Shaking detection algorithm based on multi-feature fusion

Abstract

The invention relates to a shaking detection algorithm based on multi-feature fusion. The algorithm comprises the following steps of: initializing an undistorted video frame and a video frame to be detected; then, sequentially extracting a brightness similarity factor, a contrast similarity factor and a structure similarity factor to obtain a similarity metric function, wherein the contrast similarity factor is obtained after the average brightness is removed; and meanwhile, judging that the video frame of a signal to be detected does not shake, or judging that the video frame of the signal to be detected shakes. According to the shaking detection algorithm, the brightness similarity factor, the contrast similarity factor and the structure similarity factor of the video frame of a reference signal and the video frame of the signal to be detected are sequentially extracted, the similarities of the three kinds of extracted variable information are compared, and finally, the three comparison results are combined, so that a similarity index is obtained and used as an evaluation criterion for image quality, i.e., indicating whether shaking happens or not.

Description

technical field [0001] The invention belongs to the technical field of general image data processing or generation, and in particular relates to a shaking detection algorithm based on multi-feature fusion. Background technique [0002] With the development of modern society, computer technology advances by leaps and bounds, which provides reliable technical and hardware support for the realization of video surveillance system. As we all know, computer intelligent vision technology is widely used in many occasions because of its intuition, accuracy, timeliness and rich information content. However, with the gradual application of video surveillance systems, more and more problems have been exposed. Common types of video surveillance faults include: missing video signal, abnormal video clarity, abnormal video brightness, video noise, video snowflakes, video color cast, picture freeze, PTZ motion out of control, etc. If the maintenance work of the video surveillance system is ...

AI Technical Summary

Problems solved by technology

[0011] The technical problem solved by the present invention is that, since the algorithm in the prior art is basically a shake detection and evaluation method based on error statistics, it only considers the difference between image pixels one by one, and does not take into account the strong correlation and structure between image pixels The importance of visual objects, however, there is often a strong correlation between adjacent pixels in natural image signals, and these correlations provide important information about the visual content, which leads to the problem of being unable to accurately detect and analyze jitter, and then Provides an optimized shake detection algorithm based on multi-feature fusion

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