action in advance. By pre-compressing the data into canonical vectors and separating common and private information, the system reduces the amount of data requiring channel encoding, thereby improving channel resource efficiency while maintaining reliability through the structured encoding approach.
3Manufacturing precision
If multiple correlated sources are transmitted separately, then data fidelity is maintained, but channel resource usage deteriorates
Solution Approach 1:
The patent merges the transmission of multiple correlated sources by identifying and extracting common information that appears in both data vectors. This common information is transmitted once over the public channel, while only the private, non-redundant portions are transmitted over the private channels, thereby reducing total channel resource usage while maintaining complete data fidelity.
Solution Approach 2:
The patent introduces a new dimensional structure by transforming two-dimensional correlated data vectors into a three-component transmission structure (common + private1 + private2). This dimensional transformation allows the system to exploit correlations between sources and achieve more efficient channel utilization while preserving all information.
Data Source
AI summary
Methods are provided for efficiently encoding and decoding multivariate correlated data sequences for transmission over multiple channels of a network. The methods include transforming data vectors from correlated sources into vectors that comprise substantially independent and correlated components, and generating a common information vector based on the correlated components, and two private information vectors. The methods also include computing the amount of information, such as Wyner's lossy common information, in the common information vector, computing rates that lie on the Gray-Wyner rate region, and choosing compression rates based on the amount of common information. The methods may be applicable, in general, to a wide range of communications and/or storage systems and, particularly, to sensor networks and multi-user virtual environments for gaming and other applications.