Attention-Guided Video Compression for Teleoperated Vehicle Control
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Solution Overview
Problem
Conventional video compression methods, such as H.264, fail to adapt dynamically to fluctuating network conditions and analytical demands, leading to severe performance drops in deep learning-based vision models, and existing approaches for lossy compression do not effectively balance bandwidth efficiency with video quality and analytical accuracy.
Innovation Solution
An adaptive video compression system using a differentiable surrogate model of H.264 codec and a lightweight control network to predict optimal codec parameters based on current content and network bandwidth, ensuring the performance of deep learning-based vision models is maintained while meeting bandwidth constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional video compression methods (H.264) are used to reduce bandwidth consumption, then bandwidth efficiency is improved, but the performance of deep learning-based vision models deteriorates severely
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting compression parameters (QP, CRF, RB) based on network conditions and video content. The system modifies H.264 codec parameters in real-time to optimize the balance between compression ratio and vision model performance, rather than using fixed compression settings. This allows the system to adapt compression strength according to actual bandwidth availability and analytical requirements.
Solution Approach 2:
The patent implements dynamics by making the compression system adaptive and responsive to changing conditions. The compression parameters are continuously adjusted based on real-time network bandwidth measurements and video frame analysis. The system transitions from static compression settings to dynamic parameter adjustment, allowing optimal compression ratios to change according to network conditions and video content characteristics.
2Device complexity
If static compression settings are used to simplify the compression process, then device complexity is reduced, but video quality for analytics deteriorates due to inability to adapt to changing scenes
Solution Approach 1:
The patent applies preliminary action by analyzing video frames and network conditions before compression occurs. The system pre-determines optimal compression parameters based on foreground/background detection and network bandwidth assessment. This allows the compression process to start with already-optimized parameters rather than requiring complex real-time adjustment during compression.
Solution Approach 2:
The patent implements local quality by applying different compression strengths to different regions of the video frame. Important regions (foreground objects) are compressed less aggressively while background regions undergo stronger compression. This spatially-adaptive compression maintains analytical quality where needed while reducing overall bandwidth consumption.
3Loss of energy
If aggressive compression is applied to maximize bandwidth efficiency, then bandwidth utilization is improved, but analytical accuracy of video content deteriorates
Solution Approach 1:
The patent applies local quality by differentiating compression strength across spatial regions. Foreground regions containing important objects receive mild compression to preserve analytical accuracy, while background regions undergo aggressive compression to maximize bandwidth efficiency. This regional differentiation allows the system to optimize both bandwidth utilization and analytical accuracy simultaneously.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting compression parameters based on the importance of different video regions and current network conditions. The system modifies QP, CRF, and RB parameters selectively to maintain optimal compression ratios for each region, ensuring that critical information is preserved while maximizing overall compression efficiency.
Data Source
AI summary
Systems and methods are provided for optimizing video compression for remote vehicle control, including capturing, capturing video and sensor data from a vehicle using a plurality of sensors and high-resolution cameras, analyzing the captured video to identify critical regions within frames of the video using an attention-based module. Current network bandwidth is assessed and future bandwidth availability is predicted. Video compression parameters are predicted based on an analysis of the video and an assessment of the current network bandwidth using a control network, and the video is compressed based on the predicted parameters with an adaptive video compression module. The compressed video and sensor data is transmitted to a remote-control center, and received video and sensor data is decoded at the remote-control center. The vehicle is autonomously or remotely controlled from the remote-control center based on the decoded video and sensor data.