Adaptive Video Encoding for ADAS Event Clips Under Bitrate Variations
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Solution Overview
Problem
Managing video data from high-definition cameras in advanced driver assistance systems (ADAS) and autonomous vehicles (AV) is challenging due to storage size and bandwidth issues, particularly when encoding video for notable events, as conventional encoding methods fail to maintain sufficient fidelity and result in varying bitrates based on environmental conditions.
Innovation Solution
Adaptive quantization techniques are employed to adjust quantization parameters (QPs) based on visual differences between raw and encoded video data, using metrics like structural similarity index (SSIM) to maintain visual fidelity while reducing bitrate, especially under varying lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional fixed quantization parameter mode is used to encode video, then encoding complexity is reduced, but bitrate varies significantly (more than doubling) between daytime and nighttime conditions while maintaining insufficient visual fidelity
Solution Approach 1:
The patent implements dynamic quantization parameter adjustment by introducing a metric computation mechanism that evaluates visual differences between raw and encoded video frames. The system dynamically modifies QP values based on computed metrics, enabling adaptive encoding that responds to actual visual content characteristics rather than using fixed parameters. This resolves the contradiction by making the encoding process dynamic and responsive while maintaining computational feasibility through selective frame sampling and metric-based adjustments.
Solution Approach 2:
The patent changes encoding parameters adaptively by computing visual difference metrics and using them to adjust quantization parameters. The system calculates metrics such as structural similarity or perceptual differences between original and encoded frames, then modifies QP values accordingly. This parameter change approach ensures sufficient visual fidelity is maintained while achieving more consistent bitrate across varying environmental conditions compared to fixed QP mode.
2Quantity of substance
If constant or variable bitrate modes are used to encode video, then bitrate control is improved, but visual fidelity cannot be ensured for review purposes
Solution Approach 1:
The patent implements a feedback mechanism where the system encodes video frames, computes visual difference metrics between original and encoded frames, and uses this feedback to adjust quantization parameters for subsequent frames. This closed-loop approach ensures that visual fidelity requirements are met while achieving controlled bitrate. The feedback-driven adaptation allows the system to maintain sufficient quality for review purposes while managing bitrate effectively.
Solution Approach 2:
The patent employs dynamic quantization parameter adjustment based on real-time metric computation. Rather than using static constant bitrate or variable bitrate modes, the system dynamically adapts QP values based on computed visual differences. This dynamic approach ensures that bitrate is controlled while visual fidelity is maintained at levels sufficient for event review, resolving the contradiction between bitrate control and quality assurance.
3Reliability
If high-definition video from multiple cameras is stored to capture notable events, then event review capability is improved, but storage size and bandwidth utilization become significant challenges
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization parameters based on computed visual metrics. This adaptive encoding achieves more efficient compression by modifying QP values according to actual visual content characteristics and bitrate conditions. The result is reduced storage size for the same event review capability, as the system optimizes compression ratios while maintaining sufficient visual fidelity for notable event analysis.
Solution Approach 2:
The patent implements dynamic encoding that adapts to varying video content characteristics. By continuously computing visual difference metrics and adjusting encoding parameters in real-time, the system achieves more consistent and efficient compression across different scenes and conditions. This dynamic approach reduces overall storage requirements while preserving the ability to review notable events with adequate visual quality.
Data Source
AI summary
In various examples, visual differences between video data and an encoded version of the video data are used to determine updates to quantization parameters (QPs) used to encode the video data to store or upload video clips that corresponds to notable events associated with a machine. The video data may correspond to images applied to a machine learning model to perform control operations for the machine. A metric may be used to quantify the visual differences. To evaluate the visual differences, samples of the video data and encoded video data may be determined and analyzed, rather than entire images or frames. To selectively enable updates to QPs, the system may detect that a deviation between a bitrate corresponding to the encoded data and a reference bitrate has exceeded a threshold.