Adaptive neural upsampling system for decoding lossy compressed data streams

Abstract

A system and method for enhancing lossy compressed data. The system receives a compressed data stream, decompresses it, and enhances the decompressed data using adaptive neural network models. Key features include data characteristic analysis, dynamic model selection from multiple specialized neural networks, and quality estimation with feedback-driven optimization. The system adapts to various data types and compression levels, recovering lost information without detailed knowledge of the compression process. It implements online learning for continuous improvement and includes security measures to ensure data integrity. The method is applicable to diverse data types, including financial time-series, images, and audio. By combining efficient decompression with advanced neural upsampling, the system achieves superior reconstruction of lossy compressed data, enabling improved data transmission, storage, and analysis in bandwidth-constrained or storage-limited environments while maintaining data quality and security.