Context-Aware Video Compression for Vehicle Event Recorders
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Solution Overview
Problem
Vehicle event recorders face inefficiencies in data transmission and storage due to the need to transmit and store full-length high-bandwidth video data, as only small epochs of interest are typically required for review, leading to unnecessary expenses.
Innovation Solution
A system using a context-aware compression model to dynamically adapt video data reduction based on driver and vehicle context, and an intelligent video stream restoration process that uses vehicle context, sensor data, and historical data to reconstruct high-quality video from compressed streams, reducing storage and transmission requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full-length high-bandwidth video data is transmitted and stored, then complete video information is preserved for review, but transmission and storage costs increase unnecessarily
Solution Approach 1:
The system extracts only the essential video frames (key frames and selected delta frames) that contain meaningful information, discarding redundant frames during normal driving conditions. This extraction approach preserves necessary video information while significantly reducing the volume of data transmitted and stored, directly addressing the contradiction between information completeness and transmission/storage costs.
Solution Approach 2:
The system applies partial action by transmitting only a subset of video frames rather than all frames. During normal driving, only key frames and selective delta frames are transmitted. During event detection, the system increases transmission frequency to capture complete event sequences, applying excessive action only when necessary to ensure information completeness while minimizing overall data volume.
2Loss of energy
If video compression is applied to reduce data volume, then transmission and storage efficiency improve, but video quality and reconstruction accuracy may deteriorate
Solution Approach 1:
The system introduces an intermediary reconstruction model that acts as a mediator between the compressed video data and the final reconstructed output. This model uses contextual information from sensor data and surrounding data to fill in missing details and restore video quality, effectively bridging the gap between compression efficiency and reconstruction accuracy.
Solution Approach 2:
The system dynamically changes compression parameters based on driving context and detected events. During normal driving, higher compression ratios are applied. When events are detected, the system adjusts parameters to transmit more frames with lower compression, ensuring quality preservation for important moments while maintaining efficiency during routine operation.
3Loss of energy
If video frames are selectively dropped to reduce bandwidth, then transmission costs decrease, but the ability to capture complete event sequences may be compromised
Solution Approach 1:
The system employs feedback mechanisms where sensor data and surrounding data are continuously monitored to detect events. When events are detected, the system responds by adjusting the video transmission strategy to capture complete event sequences. This feedback loop ensures that bandwidth is optimized during normal operation while reliability is maintained during critical events.
Solution Approach 2:
The video transmission system is dynamic rather than static. The frame selection and transmission frequency adapt in real-time based on detected events and driving conditions. During normal driving, frames are selectively dropped to reduce bandwidth. Upon event detection, the system dynamically increases transmission frequency to ensure complete event capture, resolving the contradiction between bandwidth efficiency and event detection reliability.
Data Source
AI summary
The system includes a processor and memory. The processor is configured to receive sensor data set and video data set associated with a vehicle; determine, using a reduction model, a compressed video data set based at least in part on the sensor data set and the video data set; and transmit or store the compressed video data set. The memory coupled to the processor and configured to provide the processor with instructions.