Video fusion performance evaluating method based on high-order singular value decomposition

Abstract

The invention discloses a video fusion performance evaluating method based on high-order singular value decomposition. The video fusion performance evaluating method based on the high-order singular value decomposition mainly solves the problem that the fusion performance of video images containing noise cannot be evaluated in the prior art. The video fusion performance evaluating method based on the high-order singular value decomposition comprises the implementation steps that two registered reference videos and a registered fusion video are input respectively; a four-order tensor is formed by the input videos, the high-order singular value decomposition is carried out on the four-order tensor, and respective space geometrical characteristic background images and respective time motion characteristic images are obtained; the time motion characteristic images are divided into motion target areas and noise areas through a thresholding method; then, different evaluation indexes are designed respectively to evaluate all the areas; lastly, an overall performance evaluation index is structured through power exponent multiplication, and thus evaluation on the whole fusion performance of the video images is achieved. The video fusion performance evaluating method based on the high-order singular value decomposition can effectively, accurately and objectively evaluate the fusion performance of the videos in a noise environment, and can be used for monitoring fusion video image quality.

Description

technical field [0001] The invention relates to the field of video image processing, in particular to a noise-containing video fusion performance evaluation method, which can effectively evaluate the video fusion performance in a noisy environment, and can be used for monitoring the fusion video image quality. Background technique [0002] As image fusion technology is widely used in the fields of target tracking, detection and machine vision, image fusion performance evaluation is becoming more and more important. At present, most image fusion performance evaluation indicators are for static image fusion methods. But there are less about video image fusion methods. Video image fusion not only needs to meet the basic requirements of general image fusion in terms of spatial performance, that is, each frame image in the fused video image should not only retain the useful information in each frame image of the input video image as much as possible, but also avoid introducing fa...

AI Technical Summary

Problems solved by technology

However, these algorithms are the same as the static image fusion performance evaluation indicators. They are mainly designed for clean video image fusion, but in fact, the video is easily disturbed by noise during acquisition or transmission.

However, the existing video fusion performance evaluation indicators all mistake noise as important information. When evaluating the noise-suppressed video fusion algorithm, it will be considered that important information has been lost, thus obtaining inconsistent or even opposite results with subjective evaluation results.

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