Non-reference image quality objective evaluation method based on deep learning

A technology for objective quality evaluation and reference images, applied in image analysis, image communication, image data processing, etc., can solve the problems that classic deep learning models cannot directly apply image quality evaluation, are not accurate enough, and have high time complexity

CN105208374AActive Publication Date: 2015-12-30NINGBO UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2015-12-30

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Abstract

The invention discloses a non-reference image quality objective evaluation method based on deep learning. Multi-resolution pyramid and Gaussian difference decomposition is performed on distortion images to be evaluated and then natural statistical characteristics can be extracted by performing simple local normalization on sub-band images without extracting characteristics from a transform domain so that complexity is greatly reduced. Degree of distortion of the images is measured by degree of loss of the natural statistical characteristics with no requirement for reference images or distortion types. The change condition of visual quality of the images under the influence of various image processing and compression methods can be objectively reflected by the method, and evaluation performance of the method is not influenced by the image content or the distortion types and is consistent with subjective perception of human eyes. An existing L moment estimation method is adopted to estimate the distribution parameters of the envelope curve of a gray level histogram, and the distribution parameters obtained through estimation are more accurate and have higher generalization capability.
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Description

technical field

[0001] The present invention relates to an image quality evaluation method, in particular to an objective evaluation method of image quality without reference based on deep learning. Background technique

[0002] Image quality assessment (image quality assessment, IQA) is an indispensable part in many image processing applications. The objective image quality evaluation model is an algorithm that can automatically predict the degree of image distortion, and is usually used to monitor multimedia services to ensure that end users obtain satisfactory quality of experience. According to whether the original reference image is available, objective image quality assessment can usually be divided into three categories, namely full reference image quality assessment, semi-reference image quality assessment, and blind image quality assessment (BIQA). The no-reference image quality assessment method, which can predict the perceived quality of an image without a refere...

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Embodiment Construction

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0028] Since reference images cannot be obtained in many applications, the no-reference image quality assessment method is the most practical and challenging research topic, and the traditional no-reference image quality assessment has high computational complexity and time complexity, while the agreement between the objective quality of predictions and subjective perception is poor. The present invention extracts natural statistical features in the spatial domain by decomposing images, and the time complexity is very low. At the same time, multi-resolution pyramid and Gaussian difference decomposition can be used to perform multi-resolution analysis and multi-scale texture analysis on images, thereby extracting better natural statistics. feature; before the traditional shallow learning algorithm returns, the present invention adds a deep expr...