Video Cropping via Detection Box Cost Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video cropping methods, such as center cropping, often result in the loss of important information due to the randomness of important information positions in raw videos, leading to poor practicability and user experience.
Innovation Solution
A video cropping method that determines a cropping box by dividing an image frame into detection boxes and calculating costs based on importance score, coverage area, and smoothing distance to select the detection box with the minimum cost, ensuring important information is preserved and reducing lens movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If center cropping is used to crop the raw video, then the cropping process is simple, but important information in the video may be lost due to randomness of information position
Solution Approach 1:
The image frame is divided into multiple detection boxes, each representing a potential cropping region. This segmentation allows the system to evaluate multiple candidate regions instead of using a fixed center cropping approach, thereby preserving important information while maintaining process simplicity.
Solution Approach 2:
The system changes the cropping parameter from a fixed center position to multiple candidate positions represented by detection boxes. By calculating costs based on importance scores, coverage areas, and smoothing distances, the system dynamically selects the optimal cropping region, avoiding information loss while keeping the process straightforward.
2Loss of information
If multiple candidate clipping boxes are generated and scored to select the best clipping box, then important information is preserved, but the cropping process becomes complex
Solution Approach 1:
The system performs preliminary detection to identify potential detection boxes before the actual cropping decision. By pre-calculating importance scores, coverage areas, and smoothing distances for multiple candidate boxes, the system prepares optimal cropping candidates in advance, simplifying the final selection process while ensuring information preservation.
Solution Approach 2:
The system uses cost calculation as a feedback mechanism to evaluate each detection box. The cost function incorporates importance scores, coverage areas, and smoothing distances, providing quantitative feedback that guides the selection of the optimal cropping box. This feedback-driven approach simplifies the decision-making process while maintaining high information preservation.
3Adaptability or versatility
If the cropping box position changes frequently between frames, then the cropping can adapt to important information, but lens movement increases affecting user experience
Solution Approach 1:
The system dynamically adjusts the cropping box position based on the detected important information in each frame. By calculating the cost for each detection box considering smoothing distance to the previous frame's cropping box, the system achieves adaptive cropping while controlling excessive position changes, balancing adaptability with user experience.
Solution Approach 2:
The system incorporates smoothing distance calculation as a cushioning mechanism to prevent abrupt cropping box position changes. By considering the distance to the previous frame's cropping box in the cost function, the system prepares for and mitigates excessive lens movement, ensuring smooth transitions while maintaining adaptability to important information.
Data Source
Figure 1~3
Figure 4
AI summary
Provided are a video cropping method and apparatus, a device, and a storage medium. The method includes: obtaining at least one detection box of a first image frame; determining, based on at least one of an importance score, a coverage area, or a smoothing distance of any detection box in the at least one detection box, a cost of the detection box; determining a first detection box having a minimum cost among the at least one detection box as a cropping box; and cropping the first image frame based on the cropping box. Based on a cost of each detection box, the first detection box having the minimum cost among the at least one detection box is determined as the cropping box to crop the first image frame, which can not only improve flexibility of video cropping, but also improve a cropping effect while simplifying the video cropping process.