Latent-Code Generative Compression for Editable Audiovisual Data
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Solution Overview
Problem
Existing lossy compression algorithms for audiovisual data fail to leverage conditional distributions and require manual feature selection, limiting compression efficiency and malleability.
Innovation Solution
A system utilizing a trained generative model with a latent space and generator mapping to convert audiovisual data into latent codes, enabling efficient compression, editing, and searching by generating data on the fly from partial instructions, requiring increased computing power at the edge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional lossy compression algorithms are used, then compression is achieved, but compression efficiency is limited and malleability is reduced
Solution Approach 1:
The patent changes the fundamental parameters of compression by using generative models to create latent codes that capture essential features. Instead of traditional transform coding, the system uses a generator network that maps latent codes to reconstructed images, enabling both high compression efficiency and flexibility in manipulating the compressed representation for various applications.
Solution Approach 2:
The latent code serves as an intermediary representation between the original image and the compressed form. This intermediate representation captures the essential information in a condensed format that can be efficiently stored and manipulated, while the generator network acts as a mediator to reconstruct the image from these latent codes, enabling both compression and subsequent processing.
2Ease of manufacture
If manual feature selection is used in compression algorithms, then implementation is simplified, but compression efficiency is limited
Solution Approach 1:
The generative model automatically learns and adapts to the statistical properties of the input data during training. The system performs self-service by automatically selecting and optimizing features relevant to the data distribution, eliminating the need for manual feature engineering while achieving high compression efficiency through the learned latent space representation.
Solution Approach 2:
The system transitions from manual feature selection to automated parameter learning through the generative model. The generator network automatically adjusts its internal parameters to capture the essential features of the data, replacing manual feature engineering with a data-driven approach that adapts to any input distribution.
3Productivity
If generative models with latent codes are used, then compression efficiency and editing capabilities are improved, but computational requirements increase
Solution Approach 1:
The system segments the computational workload into two distinct phases: training phase and inference phase. During training, the generative model learns compressed representations and stores them in the latent space. During inference, the pre-trained model quickly encodes new data into latent codes and decodes them to reconstructed images, significantly reducing the computational burden for compression operations.
Solution Approach 2:
The generative model performs preliminary learning and feature extraction during the training phase on a representative dataset. This preliminary action creates a pre-computed mapping between latent codes and image features, so that during actual compression operations, the system only needs to perform forward passes through the pre-trained network, which is computationally much cheaper than training.
4Productivity
If edge devices generate content on the fly from partial instructions, then compression and security are improved, but device complexity increases
Solution Approach 1:
The system extracts the essential compression and generation capabilities into a portable generative model that can be deployed on edge devices. By taking out the core functionality into a self-contained model with latent space and generator network, the system enables compression and content generation on edge devices without requiring complex infrastructure, while maintaining high compression efficiency and security through the proprietary latent representation.
Data Source
AI summary
Systems and methods for viewing, storing, transmitting, searching, and editing application-specific audiovisual content (or other unstructured data) are disclosed in which edge devices generate content on the fly from a partial set of instructions rather than merely accessing the content in its final or near-final form. An image processing architecture may include a generative model that may be a deep learning model. The generative model may include a latent space comprising a plurality of latent codes and a trained generator mapping. The trained generator mapping may convert points in the latent space to uncompressed data points, which in the case of audiovisual content may be generated image frames. The generative model may be capable of closely approximating (up to noise or perceptual error) most or all potential data points in the relevant compression application, which in the case of audiovisual content may be source images.