Generative Face Video SEI Matching for Compressed Face Reconstruction
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Solution Overview
Problem
Existing video coding standards face challenges in efficiently encoding and decoding face information in video data, particularly in high-compression scenarios, leading to suboptimal quality and efficiency in applications like surveillance, conferencing, and live broadcasting.
Innovation Solution
The use of generative face video supplemental enhancement information (SEI) messages to enhance the encoding and decoding process, allowing for the reconstruction of face pictures using generative networks, by incorporating face information parameters and identifying network compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video coding standards are used to encode face information, then compression is achieved, but encoding and decoding efficiency deteriorates and quality is suboptimal
Solution Approach 1:
The patent segments face information from general video content by introducing a dedicated face detection module and separate encoding path. Face regions are identified and extracted using bounding boxes or segmentation masks, then processed independently through SEI messages while the rest of the video follows conventional coding. This segmentation allows specialized handling of face regions to improve both quality and efficiency.
Solution Approach 2:
The patent changes the parameter representation for face information by introducing SEI messages that carry dedicated face attributes (landmarks, expressions, pose) alongside traditional video parameters. This parameter transformation enables more efficient encoding of face-specific characteristics while maintaining compatibility with existing video coding standards, thereby improving both encoding efficiency and reconstruction quality.
2Loss of energy
If compression is increased to reduce bandwidth and storage, then transmission efficiency improves, but face information quality deteriorates
Solution Approach 1:
The patent introduces SEI messages as an intermediary layer between the video encoder and decoder, specifically for face information. These messages carry compressed yet essential face attributes (landmarks, expressions, pose parameters) that act as a mediator to preserve face quality even when the main video stream is heavily compressed. The intermediary SEI layer ensures that critical face characteristics are maintained independently of the compression level applied to the overall video.
Solution Approach 2:
The patent transforms face information into a compact parameter representation within SEI messages, encoding essential face characteristics (landmarks, expressions, pose) in a compressed format. This parameter transformation allows high-quality face reconstruction from reduced data, enabling significant bandwidth savings while maintaining face information quality through efficient parameter-based representation rather than full-resolution face data.
3Device complexity
If conventional encoding methods are used, then implementation is simple, but adaptability to different face scenarios deteriorates
Solution Approach 1:
The patent creates a universal face encoding framework through SEI messages that can handle multiple face scenarios (different expressions, poses, lighting conditions, face sizes) within a single standardized structure. The SEI message format is designed to be multi-functional, accommodating various face attributes and generative network requirements, thereby providing broad adaptability across different surveillance, conferencing, and broadcasting scenarios without requiring separate encoding solutions for each case.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the SEI message content and face detection parameters to adjust automatically based on scene requirements. The system can dynamically select which face attributes to encode, adjust compression levels, and adapt to varying face conditions (distance, angle, expression) while maintaining a relatively simple base implementation. This dynamic behavior enables the system to handle diverse face scenarios without proportionally increasing implementation complexity.
Data Source
AI summary
A method for decoding a bitstream includes: receiving a bitstream and decoding, using coded information of the bitstream, one or more pictures. The decoding of the one or more pictures includes: determining whether a generative face video supplemental enhancement information (SEI) message matches with a generative network; and in response to the generative face video SEI message matches with the generative network, decoding the SEI message. The decoding of the SEI message includes: determining a face information parameter and a base picture associated with the SEI message; and reconstructing a face picture based on the face information parameter and the base picture.