UNIFIED SYSTEM FOR MULTI-MODAL DATA COMPRESSION WITH RELATIONSHIP PRESERVATION AND NEURAL RECONSTRUCTION

Abstract: a unified platform for multi-modal data compression and decompression that enables efficient processing of correlated data streams while preserving relationships between different modalities. the platform employs a virtual management layer to analyze and route input streams, implementing correlation analysis to identify temporal and spatial relationships between streams. multiple compression methods, including neural network-based approaches, are utilized to compress data sets while maintaining cross-modal dependencies. a neural upsampling system leverages learned correlations between streams to enhance reconstruction quality. the platform includes a synchronization manager that maintains temporal alignment and relationship preservation throughout processing. by integrating correlation-aware compression with neural upsampling techniques, the platform provides comprehensive multi-modal compression capabilities while preserving critical relationships between different data types. the system is particularly suited for applications involving synchronized audio-visual data, sensor streams, and other multi-modal content.

Inventor(s): Brian Galvin

CPC Classification: G06F16/215 (Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors)

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