Entropy-Maximized Media Compression With Neural Manifold Mapping
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Solution Overview
Problem
Existing audio and video compression technologies are limited by historical assumptions and beliefs about human hearing, resulting in suboptimal algorithms and methods that fail to achieve the full potential of audio fidelity and processing capabilities, particularly in capturing and reproducing the full range of frequencies and transient details beyond the limits of traditional human hearing.
Innovation Solution
A deep learning-based system performs multi-faceted analysis on media content, including spectral, statistical, and temporal-spatial correlation analysis, trains a neural network to map media data onto an entropy-maximized dimensional manifold, and applies entropy maximization techniques to compress audio and video while maintaining compatibility with existing ecosystems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional compression algorithms are used, then compatibility with existing ecosystems is maintained, but audio quality and frequency range are limited
Solution Approach 1:
The patent introduces a neural network as an intermediary component that bridges traditional compression algorithms and advanced audio processing. The neural network learns to map between compressed representations and high-quality audio, enabling the system to maintain compatibility with existing ecosystems while achieving superior audio quality through the intermediary transformation layer.
Solution Approach 2:
The patent changes the fundamental parameters of audio representation by using neural networks to transform audio data into a compressed manifold space. This allows the system to operate with different parameter spaces than traditional algorithms, achieving both high quality and extended frequency range while maintaining compatibility through learned transformations.
2Measurement precision
If deep learning-based processing is applied, then audio quality and frequency range are enhanced, but processing complexity increases
Solution Approach 1:
The patent performs preliminary training of the neural network on extensive datasets to learn optimal transformations for audio compression. By pre-training the model to map between high-quality audio representations and compressed manifolds, the system reduces the complexity of real-time processing while maintaining enhanced frequency range and audio quality capabilities.
Solution Approach 2:
The patent creates a learned copy of the complex audio processing transformations through the neural network model. Instead of implementing complex processing algorithms directly, the system uses the neural network as a compressed representation that captures the essential transformations, reducing processing complexity while maintaining high frequency range and quality.
3Productivity
If entropy maximization is applied to the manifold representation, then compression efficiency is improved, but computational steps increase
Solution Approach 1:
The patent performs preliminary dimensionality reduction and manifold learning to transform the audio data into a compressed representation space. By preparing the data structure in advance through the neural network's learned transformations, the system reduces the computational steps required for subsequent entropy maximization and compression operations, improving overall efficiency.
Solution Approach 2:
The patent applies dimensionality change by mapping audio data from the original high-dimensional space into a lower-dimensional manifold representation. This dimensional transformation reduces the computational complexity of entropy maximization operations while maintaining compression efficiency, as the reduced-dimensional data requires fewer computational steps to process.
Data Source
AI summary
A system and method for compression performs analysis of incoming audio or video data, and selects a manifold based on the analysis of the data. A deep learning model is then trained for the manifold. The data is broken down into components and entropy maximization algorithms are utilized for each component before compression commences. Finally, the system translates the compressed data into a standard file format.