Deep Generative Video Compression via Latent Code Mapping
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Solution Overview
Problem
Conventional video compression algorithms struggle to adapt to variations in video content, new resolutions, frame rates, and non-natural imagery, leading to inefficient bandwidth usage and storage requirements, especially in wireless networks.
Innovation Solution
The use of deep generative models, specifically auto-encoders and probabilistic models implemented by artificial neural networks, to compress and decompress video content by mapping frames into a latent code space and generating a probability distribution for lossless compression, allowing for adaptable video encoding and decoding mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional video compression algorithms are used, then bandwidth usage and storage requirements are reduced, but adaptability to variations in video content, new resolutions, frame rates, and non-natural imagery is poor
Solution Approach 1:
The patent transforms fixed compression parameters into learnable parameters through neural network training. The autoencoder learns optimal compression parameters automatically from training data, enabling adaptation to different video content types, resolutions, and frame rates without manual reconfiguration. This resolves the contradiction by making the algorithm adaptable while maintaining reasonable complexity through automated learning.
Solution Approach 2:
The patent introduces dynamic adaptability by training the neural network to adjust compression behavior based on input characteristics. The model dynamically modifies compression strategies according to the specific video content being processed, whether natural imagery or non-natural content, achieving versatility without requiring multiple fixed algorithms.
2Adaptability or versatility
If deep generative models are used for video compression, then adaptability to various video content types is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent performs computationally intensive training operations in advance during an offline phase. The neural network learns compression patterns and adapts to different content types beforehand, so that during actual video compression, the model can efficiently process various content types without requiring excessive real-time computational energy. This separates the high-energy learning phase from the lower-energy inference phase.
3Productivity
If auto-encoders with latent code space mapping are used, then compression efficiency and quality are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces a latent code space as an intermediary representation between the input video and compressed output. This latent space acts as a bridge that captures essential video information in a compressed form, enabling efficient reconstruction while simplifying the overall compression pipeline. The intermediary latent representation makes the complex compression task more manageable and implementable.
Data Source
AI summary
Certain aspects of the present disclosure are directed to methods and apparatus for compressing video content using deep generative models. One example method generally includes receiving video content for compression. The received video content is generally encoded into a latent code space through an auto-encoder, which may be implemented by a first artificial neural network. A compressed version of the encoded video content is generally generated through a trained probabilistic model, which may be implemented by a second artificial neural network, and output for transmission.