ORB (object request broker) operator-based reference-free video smoothness evaluation method

Abstract

The invention relates to an ORB (object request broker) operator-based reference-free video smoothness evaluation method. The method comprises the following steps: taking continuous n frames of images of a video, and extracting ORB feature points from the n frames of images; matching the ORB feature points in every two adjacent frames of images; screening and removing the mismatched ORB feature points of two adjacent frames of images; generating an affine transformation matrix between every two adjacent frames of images, namely a homography matrix between every two adjacent frames of images, according to a motion affine transformation model, and gradually performing iterative solution on the homography matrix A; calculating motion parameters between every two adjacent frames of images, wherein the motion parameters comprise translation and rotation angels of the images; generating a translational motion trajectory and a rotary motion trajectory of a video sequence; performing filtering processing on the translational motion trajectory and the rotary motion trajectory by utilizing a Gauss filter to obtain a filtered translational motion trajectory and a filtered rotary motion trajectory; calculating a transitional jitter number and a rotary jitter number, calculating a horizontal smoothness value and a rotary smoothness value, and judging the smoothness of the video.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a video smoothness detection method. Background technique [0002] Most of the existing video quality evaluation methods are developed on the basis of image quality evaluation methods. Generally, the image quality evaluation method is used to obtain the quality of each frame of video, and then each frame of video is weighted according to a certain standard. Finally, the quality of the entire video is obtained. The three types of methods are aimed at measuring physical parameters such as signal amplitude, timing relationship, and signal-to-noise ratio, and evaluating video quality through various algorithms to evaluate image blur, noise, and block effects. , but none of them have evaluated the degree of video jitter. The final receptor of the video is the human eye. Human eye observation is the most accurate method for evaluating video quality. Video jitter, that is, the smoothness...

AI Technical Summary

Problems solved by technology

[0002] Most of the existing video quality evaluation methods are developed on the basis of image quality evaluation methods. Generally, the image quality evaluation method is used to obtain the quality of each frame of video, and then each frame of video is weighted according to a certain standard. Finally, the quality of the entire video is obtained. The three types of methods are aimed at measuring physical parameters such as signal amplitude, timing relationship, and signal-to-noise ratio, and evaluating video quality through various algorithms to evaluate image blur, noise, and block effects. , but none of them have evaluated the degree of video jitter. The final receptor of the video is the human eye. Human eye observation is the most accurate method for evaluating video quality. Video jitter, that is, the smoothness of the video, is another indicator of video quality. Important indicators are also directly related to people's feelings about video. Therefore, this paper proposes a detection method without reference to video smoothness, which can be used to improve and supplement the evaluation of video quality through the detection and evaluation of video smoothness.

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