Video Frame Selection for Bandwidth Reduction
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Solution Overview
Problem
The challenge is to reduce bandwidth consumption and processing latency associated with uploading and processing large amounts of video data captured by electronic devices, which can lead to increased upload time and processing burden.
Innovation Solution
The system selectively uploads and processes sampled video data, using a lower sampling frequency to generate preview data, and employs image feature extraction to discard frames with poor image quality and determine transition points, allowing for efficient selection and annotation of video segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all captured video data is uploaded to the server for processing, then complete video processing can be performed, but bandwidth consumption and upload time increase significantly
Solution Approach 1:
The patent extracts and processes only the most representative frames (key frames and transition frames) from the video data rather than processing all frames. This selective extraction reduces the volume of data that needs to be uploaded and processed while maintaining the essential information needed for video understanding and processing.
Solution Approach 2:
The patent segments video data into distinct frames and identifies key frames based on transition detection. By segmenting the video and selecting only important segments (key frames), the system reduces processing requirements while preserving critical video content information.
2Loss of information
If all captured video data is uploaded to the server, then no content is lost, but processing burden and latency increase
Solution Approach 1:
The system extracts key frames and transition frames from the video sequence, uploading only these selected frames to the server for processing. This extraction approach maintains essential video content information while significantly reducing the amount of data that requires processing, thereby reducing latency.
Solution Approach 2:
The patent performs preliminary frame selection and transition detection on the device side before uploading to the server. By pre-processing the video data to identify and select only key frames locally, the system reduces the processing burden on the server and accelerates the overall processing pipeline.
3Loss of energy
If video data is heavily compressed to reduce upload size, then bandwidth consumption decreases, but image quality deteriorates
Solution Approach 1:
Instead of compressing all video frames, the system extracts only key frames and transition frames for upload. This selective extraction maintains high image quality for the uploaded frames while reducing overall data transmission requirements, avoiding the quality loss associated with heavy compression.
Solution Approach 2:
The patent applies different quality levels to different parts of the video data by uploading full-resolution key frames and transition frames while potentially using lower resolution or no transmission for non-key frames. This local quality approach ensures high quality where it matters most while reducing overall bandwidth consumption.
Data Source
AI summary
A system and method for selecting portions of video data from preview video data is provided. The system may extract image features from the preview video data and discard video frames associated with poor image quality based on the image features. The system may determine similarity scores between individual video frames and corresponding transition costs and may identify transition points in the preview video data based on the similarity scores and/or transition costs. The system may select portions of the video data for further processing based on the transition points and the image features. By selecting portions of the video data, the system may reduce a bandwidth consumption, processing burden and/or latency associated with uploading the video data or performing further processing.