Neural Network Image Compression with Recognition Feedback
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Solution Overview
Problem
Current image compression methods fail to effectively balance data reduction and information retention, leading to suboptimal image transfer rates and recognition accuracy.
Innovation Solution
An image compression method utilizing a neural network framework, where an original image is compressed using a compression neural network model, recognized by a recognition neural network, and the loss function is adjusted to determine convergence and update the model, ensuring improved compression effectiveness and recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image compression is performed to reduce data volume, then transfer rate is improved, but information retention deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the compressed image is fed into a recognition neural network to generate reference tag information, which is then compared with target tag information to calculate a loss function. This loss function is used to iteratively update the compression model parameters, creating a closed-loop system that continuously improves compression quality while maintaining information retention.
Solution Approach 2:
The patent performs preliminary training of both the compression neural network and recognition neural network before actual compression. The compression model is pre-trained with initialization parameters, and the recognition model is trained beforehand to establish baseline performance. This preliminary preparation enables the system to achieve effective compression with good information retention from the start.
2Quantity of substance
If compression ratio is increased to reduce data volume, then storage efficiency is improved, but recognition accuracy deteriorates
Solution Approach 1:
The patent employs dynamic parameter adjustment where the compression model parameters are not fixed but are iteratively updated based on the loss function calculated from recognition accuracy feedback. This dynamic adaptation allows the system to optimize the balance between compression ratio and recognition accuracy, adjusting the compression strength based on actual performance rather than using a static compression level.
Solution Approach 2:
The patent changes the parameters of the compression neural network based on the loss function derived from recognition accuracy. By iteratively adjusting model parameters (such as weights and biases) according to the feedback from the recognition network, the system optimizes the compression parameters to achieve the best possible recognition accuracy for a given compression ratio.
3Productivity
If conventional compression methods are used to reduce data volume, then transfer efficiency is improved, but image quality deterioration occurs
Solution Approach 1:
The patent replaces conventional mechanical/image processing compression methods with a neural network-based approach. Instead of using traditional compression algorithms that operate on pixel levels, the system uses a compression neural network that learns optimal compression representations through training, substituting mathematical/mechanical compression with intelligent, adaptive compression that better preserves image quality.
Solution Approach 2:
The patent introduces a recognition neural network as an intermediary to evaluate compression quality. This intermediary system provides objective feedback on the quality of compressed images by generating tag information that can be compared with original image tags, enabling the optimization of compression parameters based on actual quality metrics rather than subjective assessment.
Data Source
AI summary
An example image compression method may include acquiring an original image with a first resolution; compressing the original image on the basis of the target model to obtain a compressed image with a second resolution; recognizing the compressed image on the basis of a recognition neural network model to obtain reference tag information; acquiring a loss function according to the target tag information and the reference tag information; if the loss function is convergent to a first threshold value or the present number of training times of the compression neural network is more than or equal to a second threshold value, acquiring a target original image with the first resolution, and determining the target model as a corresponding compression neural network model if training of the compression neural network is completed; compressing the target original image on the basis of the compression neural network model.