CN113362315A - Image quality evaluation method and evaluation model based on multi-algorithm fusion - Google Patents
Image quality evaluation method and evaluation model based on multi-algorithm fusion Download PDFInfo
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Abstract
The invention provides an image quality evaluation method based on multi-algorithm fusion, which comprises the following steps: extracting the distortion type and the image content information by using a feature extractor, and obtaining a probability vector after transformation as input and information output of other modules; calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image; carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and image content information, and outputting image content adaptive quality evaluation prediction scores at different distortion prior times; and according to the distortion type information, weighting is given to the prediction scores under different distortion priors, and the quality evaluation prediction score with distortion type adaptability is obtained and serves as a final quality evaluation score.
Description
Technical Field
The invention relates to the field of image quality evaluation, in particular to an image quality evaluation method and an evaluation model based on multi-algorithm fusion.
Background
High quality video and pictures can provide accurate and clear visual information to people. However, distortion is inevitably generated during image and video acquisition, compression, transmission and reconstruction. Therefore, in order to guarantee the visual experience of the user, an accurate quality evaluation algorithm needs to be designed to guide and optimize the image processing and video coding system.
The method for measuring user experience is called Quality Assessment (QA), and at present, two methods exist to measure user experience, the first method is subjective Quality Assessment, that is, subjective feeling of a subject watching an image/video is obtained through a subjective experiment, and a specific Quality score is given. However, the subjective experiment needs to consume a large amount of manpower and material resources, and cannot be realized in real-time application. Therefore, in practical applications, the second method, objective quality evaluation, that is, automatically predicting the quality of the current image or video by analyzing the content of the image or video using parameters or a model based on learning, is generally adopted. And objective quality evaluation can be divided into full-reference quality evaluation, half-reference quality evaluation and no-reference quality evaluation according to whether a reference image can be provided or not.
Existing full-reference quality evaluation methods can be roughly divided into five categories, namely pixel error (MSE), structural similarity (SSIM and the like), information theory (VIF), learning (LPIPS and the like) and fusion. Among them, a single full-reference quality evaluation method often cannot perform well on all distortion types and image contents. Therefore, it is necessary to design a quality evaluation method based on fusion, which can adapt to the variation of distortion types and image contents, so that the quality evaluation for various distortion types and image contents becomes reliable.
Disclosure of Invention
In view of the above, the present invention provides an image quality evaluation method and an evaluation model based on multi-algorithm fusion, so as to partially solve at least one of the above technical problems.
In order to achieve the above object, as an aspect of the present invention, there is provided an image quality evaluation method based on multi-algorithm fusion, including:
extracting the distortion type and the image content information by using a feature extractor, and obtaining a probability vector after transformation to be used as the input and the information output of other modules;
calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and image content information, and outputting image content adaptive quality evaluation prediction scores when distortion-free prior exists;
and according to the distortion type information, weighting is given to the prediction scores under different distortion priors, and the quality evaluation prediction score with distortion type adaptability is obtained and serves as a final quality evaluation score.
Wherein the probability vector is:
VD＝TD(FD(ID,IR))
VC＝TC(FC(IR))
wherein, VD，VCRespectively for the type of distortion and the image content information output, FD，FCRespectively distortion information and content information extraction module, TD，TCAre respectively corresponding transformations, ID，IRRespectively a distorted image and a reference image.
Calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image to further obtain a quality score vector:
VS＝[SSIM(ID,IR),PSNR(ID,IR),……]。
the quality fraction vector is combined with the image content probability vector to be fused, and a distortion type prior fraction vector with content characteristics is obtained:
VSC＝TSC(VC,VS)；
wherein, TSCIs a fusion module.
And giving weights to the prediction scores under different distortion priors according to the distortion type information, and giving weights to the prediction scores by adopting linear weighting.
As another aspect of the present invention, there is provided an image quality evaluation model based on multi-algorithm fusion, the evaluation model including:
the distortion type and image content information extraction module is used for extracting the distortion type and the image content information by using the characteristic extractor, and obtaining a probability vector after transformation as the input and the information output of other modules;
the objective quality evaluation algorithm calculating module is used for calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
the nonlinear transformation fusion module is used for carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and the image content information and outputting image content adaptive quality evaluation prediction scores when distortion prior is not generated;
and the linear weighting module is used for giving weights to the prediction scores under different distortion priors according to the distortion type information to obtain a quality evaluation prediction score with distortion type adaptability as a final quality evaluation score.
