WO2022105117A1 - Procédé et dispositif d'évaluation de qualité d'image, dispositif informatique et support de stockage - Google Patents

Procédé et dispositif d'évaluation de qualité d'image, dispositif informatique et support de stockage Download PDF

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WO2022105117A1
WO2022105117A1 PCT/CN2021/090416 CN2021090416W WO2022105117A1 WO 2022105117 A1 WO2022105117 A1 WO 2022105117A1 CN 2021090416 W CN2021090416 W CN 2021090416W WO 2022105117 A1 WO2022105117 A1 WO 2022105117A1
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image
feature
training
network
generation network
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Chinese (zh)
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陈昊
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Definitions

  • the present application belongs to the technical field of artificial intelligence, and specifically relates to a method, device, computer equipment and storage medium for image quality evaluation.
  • the method based on the convolutional deep learning network, which often uses a neural network to fit the judgment results of people.
  • the image quality evaluation method of convolutional deep learning network has high requirements on the overhead of computing resources, and in some occasions (such as mobile terminals), there will be large usage restrictions.
  • the computing power and memory of mobile terminals have certain limitations. limitations, making it difficult to deploy convolutional deep learning networks.
  • the purpose of the embodiments of the present application is to propose a method, device, computer equipment and storage medium for image quality evaluation, so as to solve the problem that the existing image quality evaluation solutions have relatively limited application scenarios and cannot quickly adapt to various scenarios. question.
  • the embodiments of the present application provide a method for image quality evaluation, which adopts the following technical solutions:
  • a method of image quality evaluation comprising:
  • the embodiment of the present application also provides an image quality evaluation device, which adopts the following technical solutions:
  • a device for evaluating image quality comprising:
  • the building module is used to build an image generation network, train the image generation network through the training sample set in the preset database, and obtain an image feature extractor;
  • an extraction module for receiving the image to be evaluated, and extracting image features of the image to be evaluated by using an image feature extractor
  • the transformation module is used to transform the image features of the image to be evaluated into feature vectors, and convert the image features into feature vectors;
  • the evaluation module is used to construct a network regression function, use the network regression function to calculate the regression value of the feature vector, and determine the quality of the image to be evaluated according to the regression value of the feature vector.
  • the embodiment of the present application also provides a computer device, which adopts the following technical solutions:
  • a computer device includes a memory and a processor, wherein computer-readable instructions are stored in the memory, and the processor implements the following image quality evaluation method when executing the computer-readable instructions:
  • the embodiments of the present application also provide a computer-readable storage medium, which adopts the following technical solutions:
  • a computer-readable storage medium on which computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor, the following image quality evaluation method is implemented:
  • the present application discloses an image quality evaluation method, device, computer equipment and storage medium, which belong to the technical field of artificial intelligence.
  • the present application trains the image generation network by constructing an image generation network and using a training sample set in a preset database. Obtain the image feature extractor; receive the image to be evaluated, and use the image feature extractor to extract the image features of the image to be evaluated; perform feature vector transformation on the image features of the image to be evaluated, and convert the image features into feature vectors; build a network regression function, using The network regression function calculates the regression value of the feature vector, and determines the quality of the image to be evaluated according to the regression value of the feature vector.
  • the present application constructs an image quality evaluation system by simplifying the deep learning network and adopting machine regression.
  • the image features are first obtained through the trained deep learning network, and then the image features are calculated based on the network regression function. Regression value, and finally determine the quality of the image to be evaluated based on the regression value of the image features.
  • the image quality evaluation system constructed in this application has a simple structure, does not occupy too many server resources, effectively reduces computing resource consumption, and can meet the deployment and use requirements of mobile terminals .
  • the image quality is finally evaluated by the network regression function, and a mathematical explanation can be made for the evaluation result, which is convenient for users to analyze the problem intuitively.
