Latent-Code Audiovisual Editing for Compression and Search
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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 using generative models to map audiovisual data to latent codes, enabling efficient compression, editing, and searching by generating content on the fly from partial instructions, with specialized hardware for processing.
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 manual feature selection is required
Solution Approach 1:
The system employs generative models that automatically learn and extract relevant features from audiovisual data without requiring manual feature selection. The model self-adapts to the data characteristics, performing feature extraction as an inherent part of the compression process, thereby eliminating the need for manual intervention while improving compression efficiency
Solution Approach 2:
The invention transforms the compression approach by changing from fixed manual feature selection to dynamic parameter learning through generative models. The system learns optimal compression parameters automatically from the data distribution, adapting to different content types and achieving superior compression efficiency without manual configuration
2Adaptability or versatility
If generative models with latent codes are used, then editing capabilities are improved, but computing power requirements increase
Solution Approach 1:
The system performs preliminary encoding of audiovisual data into latent codes during the compression phase. This preliminary action transforms the data into a compact representation that can be easily manipulated for editing operations, reducing the computing power needed during actual editing while maintaining versatile editing capabilities
Solution Approach 2:
The invention introduces latent codes as an intermediary representation between the original audiovisual data and the edited output. These latent codes serve as a compact intermediate form that enables efficient editing operations, bridging the gap between high-fidelity source material and computationally efficient manipulation
3Quantity of substance
If lossy compression is applied, then data size is reduced, but information loss occurs
Solution Approach 1:
The system employs dynamic compression strategies where the generative model adapts the compression level based on the importance of different data components. Less critical information is compressed more aggressively while preserving essential features, achieving a dynamic balance between data size reduction and information retention that outperforms static lossy compression methods
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.