Machine learning-based stereoscopic image quality objective assessment method

A technology for objective quality evaluation and stereoscopic images, which is applied in image analysis, image data processing, instruments, etc., and can solve the problems of small parallax, difficult and accurate evaluation methods, and low applicability.

Active Publication Date: 2012-10-24
TSINGHUA UNIV
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Problems solved by technology

Since HVS is a complex nonlinear system, it is difficult to establish an accurate evaluation method based on it; in addition, for stereoscopic images, which are different from planar images, there is a high degree of correlation between adjacent viewpoints in stereoscopic images. The image quality of adjacent viewpoints is high,

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  • Machine learning-based stereoscopic image quality objective assessment method
  • Machine learning-based stereoscopic image quality objective assessment method

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[0014] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0015] The present invention provides a kind of stereoscopic image quality objective evaluation method based on machine learning, and it comprises the following steps:

[0016] S1: extracting parameters based on the objective evaluation of the image quality of the stereo image;

[0017] S2: Use the parameters extracted from the standard image sequence in the image library to perform machine learning;

[0018] S3: Using the learning results to fit the stereoscopic image quality evaluation results with the extracted parameters;

[0019] ...

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Abstract

The invention provides a machine learning-based stereoscopic image quality objective assessment method, which comprises the following steps of: extracting parameters based on which the image quality objective assessment of stereoscopic images is performed; performing machine learning by using the parameters extracted from standard image sequences in an image library; performing fitting between the stereoscopic image qualitative assessment results and the extracted parameters by using the learning results; and applying the fitting results to an image to be assessed and comparing the results with subjective assessment marks. The machine learning-based stereoscopic image quality objective assessment method provided by the invention has the beneficial effect that based on visual characteristics of human eyes, improvements on the form and weighted values of the formula for solving PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity ) are achieved, and a computation method of visual comfort and an assessment method using fused image parameters are set forth, and as joint fitting is performed by using a plurality of characteristic parameters, the fitting effect is improved.

Description

technical field [0001] The invention relates to the technical field of computer multimedia, in particular to an objective evaluation method of stereoscopic image quality based on machine learning. Background technique [0002] During the process of image data collection, compression processing, transmission, reconstruction, and image communication, the processed image will be different from the original image. How to evaluate these differences and the impact of these differences on image effects is the key to image quality evaluation. main content. When evaluating image quality, people are the final recipients and judges of stereoscopic images. Therefore, the stereoscopic information processing mechanism of HVS (Human Visual System) must be considered, and its perception process of stereoscopic information must be simulated. quality assessment model. [0003] At present, image quality evaluation methods can be divided into subjective evaluation methods and objective evalua...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 戴琼海马潇曹汛王好谦
Owner TSINGHUA UNIV
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