Video Compression Metadata Handling for Autonomous Driving
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Solution Overview
Problem
Standard video compression algorithms used in autonomous driving vehicles are not optimized for post-processing, lacking support for machine perception processes and requiring complex extensions to facilitate efficient video data processing for navigation and control operations.
Innovation Solution
A method involving metadata processing, where timestamps and other metadata are appended to, extracted from, and reattached to video frames during compression, enabling efficient video compression and post-processing for machine perception in autonomous driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard video compression algorithms are used for autonomous driving vehicles, then video data can be compressed for storage and transmission, but the algorithms do not support post-processing requirements for machine perception processes
Solution Approach 1:
The video data is segmented into individual frames, and metadata is appended to each frame independently. This allows standard compression algorithms to process each frame while preserving the ability to perform post-processing operations on specific frames without affecting the entire video stream.
Solution Approach 2:
Metadata is introduced as an intermediary element that carries timing and synchronization information between the compressed video frames and the machine perception processes. This metadata layer enables post-processing operations without requiring modifications to the standard compression algorithm itself.
2Loss of energy
If video compression is applied to reduce data size, then storage and transmission efficiency improve, but time synchronization and metadata preservation become more difficult
Solution Approach 1:
Metadata including timestamps is appended to video frames before compression. This preliminary action ensures that the metadata is preserved through the compression process and is available for post-processing operations, eliminating the need for separate metadata transmission channels.
Solution Approach 2:
The metadata is nested within the video frame data structure, allowing it to be transmitted and stored together with the compressed video data. This nesting ensures that metadata remains associated with its corresponding video frames throughout the compression, transmission, and decompression process.
3Adaptability or versatility
If complex extensions are made to video compression standards to support ADV features, then post-processing capability improves, but system complexity and compatibility issues increase
Solution Approach 1:
The solution uses universally supported video compression standards without requiring proprietary extensions. By appending metadata to individual frames and using standard compression algorithms, the system achieves multi-functionality - working with both standard video players and advanced driver assistance systems without requiring different compression formats.
Data Source
AI summary
A method to perform video compression for ADV is disclosed. The method receives multiple frames of image data from multiple cameras. Metadata are appended to each frame of the image data to generate one of multiple frames of uncompressed image data as the image data are received. The frames of uncompressed image data may be stored. To compress the image data later, the method retrieves the frames of uncompressed image data, extracts the metadata from each frame of the uncompressed image data to generate one of multiple frames of processed image data. The method compresses each frame of the processed image data with the metadata extracted to generate one of multiple frames of compressed image data. The method reattaches the metadata to a corresponding frame of the compressed image data to generate one of multiple compressed image frames. The metadata supports time synchronization and error handling of the image data.