Latent-Space Image Compression With GAN-Based Reconstruction
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Solution Overview
Problem
Existing image compression techniques fail to efficiently reduce the amount of space or bandwidth needed to store or transmit data, and decompression can be a tedious task, and existing methods for transforming the tedious task, and existing technologies fail to efficiently handle the amount of space or bandwidth needed to store or transmit data.
Innovation Solution
A training system for computer implemented generating a synthetic image representing a compressed version of an original image, and existing image compression techniques fail to efficiently handle the amount of space or bandwidth needed to store or transmit data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional image compression techniques are used, then the compression ratio is improved, but the decompression complexity increases
Solution Approach 1:
The patent replaces traditional mechanical decompression algorithms with a neural network-based generator that learns the inverse mapping from latent space to image space. The generator is trained to reconstruct high-quality images from compressed latent representations, eliminating the need for complex traditional decompression algorithms while maintaining visual fidelity.
Solution Approach 2:
The patent transforms the image representation from pixel space to latent space through an encoder, changing the parameter space from raw pixel values to compressed latent variables. This parameter transformation enables efficient storage and transmission while the trained generator reverses this transformation to produce high-quality images without complex decompression.
2Quantity of substance
If image compression is applied, then storage space and bandwidth are reduced, but image quality may deteriorate
Solution Approach 1:
The patent replaces traditional lossy compression that discards image information with a neural network-based autoencoder that learns optimal compression representations. The generator, trained adversarially with a discriminator, reconstructs images with high visual quality by learning the underlying data distribution, thereby maintaining image quality while achieving efficient compression.
Solution Approach 2:
The patent implements feedback through the adversarial training mechanism where the discriminator evaluates the authenticity of generated images and provides gradients back to the generator. This feedback loop continuously improves the generator's ability to produce high-quality images from compressed latent representations, ensuring minimal quality loss during compression.
3Productivity
If complex compression algorithms are used, then compression efficiency is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-training the encoder and generator networks on large datasets to learn optimal compression and reconstruction mappings. Once trained, the compression process simply involves forward propagation through the encoder, which is computationally efficient compared to iterative optimization methods. The heavy computational work is done during the offline training phase, not during actual compression operations.
Data Source
AI summary
A training system for computer implemented training generation of synthetic image data representing a compressed version of original image data comprises an encoder (20) configured to encode original image data (OI) into a latent space representation, a generator (21) configured to generate synthetic image data (GI) based on a latent variable describing a distribution of the latent space representation, and a discriminator (22) configured to evaluate the generated synthetic image data (GI) as to its authenticity. The generated synthetic image data (GI) represents a compressed version of the original image data (OI).