Wherein the probability vector is:
VD＝TD(FD(ID,IR))
VC＝TC(FC(IR))
wherein, VD，VCRespectively for the type of distortion and the image content information output, FD，FCRespectively distortion information and content information extraction module, TD，TCAre respectively corresponding transformations, ID，IRRespectively a distorted image and a reference image.
Calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image to further obtain a quality score vector:
VS＝[SSIM(ID,IR),PSNR(ID,IR),……]。
the quality fraction vector is combined with the image content probability vector to be fused, and a distortion type prior fraction vector with content characteristics is obtained:
VSC＝TSC(VC,VS)；
wherein, TSCIs a fusion module.
And giving weights to the prediction scores under different distortion priors according to the distortion type information, and giving weights to the prediction scores by adopting linear weighting.
Based on the technical scheme, compared with the prior art, the image quality evaluation method and the evaluation model based on multi-algorithm fusion have at least one of the following beneficial effects:
(1) the invention can make adaptive adjustment aiming at different distortion types and image contents, thereby having good subjective consistency for the quality score prediction of various distortion types and image contents.
(2) The invention can provide the analysis result of the image distortion type and the image content, thereby ensuring higher interpretability and reliability of the prediction result.
Drawings
Fig. 1 is a flowchart of a specific implementation of the evaluation method according to an embodiment of the present invention.
Detailed Description
The invention provides an objective quality evaluation method and an evaluation model integrating a plurality of algorithms, which effectively solve the following key technical problems:
1) due to the difference of the acquisition flow, the encoding and decoding modes and the network environment, the types of image distortion are various, the existing fusion algorithm utilizes a weighting mode to perform fusion, and the weight is acquired in a man-made or learning mode. This approach does not allow the weights to be adaptively adjusted depending on the type and content of the image distortion.
2) For the result of image quality evaluation prediction, only prediction scores can be given directly, but the basis information of the prediction result cannot be reflected well, so that the reliability of the prediction result is greatly reduced, and therefore, it is very important to give the analysis of distortion types and image contents while giving the prediction scores.
Specifically, the invention provides a method for guiding multiple full-reference quality evaluations to perform algorithm fusion by using image content and distortion type information, and the method can output related analysis information of the distortion type and the image content while outputting a final quality prediction score, so that a model can provide reliable image quality score prediction under different situations.
The method comprises the steps of firstly calculating the prediction scores of a plurality of objective quality evaluation algorithms, and simultaneously extracting image content information and distortion type information. And fusing the objective quality prediction scores and the image content information together to obtain quality prediction scores under different distortion priors. And carrying out linear weighting on the quality prediction scores under different distortion types in a priori by using the distortion type transformation information to obtain the final quality evaluation score.
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
As shown in fig. 1, a specific flowchart of the evaluation method provided by the present invention specifically includes:
extracting the distortion type and the image content information by using a feature extractor, and obtaining a probability vector after transformation to be used as the input and the information output of other modules;
calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and image content information, and outputting image content adaptive quality evaluation prediction scores when distortion-free prior exists;
and according to the distortion type information, weighting is given to the prediction scores under different distortion priors, and the quality evaluation prediction score with distortion type adaptability is obtained and serves as a final quality evaluation score.
The invention also provides an image quality evaluation model based on multi-algorithm fusion, and the evaluation model comprises:
the distortion type and image content information extraction module is used for extracting the distortion type and the image content information by using the characteristic extractor, and obtaining a probability vector after transformation as the input and the information output of other modules;
the objective quality evaluation algorithm calculating module is used for calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
the nonlinear transformation fusion module is used for carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and the image content information and outputting image content adaptive quality evaluation prediction scores when distortion prior is not generated;
and the linear weighting module is used for giving weights to the prediction scores under different distortion priors according to the distortion type information to obtain a quality evaluation prediction score with distortion type adaptability as a final quality evaluation score.
The model provided by the invention can predict the quality scores of given distortion graphs and reference graphs, and can output distortion type information and image content information.
