Image quality evaluating method based on support vector machine

Abstract

The invention provides an image quality evaluating method based on a support vector machine. The method comprises the following steps: first, a preprocessed image sample is selected and extracted according to characteristic value, a processed sample set is respectively divided into a training set and a testing set; secondly, the training set is used for training the support vector machine, the number of the support vector machine is ensured according to a certain level which is required by a system, thus ensuring each support vector machine to be trained, wherein, an input sample is the characteristic value of the image and an output sample is the level of the image quality; thirdly, the trained support vector machine is used for adjusting and optimizing correlation parameters with the testing set and determining the parameter of the decision function of the optimal hyperplane of the support vector machine model; and finally, the support vector machine model which is trained and optimized is used for evaluating the quality level of the image sample. The invention has the advantages of little required sample, fast arithmetic speed, high precision, good performance, strong popularization, etc.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image quality evaluation method based on a support vector machine. Background technique [0002] Correct evaluation of image quality is a very meaningful research topic in the field of image information engineering. Image quality evaluation methods are generally divided into two categories: subjective and objective. The image is ultimately viewed by people, so the most accurate evaluation method for its quality is subjective evaluation, but there are many problems in the practical application of subjective evaluation methods, so people are unremittingly devoting themselves to designing objective evaluation methods to approximately reflect the subjective feeling requirements. [0003] At present, the grading estimation and evaluation of remote sensing images based on the degree of interpretation has been carried out internationally. Some countries have for...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to address the deficiencies in the prior art: slow processing, poor reliability, poor generalization ability, subjective evaluation and objective measurement inconsistent shortcomings, propose a kind of image based on Support Vector Machine (SVM, full name Support Vector Machine) Quality Evaluation Method

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