Video Still-Image Detection Using Brightness and Histogram Checks
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Solution Overview
Problem
Existing methods for detecting static images in videos suffer from low accuracy and high computational complexity, particularly in distinguishing still images from video stalls and black frames.
Innovation Solution
A detection method that combines average brightness difference calculation for initial frames with grayscale histogram comparison for subsequent frames to determine static images, reducing computational load while improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the way by calculating average brightness difference is adopted for determining static image, then the computational complexity is reduced, but the detection accuracy deteriorates
Solution Approach 1:
The patent divides the video frames into multiple regions (first region and second region) and performs separate brightness difference calculations for each region. This segmentation allows the system to focus computational resources on critical areas while maintaining overall detection accuracy, thus resolving the contradiction between reduced computational complexity and maintained detection accuracy.
Solution Approach 2:
The patent applies different detection strategies to different regions of the frame. By calculating brightness difference specifically in the first region (which may contain critical content) while using a different approach for the second region, the system achieves accurate static image detection without requiring full-frame high-precision analysis, thereby reducing overall computational complexity while maintaining necessary accuracy.
2Measurement precision
If the way by calculating gray level difference is adopted for determining static image, then the detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the frame into multiple regions and applies gray level difference calculation only to the first region rather than the entire frame. This selective application of the computationally intensive gray level analysis to only critical regions maintains detection accuracy where it matters most while significantly reducing the overall computational burden compared to full-frame analysis.
Solution Approach 2:
The patent performs partial gray level difference calculation only on the first region of the frame rather than the entire frame. This partial action provides sufficient detection accuracy for static image identification while avoiding the excessive computational complexity that would result from analyzing all pixels in the frame, thus resolving the contradiction between accuracy and computational load.
Data Source
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AI summary
Disclosed are a detection method for static image of a video, a detection terminal, and a computer-readable storage medium. The method includes: obtaining a frame image from the video as a reference frame image in a playback order, obtaining an image set comprising n frame images after the reference frame image in the playback order; sequentially determining whether first (n-1) frame images in the currently obtained image set satisfy a preset static image brightness requirement; determining whether a nth frame image in the image set satisfies a preset gray histogram judgment condition; determining whether a stop condition is satisfied according to the reference frame image and the nth frame image in the currently obtained image set; if the above determination results are yes, determining that a video image including the frame images from the reference frame image to the nth frame image in the current image set is a static image.