  • FIG. 1 shows an exemplary system architecture diagram to which the present application can be applied
  • FIG. 2 shows a flow chart of an embodiment of a method for image quality evaluation according to the present application
  • Fig. 3 shows a flowchart of a specific implementation manner of step S201 in Fig. 2;
  • Fig. 4 shows a flowchart of a specific implementation manner of step S204 in Fig. 2;
  • FIG. 5 shows a schematic structural diagram of an embodiment of an apparatus for evaluating image quality according to the present application
  • FIG. 6 shows a schematic structural diagram of an embodiment of a computer device according to the present application.
  • the system architecture 100 may include terminal devices 101 , 102 , and 103 , a network 104 and a server 105 .
  • the network 104 is a medium used to provide a communication link between the terminal devices 101 , 102 , 103 and the server 105 .
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
  • the user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like.
  • Various communication client applications may be installed on the terminal devices 101 , 102 and 103 , such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, and the like.
  • the terminal devices 101, 102, and 103 can be various electronic devices that have a display screen and support web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic Picture Experts Compression Standard Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4) Players, Laptops and Desktops, etc.
  • MP3 players Moving Picture Experts Group Audio Layer III, dynamic Picture Experts Compression Standard Audio Layer 3
  • MP4 Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4
  • the server 105 may be a server that provides various services, such as a background server that provides support for the pages displayed on the terminal devices 101 , 102 , and 103 .
  • the image quality evaluation method provided in the embodiments of the present application is generally performed by a server/terminal device, and accordingly, an image quality evaluation apparatus is generally set in the server/terminal device.
  • terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.
  • the described image quality evaluation method includes the following steps:
  • S201 Build an image generation network, train the image generation network through a training sample set in a preset database, and obtain an image feature extractor.
  • an image generation network can be constructed based on a deep convolutional neural network model.
  • Convolutional Neural Networks is a kind of feedforward neural network (Feedforward Neural Networks) that includes convolution calculation and has a deep structure.
  • feedforward Neural Networks feedforward neural network
  • Convolutional neural network has the ability of representation learning and can perform shift-invariant classification of input information according to its hierarchical structure, so it is also called "shift-invariant artificial neural network”.
  • Convolutional neural network is constructed by imitating the visual perception mechanism of biology, which can perform supervised learning and unsupervised learning. Small computational effort to learn grid-like topology features, such as pixels and audio, with stable results and no additional feature engineering requirements on the data.
  • the image generation network includes an encoding layer encoder and a decoding layer decoder.
  • the encoding layer encoder includes several convolution kernels
  • the decoding layer decoder includes several deconvolution kernels.
  • the convolution kernels correspond to the deconvolution kernels one by one.
  • the encoding layer encoder A connected channel is established between the convolution kernel of the corresponding decoding layer decoder and the deconvolution kernel of the corresponding decoding layer decoder. After the convolution kernel of the encoding layer encoder extracts the image features, the extracted image features can be directly transmitted through the connected channel to the inverse of the corresponding decoding layer decoder. on the convolution kernel.
  • the encoding layer encoder is a full convolution layer, and the encoding layer encoder is used to extract the image features of the input image, and the image feature extractor is also composed of this part.
  • the decoding layer decoder is a deconvolution layer.
  • the decoding layer decoder is used to decode the extracted image features and restore the image features to the input image.
  • the purpose of the decoding layer decoder to restore the image features is to complete the verification of the encoding layer encoder. It should be noted that when constructing an image generation network, the loss functions L1 and L2 are set for the encoding layer encoder and the decoding layer decoder respectively. When the image generation network is iteratively updated, the image generation can be based on the L1 loss function and the L2 loss function. Iterative update of the network.
  • an image generation network is constructed based on a deep convolutional neural network model, a training sample set is obtained from a preset database, the image generation network is trained through the training sample set, and after the trained image generation network is obtained, the image generation network is The convolution kernels in the encoder layer of the network build image feature extractors.
  • S202 Receive the image to be evaluated, and use an image feature extractor to extract image features of the image to be evaluated.
