Household Image Compression Using Generative Neural Networks
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Solution Overview
Problem
The increasing volume of high-resolution image and video data from household appliances leads to higher data transmission and storage costs, necessitating improved image compression and decompression methods.
Innovation Solution
The use of generative neural networks, specifically file-tuned to images of household members, for both compressing and decompressing images, along with deep image compression techniques, to enhance data compression ratios and maintain image fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images and video data are captured and stored, then image quality and detection accuracy are improved, but data storage and transmission costs increase
Solution Approach 1:
The patent applies parameter changes by transforming image data from spatial domain to frequency domain using Fourier transform, then selectively retaining only significant frequency components. This changes the representation parameters of the image data, allowing high-quality reconstruction with fewer data points, thus reducing storage and transmission costs while maintaining image quality
Solution Approach 2:
The patent extracts and retains only the most important frequency components of the image signal using thresholding techniques. By identifying and keeping only significant coefficients above a certain threshold, the system removes redundant information while preserving essential image features, achieving compression without significant quality loss
2Quantity of substance
If image compression is applied to reduce data volume, then storage and transmission costs are reduced, but image quality and recognition accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary action by pre-processing images with Fourier transform and identifying significant frequency components before compression. This preparatory step allows the system to focus compression efforts on less critical data while preserving important features, ensuring quality is maintained even after aggressive compression
Solution Approach 2:
The patent introduces frequency domain representation as an intermediary between the original image and the compressed storage format. This intermediate representation allows for selective retention of important information and facilitates lossless or near-lossless reconstruction, mediating between compression requirements and quality preservation
Data Source
AI summary
Methods of handling an image of a member of a household may include compressing the image of the member of the household using deep image compression and storing the compressed image in a remote database. Some methods may include storing a face annotation and a caption of the image of the member of the household in the remote database. Such methods may also include decompressing the image of the member of the household. The image may be decompressed using the face annotation and the caption. The image may be decompressed using a generative neural network file-tuned to images of members of the household.