Three-dimensional image quality objective evaluation method based on sparse representation

Abstract

The invention discloses a three-dimensional image quality objective evaluation method based on sparse representation. According to the method, in a training stage, left viewpoint images of a plurality of original undistorted three-dimensional images are selected for forming a training image set, Gaussian difference filtering is adopted for carrying out filtering on each image in the training image set to obtain filtered images in different scales, and in addition, a K-SVD method is adopted for carrying out dictionary training operation on a set formed by all sub blocks in all of the filtered images in different scales for constructing a visual dictionary table; and in a test stage, the Gaussian difference filtering is performed on any one tested three-dimensional image and the original undistorted three-dimensional image to obtain filtered images in different scales, then, the filtered images in different scales is subjected to non-overlapped partition processing, and an image quality objective evaluation prediction value of the tested images is obtained. The three-dimensional image quality objective evaluation method has the advantages that a complicated machine learning training process is not needed in the training stage; and the in the test stage, the image quality objective evaluation prediction value only needs to be calculated through a sparse coefficient matrix, and in addition, the consistency with the subjective evaluation value is better.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective evaluation method of stereoscopic image quality based on sparse representation. 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 evalu...

AI Technical Summary

Problems solved by technology

At present, the existing method is to predict the evaluation model through machine learning, but its computational complexity is high, and the training model needs to predict the subjective evaluation value of each evaluation image, which is not suitable for practical applications and has certain limitations.

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