Non-reference quality evaluation method for fuzzy distortion three-dimensional images

A stereoscopic image, fuzzy distortion technology, applied in the direction of image analysis, image data processing, instruments, etc., can solve the problems of high computational complexity and unsuitable application occasions, reduce computational complexity, avoid machine learning training process, and better consistent effect

Active Publication Date: 2014-07-09
山东立信华创信息科技咨询有限公司
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Problems solved by technology

The current no-reference quality evaluation usually uses machine learning to predict the evaluation model, which has high computational complexity, and the train

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  • Non-reference quality evaluation method for fuzzy distortion three-dimensional images
  • Non-reference quality evaluation method for fuzzy distortion three-dimensional images
  • Non-reference quality evaluation method for fuzzy distortion three-dimensional images

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Embodiment Construction

[0032] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] A no-reference quality evaluation method for fuzzy and distorted stereoscopic images proposed by the present invention, its overall realization block diagram is as follows figure 1 As shown, it includes two processes of training phase and testing phase: in the training phase, multiple original undistorted stereo images and corresponding blurred and distorted stereo images are selected to form the training image set, and the training image set is decomposed by using two-dimensional empirical mode Each fuzzy and distorted stereo image is decomposed to obtain the intrinsic mode function image, and then the non-overlapping block processing is performed on each intrinsic mode function image, and the visual dictionary table is constructed by using the K-means clustering method; The frequency response of each pixel in each original undistorted...

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Abstract

The invention discloses a non-reference quality evaluation method for fuzzy distortion three-dimensional images. In a training phase, multiple distortionless three-dimensional images and corresponding fuzzy distortion three-dimensional images are selected to constitute a training image set, the fuzzy distortion three-dimensional images are decomposed by means of bidimensional empirical mode decomposition to obtain intrinsic mode function images, and a vision dictionary table is constructed by means of a K mean value clustering method; objective evaluation metric values of pixels in the fuzzy distortion three-dimensional images are obtained, so that a visual quality table is constructed; in a testing phase, three-dimensional images to be tested are decomposed through bidimensional empirical mode decomposition to obtain intrinsic mode function images, and then image quality objective evaluation predicted values of the images to be tested are obtained according to the vision dictionary table and the visual quality table. The method has the advantages that complicated machine learning and training processes are not needed in the training phase, the image quality objective evaluation predicted values can be obtained only through a simple vision dictionary search process in the testing phase, and consistency of subjective evaluation values is good.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a no-reference quality evaluation method for blurred and distorted stereoscopic images. Background technique [0002] With the rapid development of image coding technology and stereoscopic display technology, stereoscopic image technology has received more and more attention and applications, and has become a current research hotspot. Stereoscopic image technology utilizes the principle of binocular parallax of the human eye. Both eyes independently receive left and right viewpoint images from the same scene, and form binocular parallax through brain fusion, so as to enjoy stereoscopic images with a sense of depth and realism. . Compared with single-channel images, stereo images need to ensure the image quality of two channels at the same time, so it is very important to evaluate its quality. However, there is currently no effective objective evaluation method to evaluate t...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 邵枫王珊珊李福翠
Owner 山东立信华创信息科技咨询有限公司
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