Static Image Detection Method for OLED Screens
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Solution Overview
Problem
Existing methods for detecting static images on OLED screens suffer from low accuracy and high calculation requirements, making them inefficient for determining whether an input image is static.
Innovation Solution
A detection method that first determines if a series of frame images meet a static image brightness requirement and then checks a gray histogram condition, updating the reference frame as necessary, to comprehensively assess image staticness with reduced computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the way by calculating average brightness difference is adopted for determining static image, then the calculation amount is reduced, but the detection accuracy is low
Solution Approach 1:
The patent segments the static image detection process into two distinct stages: first calculating average brightness difference for quick preliminary filtering, then calculating gray level difference only for images that pass the first stage. This segmentation allows the system to benefit from the low calculation amount of brightness difference while ensuring high detection accuracy through gray level difference verification, thus resolving the contradiction between detection accuracy and calculation amount.
2Measurement precision
If the way by calculating gray level difference is adopted for determining static image, then the detection accuracy is improved, but the calculation amount increases and requires high performance computing capabilities
Solution Approach 1:
The patent applies partial action by performing complete gray level difference calculation only on a subset of images that have already passed the brightness difference filter. For most images, only the lighter brightness difference calculation is performed. This partial application of the more accurate but computationally intensive method significantly reduces the overall computing capability requirement while maintaining high detection accuracy for images that require it.
Data Source
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.