  • an image evaluation requirement is generated, an image evaluation instruction is received, an image to be evaluated is acquired based on the image evaluation instruction, and the image feature of the image to be evaluated is extracted by using the image feature extractor constructed above.
  • the image feature extractor is constructed by the encoder part of the compressed coding layer network, and the image feature extractor will output multi-scale image features during feature extraction, and the image features of the previous layer are the same as the next layer. image input to the layer.
  • a total of 5 layers of feature extraction convolution layers are constructed.
  • the image features extracted from these 5 layers are scale feature 0, scale feature 1, and scale feature 2 respectively.
  • scale feature 3 and scale feature 4 scale feature 0, scale feature 1, scale feature 2, scale feature 3 and scale feature 4 are 512x512, 256x256, 128x128, 64x64, 32x32 respectively.
  • the electronic device (for example, the server/terminal device shown in FIG. 1 ) on which the image quality evaluation method runs may receive the image evaluation instruction through a wired connection or a wireless connection.
  • the above wireless connection methods may include but are not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or developed in the future .
  • S203 transform the image features of the image to be evaluated into feature vectors, and convert the image features into feature vectors.
  • the to-be-evaluated The multiple image features of the image are converted into feature vectors, and the feature vectors are vectors of the same size. Converting multiple image features of the image to be evaluated into feature vectors is beneficial to use the network regression function to calculate the regression value of the image features in the subsequent steps. .
  • spatial pyramid pooling can convert feature maps of any size into fixed-size feature vectors, and send the fixed-size feature vectors to the fully connected layer.
  • the image evaluation task is divided into image feature extraction and multiple regression evaluation processes.
  • the regression value of the eigenvector is calculated by the multivariate network regression function, the regression value of the eigenvector is calculated based on the construction of the network regression function, and the regression value of the eigenvector is normalized so that the regression value of the eigenvector falls within the range of 0-1.
  • the regression value can be regarded as a comprehensive score of multiple dimensions of image features, and finally the quality of the image to be evaluated is determined according to the regression value of the feature tensor.
  • the Bayesian kernel regression method is mainly used to construct a multi-dimensional image.
  • the network regression function of the feature is mainly used to construct a multi-dimensional image.
  • a network regression function is constructed based on the Bayesian kernel regression equation, and the network regression function is used to calculate the regression value of the feature vector, and the regression value of the feature vector is normalized.
  • the resulting eigenvector regression value determines the quality of the image to be evaluated. If the regression value is 1, the image quality is excellent, and if the regression value is 0, the image quality is unqualified.
  • the present application discloses an image quality evaluation method, device, computer equipment and storage medium, which belong to the technical field of artificial intelligence.
  • the present application trains the image generation network by constructing an image generation network and using a training sample set in a preset database. Obtain the image feature extractor; receive the image to be evaluated, and use the image feature extractor to extract the image features of the image to be evaluated; perform feature vector transformation on the image features of the image to be evaluated, and convert the image features into feature vectors; build a network regression function, using The network regression function calculates the regression value of the feature vector, and determines the quality of the image to be evaluated according to the regression value of the feature vector.
  • the present application constructs an image quality evaluation system by simplifying the deep learning network and adopting machine regression.
  • the image features are first obtained through the trained deep learning network, and then the image features are calculated based on the network regression function. Regression value, and finally determine the quality of the image to be evaluated based on the regression value of the image features.
  • the image quality evaluation system constructed in this application has a simple structure, does not occupy too many server resources, effectively reduces computing resource consumption, and can meet the deployment and use requirements of mobile terminals .
  • the image quality is finally evaluated by the network regression function, and a mathematical explanation can be made for the evaluation result, which is convenient for users to analyze the problem intuitively.
  • training the image generation network through the training sample set in the preset database, and acquiring the image feature extractor it also includes:
  • the image data is acquired from a preset database, the image data is marked, and the quality index of the image data can be marked during marking.
  • the labeled image data can be randomly divided into 10 equal sample subsets, of which 9 sample subsets are randomly combined as the training sample set , the remaining sample subset is used as the validation data set, and the training sample set and the validation data set are stored in the preset database.
