Compressed Video Object Blurring via Selective Key Frame Processing
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Solution Overview
Problem
Current systems for object blurring in compressed videos are inefficient in terms of computational and storage usage, and increase system complexity without maintaining video quality.
Innovation Solution
A system and method for object blurring in compressed videos that includes sensors, human-machine interfaces, and control modules with specific control logics for encoding, decoding, and post-processing to identify and obfuscate protected objects within video frames, reducing computational and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If videos are decoded prior to image processing for object blurring, then object obfuscation can be applied, but computational complexity and storage usage increase
Solution Approach 1:
The patent extracts only the essential independent frames from the compressed video stream for processing, rather than fully decoding the entire video. This allows object detection and blurring to be applied selectively to key frames while maintaining the compressed format for the majority of frames, thereby reducing computational complexity while achieving effective object obfuscation.
Solution Approach 2:
The video stream is segmented into independent frames and dependent frames. Only independent frames are fully decoded and processed for object blurring, while dependent frames remain in compressed form. This segmentation approach reduces the overall computational burden while maintaining the effectiveness of object obfuscation in the reconstructed video stream.
2Ease of operation
If full video decoding is performed for post-processing, then image processing algorithms can be applied, but storage requirements increase
Solution Approach 1:
The patent extracts only the necessary independent frames from the compressed video data for post-processing operations. By working with a subset of frames rather than the complete decoded video, the system reduces storage requirements while maintaining the capability to apply image processing algorithms for object blurring where needed.
Solution Approach 2:
The system performs preliminary identification of independent frames within the compressed video stream before applying post-processing. This preliminary action allows the system to prepare only the necessary frames for processing, reducing storage usage by avoiding full video decoding while still enabling effective image processing algorithms to be applied to the extracted frames.
3Reliability
If re-encoding is performed after post-processing, then processed videos can be stored or transmitted, but system complexity increases
Solution Approach 1:
The system segments the video processing into distinct stages: independent frame extraction, post-processing application, and selective re-encoding. By processing only independent frames through the full decode-process-re-encode pipeline, the system reduces overall system complexity compared to processing entire video streams, while maintaining video quality through proper handling of the frame sequence.
4Adaptability or versatility
If multiple video streams are supported, then application functionality increases, but encoder and decoder usage increases
Solution Approach 1:
The patent applies partial action by processing only independent frames from multiple video streams rather than fully decoding and processing every frame in every stream. This approach maintains the versatility of supporting multiple video streams while significantly reducing encoder and decoder usage by limiting processing to essential frames that carry the most information.
Data Source
AI summary
A system for object blurring in compressed videos includes sensors, human machine interfaces (HMIs), and control modules. Each control module has a processor, memory, and input/output (I/O) ports. The control modules communicate with the sensors and the HMIs via the I/O ports. The memory stores control logic that is executed by the processor. The control logic receives raw image data from the sensors, encodes the raw image data into compressed image data and stores the compressed image data in memory. The control logic parses and partially decodes the compressed image data to access raw independent video frames and performs post-processing algorithms on the raw independent video frames to generate post-processed independent video frames. The post-processed independent video frames are encoded and replace raw independent video frames in the compressed image data. The control logic transmits an output including the encoded post-processed independent and dependent video frames to the HMI.