Video Compression Using Probabilistic Latent Space Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video compression technologies are inefficient in utilizing all frames coherently and fail to maximize compression ratios due to reliance on key frames and limited probabilistic modeling, leading to suboptimal file size reduction and image quality.
Innovation Solution
A deep learning-based system using a variational autoencoder framework that encodes and decodes video content by generating a latent space with local and global variables, applying convolutional layers, long short-term memory models, and multilayer perceptrons to model temporal probability distributions, allowing for more effective compression across all frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video compression using key frames and limited probabilistic modeling is used, then device complexity is reduced, but compression ratio and image quality deteriorate
Solution Approach 1:
The patent transforms the video compression approach by changing from traditional deterministic encoding parameters to probabilistic parameters. The system models temporal probability distributions of video frames using deep learning, representing video data in terms of probability distributions rather than fixed values. This parameter transformation enables higher compression ratios by capturing temporal correlations more effectively while managing complexity through learned probabilistic models.
Solution Approach 2:
The patent replaces traditional mechanical video compression mechanisms (key frame selection, motion estimation algorithms) with a deep learning-based probabilistic modeling system. Instead of using fixed mechanical rules for frame compression, the system employs neural networks to learn and model temporal probability distributions, substituting deterministic mechanical processes with adaptive intelligent systems that achieve superior compression performance.
2Manufacturing precision
If key frames and limited probabilistic modeling are used, then processing speed is improved, but image quality and compression efficiency deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training deep learning models on large video datasets to capture temporal probability distributions. The system performs offline training to learn temporal correlations, so that during actual compression, the pre-learned probabilistic models can be applied directly without requiring complex real-time calculations. This preliminary modeling action enables high image quality while maintaining processing speed by shifting computational burden from encoding to training phase.
3Loss of information
If all frames are processed coherently with deep learning, then compression ratio is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by differentiating the processing approach for different parts of video data. The system uses local variables to represent frame-specific features and global variables to represent temporal correlations across multiple frames. By applying different levels of probabilistic modeling to local and global aspects separately, the system achieves coherent processing of all frames while managing computational complexity through hierarchical variable representation.
Solution Approach 2:
The patent segments the video compression task into distinct probabilistic modeling components: local variable encoding for individual frame characteristics and global variable encoding for temporal correlations. This segmentation allows the system to process all frames coherently by combining local and global probabilistic models, achieving high information retention while organizing computational complexity into manageable separate modules that can be processed independently.
Data Source
AI summary
Systems and methods for compressing target content are disclosed. In one embodiment, a system may include non-transient electronic storage and one or more physical computer processors. The one or more physical computer processors may be configured by machine-readable instructions to obtain the target content comprising one or more frames, wherein a given frame comprises one or more features. The one or more physical computer processors may be configured by machine-readable instructions to obtain a conditioned network. The one or more physical computer processors may be configured by machine-readable instructions to generate decoded target content by applying the conditioned network to the target content.