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No-reference image quality evaluation method based on channel attention

A technology of image quality evaluation and attention, applied in the field of image processing, can solve the problem of low prediction accuracy of distorted image quality, achieve high-precision prediction and satisfy visual experience

Pending Publication Date: 2022-07-29
渭南日报社印刷厂 +1
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Problems solved by technology

[0005] The purpose of the present invention is to provide a non-reference image quality evaluation method based on channel attention, which solves the problem of low prediction accuracy of distorted image quality in the prior art

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  • No-reference image quality evaluation method based on channel attention
  • No-reference image quality evaluation method based on channel attention
  • No-reference image quality evaluation method based on channel attention

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Embodiment Construction

[0035] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] A reference-free image quality assessment method based on channel attention includes the following steps:

[0037] Step 1. Preprocess the images in the database to maintain a uniform size, input the preprocessed images into the ResNet50 network, and perform feature extraction through layers 1-4 of the ResNet50 network to obtain multi-level features F. i , the feature extraction method is as follows:

[0038] F i =f(W i *X) (1);

[0039] In the above formula, X represents the input image, W i represents the overall parameters of each layer of the network, and f( ) represents the feature extraction of the image.

[0040] Traditional convolutional networks or fully connected networks will have more or less information loss, loss and other problems during information transmission, and will also lead to gradient disappearance or gradient e...

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Abstract

The invention discloses a no-reference image quality evaluation method based on channel attention, and the method comprises the steps: inputting an image into a ResNet50 network, carrying out the feature extraction through the lay1-4 layers of the ResNet50 network, and obtaining four features; respectively inputting each feature into a channel attention layer, and obtaining the feature of a region of interest of the image; inputting the features of each concerned area into a feature enhancement module to obtain enhanced features; fusing the four enhanced features, and fusing the features; and inputting the fusion features into a quality prediction network for quality prediction to obtain a prediction result. The features of the concerned area are obtained through an attention mechanism, and the visual perception of human eyes is met; the low-level features and the high-level features are effectively combined through the feature fusion module, detail information is enhanced while global information is represented, multi-scale information is obtained, and high-precision prediction is achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a reference-free image quality evaluation method based on channel attention. Background technique [0002] With the rapid development of multimedia, image processing and communication technologies, digital images, as one of the most intuitive and effective information carriers, transmit important visual signals and are widely used in all aspects of life. However, in the process of image acquisition, compression, storage, and transmission, due to unavoidable factors, image distortion will lead to image quality degradation, such as: shooting jitter, uneven exposure and other problems will cause image quality degradation. Image quality has a great impact on the acquisition of human visual information. If the image quality is low, it will not only affect the perception effect, but also cannot accurately capture useful information. Therefore, it is of great significance to eff...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06V10/40G06V10/80G06V10/25G06K9/62G06V10/82G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/30168G06T2207/20081G06T2207/20084G06N3/048G06N3/045G06F18/253
Inventor 钟崇军解博刘春霞郑元林
Owner 渭南日报社印刷厂
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