Video Stream Processing Buffering for Deep Learning Inference
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Solution Overview
Problem
Current video stream processing technologies, such as FFmpeg, face complexity and reduced speed due to the dispersion of processing across different software modules and the need to save and re-read video frames as images for deep learning processing.
Innovation Solution
A method involving a video frame processing model that preprocesses and adapts video frames, stores them in a buffer area, processes them in batches based on decoding duration, and generates a new video stream, reducing software complexity and improving processing speed by eliminating the need to save frames as images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video frames are saved as images frame by frame and stored in a disk for deep learning processing, then deep learning models can process video frames, but the software complexity increases and processing speed decreases
Solution Approach 1:
The patent merges the video decoding, buffer storage, and deep learning processing functions into a single integrated processing pipeline. The video decoding device directly outputs frames to a buffer that is immediately accessible by the deep learning processing unit, eliminating the need for separate file I/O operations and multiple software modules. This integration reduces software complexity while maintaining deep learning processing capability.
Solution Approach 2:
The patent introduces a buffer area as an intermediary data structure that holds video frames in memory during processing. This buffer acts as a mediator between the video decoding unit and the deep learning processing unit, allowing efficient data transfer without requiring frame-by-frame file operations. The buffer enables batch processing and reduces the complexity of coordinating between different processing stages.
2Adaptability or versatility
If video frames are saved as images frame by frame and stored in a disk, then deep learning models can process video frames, but the processing speed decreases
Solution Approach 1:
The patent performs preliminary actions by pre-allocating a buffer area in memory and establishing the data flow path from video decoding to deep learning processing before actual processing begins. This preliminary setup eliminates the need for dynamic file creation and I/O operations during processing, significantly improving processing speed while maintaining deep learning capability.
Solution Approach 2:
The patent ensures continuous processing by maintaining video frames in a memory buffer that is continuously fed from the video decoding unit and continuously processed by the deep learning unit. This eliminates the stop-start nature of file-based processing, where each frame must be written to and read from disk, thereby maintaining high processing speed throughout the entire video sequence.
3Adaptability or versatility
If video frames are processed by saving as images and re-encoding, then deep learning processing can be performed, but time is lost due to multiple I/O operations
Solution Approach 1:
The patent extracts the time-consuming file I/O operations from the processing pipeline by keeping all video frames in memory throughout the entire processing sequence. Only the essential deep learning processing remains, while the redundant steps of writing frames to disk and reading them back are completely removed, significantly reducing total processing time.
Data Source
AI summary
Provided are a video stream processing method and apparatus, an electronic device and a computer-readable medium, which relate to the field of Internet technology. An exemplary method includes: storing a video frame in an original video stream in a first buffer area by calling a video stream processing interface of a video stream processing tool; processing the video frame in the first buffer area by means of a video frame processing model to obtain a processed video frame; and on the basis of the processed video frame, generating a standard video stream corresponding to the original video stream. By means of the embodiments of the this application, the software complexity of video stream processing is effectively reduced, and the speed of video stream processing is effectively improved.