  • the image generation network includes an encoding layer and a decoding layer
  • the encoding layer includes several convolution kernels
  • the decoding layer includes Several deconvolution kernels
  • the convolution kernels correspond to the deconvolution kernels one-to-one
  • construct an image generation network train the image generation network through the training sample set in the preset database, and obtain the steps of image feature extractor, which specifically includes :
  • the deep compression algorithm trains the neural network, obtains the weight of each convolutional layer of the trained neural network, sets the weight threshold, and then deletes the convolutional layers below the weight threshold, and then iterates For training, redundant layers are removed again and again through iterative training. Finally, the weights of the convolutional layers retained in the neural network are clustered and the weights are shared, and the value of the cluster center point is used as the value of the ownership value.
  • the model compression effect, and finally the weights are Huffman encoded.
  • This proposal adopts the Deep Compression method to compress the neural network without losing precision, in which the size of the neural network can be compressed to 35 times to 49 times the original size, and the application of storage is more efficient during inference.
  • the training samples in the training sample set are extracted, and each training sample is sequentially imported into the encoding layer encoder of the image generation network.
  • the encoding layer encoder is preset with a number of convolution kernels, and each training sample is used for the image generation network.
  • the convolution kernel of the encoding layer encoder is trained, and several trained convolution kernels are obtained, the weight threshold is set, and several convolution kernels after training are screened based on the deep learning compression algorithm.
  • the convolution layer is removed to remove redundant items in several convolution kernels, and an image feature extractor is constructed by using several convolution kernels after removing redundant items.
  • the method further includes:
  • the training result of each convolution kernel is imported into the corresponding deconvolution kernel, and the corresponding deconvolution kernel is trained through the training result of each convolution kernel, and several trained deconvolution kernels are obtained.
  • the training results of each convolution kernel in the encoding layer encoder are collected, the training results of each convolution kernel in the encoding layer encoder are marked, and the training results of each convolution kernel in the marked encoding layer encoder are used for training.
  • the training results of each convolution kernel in the marked encoding layer encoder are used for training.
  • several trained deconvolution kernels are obtained.
  • the feature extraction results of several verification samples are respectively imported into the corresponding deconvolution kernels for feature restoration, and the feature restoration results are obtained;
  • the prediction error is compared with the preset threshold, and if the prediction error is greater than the preset threshold, the image generation network is iteratively updated until the prediction error is less than or equal to the preset threshold, and the image generation network is acquired.
  • the backpropagation algorithm that is, the error backpropagation algorithm (Backpropagation algorithm, BP algorithm) is a learning algorithm suitable for multi-layer neuron networks. It is based on the gradient descent method and is used for the error of deep learning networks. calculate.
  • the input and output relationship of BP network is essentially a mapping relationship: the function completed by a BP neural network with n input and m output is a continuous mapping from n-dimensional Euclidean space to a finite field in m-dimensional Euclidean space. A map is highly nonlinear.
  • the learning process of BP algorithm consists of forward propagation process and back propagation process.
  • the input information is processed layer by layer through the hidden layer through the input layer and transmitted to the output layer, and then transferred to the back propagation, and the partial derivative of the objective function to the weight of each neuron is obtained layer by layer, which constitutes The gradient of the objective function to the weight vector is used as the basis for modifying the weight.
  • the verification samples in the verification data set are extracted, the verification data set is imported into the image generation network, and several trained convolution kernels are used to extract the features of the verification samples respectively, and the corresponding deconvolution kernels are used to restore the features.
  • the backpropagation algorithm calculates the prediction error and compares the prediction error with the preset error threshold. If the prediction error is greater than the preset error threshold, the image generation network is iterated based on the loss functions L1 and L2 of the encoder layer encoder and decoder layer decoder. Update until the prediction error is less than or equal to the preset error threshold, and obtain the image generation network that has passed the verification.