For the input distorted image and the reference image, respectively extracting features by using a feature extractor and transforming to obtain probability vectors:
VD＝TD(FD(ID,IR))
VC＝TC(FC(IR))
wherein, FD,FCRespectively distortion information and content information extraction module, TD,TCAre respectively corresponding transformations, ID，IRRespectively a distorted image and a reference image. Simultaneously calculating a plurality of objective quality evaluation algorithms to obtain quality score vectors:
VS＝[SSIM(ID,IR),PSNR(ID,IR),……]
combining the quality fraction vector with the image content probability vector to fuse to obtain a distortion type prior fraction vector with content characteristics:
VSC＝TSC(VC,VS)
wherein T isSCIs a fusion module. Finally using distortion type probability vector pairWeighting the distortion type prior fraction vector to obtain a final fraction:
S＝VSC*VD
the final score obtained is used as the objective quality evaluation output of the model, VDAnd VCThe output as distortion type and image content information may reflect probabilities of belonging to different distortion types and image contents.
The method can adjust the adaptability according to different distortion types and image contents, so that the method has good subjective consistency on the quality score prediction of various distortion types and image contents, and as shown in table 1, the higher the relevant index is, the better the quality score prediction is.
Table 1 comparison of the performance of the correlation coefficients of the invention on database 1
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An image quality evaluation method based on multi-algorithm fusion comprises the following steps:
extracting the distortion type and the image content information by using a feature extractor, and obtaining a probability vector after transformation to be used as the input and the information output of other modules;
calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and image content information, and outputting image content adaptive quality evaluation prediction scores at different distortion prior times;
and according to the distortion type information, weighting is given to the prediction scores under different distortion priors, and the quality evaluation prediction score with distortion type adaptability is obtained and serves as a final quality evaluation score.
2. The evaluation method of claim 1, the probability vector being:
VD＝TD(FD(ID，IR))
VC＝TC(FC(IR))
wherein, VD，VCRespectively for the type of distortion and the image content information output, FD，FCRespectively distortion information and content information extraction module, TD，TCAre respectively corresponding transformations, ID，IRRespectively a distorted image and a reference image.
3. The evaluation method of claim 1, wherein the computing of the plurality of quality evaluation algorithm scores for the distorted image and the reference image further yields a quality score vector:
VS＝[SSIM(ID，IR)，PSNR(ID，IR)，......]。
4. the evaluation method of claim 1, wherein the quality score vector is fused with an image content probability vector to obtain a distortion type prior score vector with content characteristics:
VSC＝TSC(VC，VS)；
wherein, TSCIs a fusion module.
5. The method of claim 1, wherein weighting prediction scores at different distortion priors according to distortion type information is performed by linear weighting.
6. An image quality evaluation model based on multi-algorithm fusion, the evaluation model comprising:
the distortion type and image content information extraction module is used for extracting the distortion type and the image content information by using the characteristic extractor, and obtaining a probability vector after transformation as the input and the information output of other modules;
the objective quality evaluation algorithm calculating module is used for calculating a plurality of quality evaluation algorithm scores of the distorted image and the reference image;
the nonlinear transformation fusion module is used for carrying out nonlinear transformation fusion on the obtained multiple objective quality evaluation algorithm scores and the image content information and outputting image content adaptive quality evaluation prediction scores when distortion prior is not generated;
and the linear weighting module is used for giving weights to the prediction scores under different distortion priors according to the distortion type information to obtain a quality evaluation prediction score with distortion type adaptability as a final quality evaluation score.
7. The evaluation model of claim 6, the probability vector being:
VD＝TD(FD(ID，IR))
VC＝TC(FC(IR))
wherein, VD，VCRespectively for the type of distortion and the image content information output, FD，FCRespectively distortion information and content information extraction module, TD，TCAre respectively corresponding transformations, ID，IRRespectively a distorted image and a reference image.
8. The evaluation model of claim 6, wherein the computing of the plurality of quality evaluation algorithm scores for the distorted image and the reference image further yields a quality score vector:
VS＝[SSIM(ID，IR)，PSNR(ID，IR)，......]。
9. the evaluation model of claim 6, wherein the quality score vector is fused with the image content probability vector to obtain a distortion type prior score vector with content characteristics:
VSC＝TSC(VC，VS)；
wherein, TSCIs a fusion module.
10. The evaluation model of claim 6, wherein weighting prediction scores at different distortion priors according to distortion type information is weighting using linear weighting.
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