Compression-Domain Vision Models With Online Decoder Training

The patent merges the encoder and decoder into a unified neural network framework that is trained jointly as a single system. The encoder and decoder share a common loss function and are optimized together through end-to-end training, allowing the entire compression system to be treated as one integrated module rather than separate components. This resolving the contradiction by ensuring that module-level optimizations contribute to overall system performance.

The neural network framework is designed to perform multiple functions simultaneously: the encoder performs compression while the decoder performs reconstruction, and both are optimized through a unified training process. The system achieves multi-functionality by using shared neural network components that serve both encoding and decoding purposes, allowing simultaneous optimization of all modules through a single training regime.

The patent replaces traditional manual compression tool optimization with an automated machine learning system. Instead of requiring experts to manually adjust compression parameters and architectures, the system uses neural networks that automatically learn optimal compression strategies through training data. This substitution of mechanical/manual optimization processes with automated learning algorithms dramatically reduces the time and expertise required while maintaining or improving compression quality.

The neural network compression system is self-optimizing through automated training processes. The system automatically adjusts its own parameters, architecture, and compression strategies by learning from training data without requiring continuous human intervention or expert tuning. This self-service capability allows the system to improve compression quality autonomously, eliminating the need for significant human effort and time in the optimization process.