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

A smoothness and video technology, which is applied in the field of image processing and can solve problems such as evaluation of video jitter.

Inactive Publication Date: 2014-04-02
WUHAN INSTITUTE OF TECHNOLOGY
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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

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

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[0050] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0051] Such as figure 1 As shown, a no-reference video smoothness detection method includes the following steps:

[0052] 1) Take n consecutive frames of video images, extract the ORB feature points in the n frames of images; describe a certain image block through a binary string, and the construction method of the binary string is as follows:

[0053] f n ( p ) = Σ 1 ≤ i ≤ n 2 i ...

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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...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 卢培磊王海晖曾祥进陈青徐凯
Owner WUHAN INSTITUTE OF TECHNOLOGY
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