ML Encoder Circuit for High-Resolution Data Compression
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Solution Overview
Problem
Current data compression techniques, especially lossy compression, result in significant information loss, which is not suitable for high-bandwidth and high-storage demands of 4K, 8K video, high-resolution audio, and holographic data, while lossless compression provides insufficient reduction.
Innovation Solution
A machine learning (ML) encoder circuit generates an ML model from source data features, allowing for efficient data encoding by outputting features instead of raw data, reducing storage and bandwidth requirements, and dynamically updates the model when significant changes occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression techniques are used, then data accuracy is improved, but compression ratio deteriorates
Solution Approach 1:
The patent segments data processing into two distinct phases: a training phase where the ML model learns from source data, and an encoding phase where the trained model compresses data. This segmentation allows the system to achieve high compression ratios during encoding while maintaining accuracy through the pre-trained model's understanding of data patterns.
Solution Approach 2:
The patent performs preliminary training of the ML model using source data before actual compression encoding. This preliminary action (training phase) enables the model to learn data characteristics and patterns in advance, allowing it to achieve both high compression ratios and maintain data accuracy during the subsequent encoding phase without losing information.
2Quantity of substance
If lossy compression techniques are used, then compression ratio is improved, but information loss worsens
Solution Approach 1:
The patent introduces an ML model as an intermediary between source data and compressed data. The model acts as a smart mediator that learns to represent data efficiently while preserving essential information. During encoding, the trained model transforms source data into compressed form with high compression ratios while maintaining data fidelity, avoiding the information loss typical of traditional lossy compression.
3Quantity of substance
If data is compressed for bandwidth reduction, then storage and transmission efficiency is improved, but data fidelity deteriorates
Solution Approach 1:
The patent replaces traditional mechanical compression algorithms with a machine learning-based compression system. The ML model learns complex data patterns and relationships that traditional algorithms cannot capture, enabling high compression ratios while maintaining data fidelity. The system substitutes conventional compression mechanics with intelligent, adaptive learning-based compression that preserves data quality.
Data Source
AI summary
Approaches for encoding include inputting a time-ordered sequence of source data to a machine learning (ML) encoder circuit. The ML encoder circuit extracts first features from a first subset of the source data and generates an ML model from the first features. The ML encoder circuit outputs the first subset of source data while generating the ML model and the ML model is incomplete. Once completed, the ML encoder circuit outputs the ML model for decoding subsequently extracted features. Thereafter, the ML encoder circuit extracts second features from a second subset of the source data and outputs the second features for decoding using the ML model.