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A Machine Learning-Based Evaluation Method for Color Correction

A machine learning and color correction technology, applied in the field of image processing and computer vision, can solve problems such as unsatisfactory results, achieve good use value, improve accuracy and high consistency

Inactive Publication Date: 2017-03-08
FUZHOU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the quality of color correction results is related to many factors, and the effect of simply using a few factors for evaluation is not ideal

Method used

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  • A Machine Learning-Based Evaluation Method for Color Correction
  • A Machine Learning-Based Evaluation Method for Color Correction
  • A Machine Learning-Based Evaluation Method for Color Correction

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] The present invention provides a color correction evaluation method based on machine learning, such as figure 1 , 2 shown, including the following steps:

[0040] Step S1: Input the reference image and the target image, the target image is the distorted image, use the full reference image quality assessment method based on image registration to extract the features of the target image, and obtain the feature set F1.

[0041] In this example, if image 3 As shown in , the feature extraction of the target image is performed using the full-reference image quality assessment method based on image registration, which specifically includes the following steps:

[0042] Step S11: use the image registration algorithm SIFT Flow to perform image registration on the reference image and the target image, and generate a matching image as a new reference im...

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Abstract

The present invention relates to a color correction evaluation method based on machine learning, comprising the following steps: S1: input a reference image and a target image, the target image is a distorted image, and use a full-reference image quality evaluation method based on image registration to perform an evaluation on the target image Feature extraction, to obtain feature set F1; S2: Use the image retargeting evaluation method to perform feature extraction on the target image to obtain feature set F2; S3: Synthesize feature sets F1 and F2, use it as the feature set F of the machine learning algorithm, and pass The machine learning algorithm and the third-half cross-validation method learn to obtain an objective evaluation model; S4: use the objective evaluation model to objectively evaluate the target image, and obtain the final quality evaluation score of the target image. This method can effectively evaluate the color consistency between images, and has a high correlation and accuracy with the user's subjective perception.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a machine learning-based color correction evaluation method consistent with subjective perception. Background technique [0002] Ensuring color consistency between images is of great significance in the fields of image / video stitching and color correction of left and right views of three-dimensional images / videos. During the image / video stitching process, the color difference of the image will lead to obvious stitching traces in the generated panorama; the color difference between the left and right 3D views will not only reduce the performance of the post-processing of the 3D stereoscopic image / video, but also affect the user experience, resulting in 3D visual fatigue. In order to solve the problem of color differences between images, color correction algorithms are proposed. The color correction algorithm is used to correct the color difference b...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/33G06T7/90G06K9/46
CPCG06T2207/20081G06T2207/10004G06T2207/10024G06T2207/30168G06V10/462
Inventor 牛玉贞张海锋郭文忠陈羽中
Owner FUZHOU UNIV