High-speed monitoring video quality detection method
A quality inspection method and video monitoring technology, applied in television, electrical components, image communication, etc., to achieve the effect of improving video clarity, improving overall quality, and ensuring clarity and integrity
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Embodiment 1
[0041] This embodiment discloses a high-speed surveillance video quality detection system, including a black screen detection module, an occlusion detection module, a blur detection module, an abnormal brightness detection module, an abnormal chromaticity detection module, and a streak noise detection module;
[0042] The black screen detection module is used to detect whether there is a black screen phenomenon in the video image. The detection process is to first grayscale the video image, and then count the proportion of dark pixels in the total pixels. Pixels whose gray value is less than T1 are called dark pixels. , and finally determine whether the screen is black by setting a threshold. That is, the threshold is set according to a large amount of previous data. When judging a black screen, the ratio of dark pixels to the total pixels is compared with the set threshold. If the ratio is greater than the set threshold, it is a black screen. Otherwise, it is not a black scree...
Embodiment 2
[0049] This embodiment discloses a method for detecting the quality of a high-speed surveillance video, comprising the following steps:
[0050] S01), black screen detection, specifically:
[0051]1) Convert the image to grayscale, and calculate the grayscaled value of the pixel by the formula Gray=R*0.299+G*0.587+B*0.114. R, G, and B are the three channels of the pixel, and the pixel is grayscaled The pixels with the final value smaller than T1 are called dark pixels, and then calculate the ratio of dark pixels to the total pixels rate, rate=blackNum / totalNum, blackNum is the total number of dark pixels, totalNum is the total number of pixels, totalNum=gray.rows*gray. cols, gray.rows indicates the height of the image, gray.cols indicates the width of the image, and T1 is the empirical value for judging dark pixels;
[0052] 2) Set the contrast threshold T, compare the ratio rate of dark pixels to the total pixels with the contrast threshold T, if rate>T, the current image is...
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