Neural Network Image Compression for Fidelity and File Size
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image compression techniques use a fixed method that does not optimize for all variations of images, resulting in either loss of fidelity or larger file sizes.
Innovation Solution
Utilizing a machine learning model, specifically a neural network, to generate a compressed representation of an image through training based on a set of images, allowing for optimized image compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional fixed compression methods are used, then processing simplicity is maintained, but image fidelity is lost and file size increases
Solution Approach 1:
The patent applies dynamics by transitioning from fixed compression methods to adaptive neural network-based compression. The system dynamically adjusts compression parameters and strategies based on the specific characteristics of each image, allowing optimization for both fidelity and file size reduction rather than using a one-size-fits-all approach.
Solution Approach 2:
The patent utilizes parameter changes by modifying compression ratios, quality levels, and processing techniques based on image-specific parameters analyzed by the neural network. This allows the system to select optimal compression parameters for each image to maintain fidelity while minimizing file size.
2Adaptability or versatility
If conventional fixed compression methods are used, then device complexity is reduced, but compression optimization for image variations is lost
Solution Approach 1:
The patent employs copying by using pre-trained neural network models that can be replicated and deployed across different systems. These model copies enable adaptive compression without requiring complex real-time training, balancing adaptability with manageable system complexity.
Solution Approach 2:
The patent applies preliminary action through pre-training neural network models on diverse image datasets before deployment. This preliminary training equips the models with adaptability to handle various image types, reducing the complexity of real-time processing while maintaining high compression optimization.
Data Source
AI summary
The present disclosure generally relates to representing an image using a machine learning model.