Variational Autoencoder Image Compression Latent Tensor
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Solution Overview
Problem
Traditional image compression methods, such as Fourier transform and principal component analysis, are lossy and result in blurred images and loss of information, especially when achieving high compression ratios.
Innovation Solution
The use of a variational autoencoder that compresses images into a latent tensor and decompresses them back into pixel space with high fidelity, allowing for superior compression ratios without significant loss of image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression algorithms (FT, PCA, fractal) are used to achieve high compression ratios, then image size is reduced, but image quality deteriorates with blurring and information loss
Solution Approach 1:
The patent replaces traditional mechanical/mathematical compression algorithms (Fourier transform, PCA, fractal methods) with a neural network-based variational autoencoder system. This substitution enables the model to learn optimal compression representations that preserve image quality while achieving high compression ratios, resolving the contradiction between size reduction and quality maintenance
Solution Approach 2:
The patent changes the fundamental parameters of image compression by using a learned latent space representation instead of fixed mathematical transforms. The variational autoencoder learns optimal encoding parameters during training, allowing flexible adjustment between compression ratio and image quality based on the specific image content and desired output quality
2Quantity of substance
If Fourier transform is used to compress images by discarding high frequency information, then compression ratio is improved, but edge sharpness deteriorates
Solution Approach 1:
The patent replaces the Fourier transform mechanical system with a neural network-based variational autoencoder that learns to preserve edge information through its latent space representation, avoiding the blurring effect inherent in frequency-based compression methods
3Quantity of substance
If PCA is used to compress images by reducing feature dimensions, then compression ratio is improved, but image features are lost
Solution Approach 1:
The patent replaces PCA's linear dimensionality reduction with a non-linear variational autoencoder that preserves image features through its learned latent space, preventing feature loss while achieving compression
Solution Approach 2:
The patent changes from fixed linear transformation parameters in PCA to learned non-linear parameters in the neural network, enabling better preservation of image features during compression
4Quantity of substance
If fractal compression is used to simulate image data, then compression ratio is improved, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent replaces fractal compression's simulation-based reconstruction with a neural network-based generative model that directly learns to reconstruct images, achieving higher accuracy while maintaining compression efficiency
Data Source
AI summary
Disclosed solutions perform image compression using a variational autoencoder that enables greater compression than traditional methods, while simultaneously maintaining superior fidelity for the decompressed image. Examples persist the bottleneck layer output of a variational autoencoder as a compressed image in the form of a latent tensor. The latent tensor is decompressed by a variational autodecoder into a recovered image in pixel space. In some examples, different encoder/decoder pairs are trained on specific image types, based on feature attributes. For example, maps have lines that are narrow compared to their length (e.g., have a high aspect ratio) which are different than features within photographs of people and scenes. Some examples leverage contrastive language-image pre-training (CLIP) and/or bootstrapping language-image pre-training (BLIP) models to store embeddings, each associated with a compressed image, to enable natural language searches of compressed image collections without requiring decompression.