Interaction-Driven Video Clipping for High-Quality Short Videos
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Solution Overview
Problem
The challenge of creating high-quality short videos from long videos that accurately reflect user interests and preferences in the context of short video platforms is not adequately addressed by existing technologies.
Innovation Solution
A method and device for video clipping that determines interest points in a video based on user interaction behavior data, using a neural network model to predict optimal clipping start and end points, and adjusts the model parameters to enhance accuracy, followed by secondary clipping for improved quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video clipping methods are used, then the clipping process is simple and fast, but the generated short videos do not accurately reflect user interests and preferences
Solution Approach 1:
The system pre-processes user interaction behavior data to calculate interaction heat values for different time points in the video before actual clipping occurs. This preliminary analysis of user behavior patterns enables the clipping algorithm to identify optimal interest points in advance, ensuring accurate reflection of user interests when clipping is performed
Solution Approach 2:
The system uses user interaction behavior data (likes, comments, shares, watch time) as feedback to continuously optimize the clipping process. By analyzing how users actually interact with video content and adjusting clipping decisions based on this feedback, the system progressively improves its ability to generate short videos that accurately reflect user interests
2Manufacturing precision
If interest points are determined based on user interaction behavior data, then the quality of generated short videos improves, but the processing time and computational resources increase
Solution Approach 1:
Instead of analyzing every single user interaction data point, the system focuses on key interaction metrics and time points with significant user engagement. By selectively processing only the most relevant interaction data that substantially impacts video quality, the system achieves high-quality clipping while reducing overall processing time and computational burden
3Adaptability or versatility
If multiple clipping points are selected based on interaction heat, then more diverse short video content is generated, but the complexity of selecting optimal clipping points increases
Solution Approach 1:
The system transforms the complex multi-dimensional user interaction behavior data into a simplified parameter called 'interaction heat' that can be calculated for each time point in the video. This parameter transformation converts the difficult task of analyzing multiple interaction metrics into a straightforward process of selecting time points with the highest interaction heat values, making optimal point selection both diverse and manageable
Data Source
AI summary
Provided are a video clipping and model training method, relating to the field of video technologies, and in particular, to the field of short video technologies. The video clipping method includes: acquiring interaction behavior data for an original video file; determining interaction heat at respective time points of the original video file, according to the interaction behavior data; selecting N time points with highest interaction heat, to take the selected time points as interest points of the original video file, where N is a positive integer; and clipping the original video file based on the respective interest points, to obtain N clipped video files. Therefore, high-quality short video files can be generated.