Content Segment Selection for Video Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile terminals often capture content with poor brightness, shake, and blur due to limited camera sensors and unfavorable recording conditions, making it difficult to improve image quality without increasing noise and removing shake and blur effectively.
Innovation Solution
A method and apparatus that select content segments based on contextual similarity levels, brightness levels, blur levels, and shake levels to produce a resultant video with improved brightness and reduced shake and blur, while maintaining a desired field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If post-processing techniques are used to improve brightness, then brightness levels can be enhanced, but random noise increases
Solution Approach 1:
The system performs preliminary selection of content segments based on brightness levels, blur levels, and shake levels before final video assembly. By pre-filtering segments with adequate brightness and low noise in the selection criteria, the system avoids the need for aggressive post-processing that would amplify noise, thus resolving the contradiction between brightness enhancement and noise increase.
2Manufacturing precision
If multiple content segments are selected and combined, then video quality improves, but device complexity increases
Solution Approach 1:
The system divides the video content into discrete segments and evaluates each segment independently based on brightness, blur, and shake characteristics. This segmentation approach allows for quality improvement by selecting only the best segments while maintaining manageable system complexity through automated segment-level analysis and selection algorithms.
Solution Approach 2:
The system changes the evaluation parameters from a single quality metric to multiple parameters (brightness level, blur level, shake level) that are measured and compared across segments. By establishing threshold values for these parameters, the system automates the selection process, improving video quality without proportionally increasing complexity.
3Area of stationary object
If content segments with different fields of view are selected, then more content coverage is achieved, but contextual consistency decreases
Solution Approach 1:
The system applies different selection criteria to different segments based on their contextual characteristics. By evaluating brightness, blur, and shake levels locally for each segment and comparing contextual characteristics to determine similarity levels, the system maintains contextual consistency within each segment while allowing overall content coverage to expand through selective inclusion of diverse segments.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method, apparatus and computer program product select content segments based at least in part on a contextual similarity level between the content segments and at least one of brightness levels, blur levels, and shake levels of the content segments. As such, a resultant video may be produced that comprises selected content segments. Accordingly, brightness levels of the content may be improved and shake and blur levels reduced while maintaining a desired field of view.