  • image features of the image to be evaluated are transformed into feature vectors, and the steps of transforming the image features into feature vectors include:
  • the image features of the image to be evaluated are transformed into feature vectors, and the image features are transformed into feature vectors.
  • multiple image features of the image to be evaluated are extracted by using the image feature extractor to perform spatial pyramid pooling to convert the multiple image features of the image to be evaluated.
  • the feature vector is a vector with the same size. Converting multiple image features of the image to be evaluated into a feature vector is beneficial to use the network regression function to calculate the regression value of the image feature in the subsequent steps.
  • spatial pyramid pooling can convert feature maps of any size into fixed-size feature vectors, and send the fixed-size feature vectors to the fully connected layer.
  • the image features are converted into feature vectors in the form of full links through spatial pyramid pooling.
  • the spatial pyramid pooling refers to performing deformation and convolution operations on the above image features of different scales, and finally obtains feature vectors in the form of full links.
  • Fig. 4 shows a flow chart of a specific implementation of step S204 in Fig. 2, constructing a network regression function, using the network regression function to calculate the regression value of the eigenvector, and according to the regression of the eigenvector. value to determine the quality of the image to be evaluated, including:
  • S404 import the feature vector into the network regression function, calculate the regression value of the feature vector, and determine the quality of the image to be evaluated according to the regression value of the feature vector.
  • the initial regression function is constructed based on the Bayesian equation.
  • the Bayesian equation is as follows:
  • Y here refers to the Bayesian regression value
  • i refers to the serial number of the input image
  • h refers to the high-dimensional response function
  • z refers to the image feature
  • x refers to the potential factor
  • ⁇ refers to the weight
  • ⁇ is the modulation factor.
  • ⁇ is the former coefficient of the kernel function, where the kernel function uses a Gaussian kernel function, so here K(z, z') can be rewritten as:
  • exp is the e index
  • M is the training set capacity, that is, the number of samples.
  • n 1,...,M
  • rm is the probability value of the conditional probability of Bayes' theorem
  • f is the probability density function
  • bernouli refers to the composite Bernoulli distribution
  • ⁇ is the variance
  • an initial regression function is constructed based on the Bayesian algorithm, the parameters of the image feature extractor are extracted, and the feature weights are calculated based on the parameters of the image feature extractor, and the feature weights are normalized.
  • import the normalized feature weights into the initial regression function to obtain the network regression function import the feature vector into the network regression function, calculate the regression value of the feature vector, and determine the quality of the image to be evaluated according to the regression value of the feature vector .
  • the above-mentioned images to be evaluated can also be stored in a node of a blockchain.
  • the blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • the aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.
  • the present application provides an embodiment of an apparatus for evaluating image quality.
  • the apparatus embodiment corresponds to the method embodiment shown in FIG. 2 . Specifically, it can be applied to various electronic devices.
  • the apparatus for image quality evaluation described in this embodiment includes:
  • the building module 501 is used to build an image generation network, and train the image generation network through a training sample set in a preset database to obtain an image feature extractor;
  • Extraction module 502 for receiving the image to be evaluated, and extracting image features of the image to be evaluated by using an image feature extractor
  • the conversion module 503 is used to convert the image features of the image to be evaluated into feature vectors, and convert the image features into feature vectors;
  • the evaluation module 504 is configured to construct a network regression function, use the network regression function to calculate the regression value of the feature vector, and determine the quality of the image to be evaluated according to the regression value of the feature vector.
  • the device for image quality evaluation also includes:
  • the preprocessing module is used to obtain the image data in the preset database and preprocess the image data
  • the labeling module is used to label the preprocessed image data, and randomly combine the labelled image data to obtain a training sample set and a verification data set;
  • the storage module is used to store the training sample set and the validation data set in the preset database.
  • the image generation network includes an encoding layer and a decoding layer
  • the encoding layer includes several convolution kernels
  • the decoding layer includes several deconvolution kernels
  • the convolution kernels correspond to the deconvolution kernels one-to-one.
  • the building module 501 specifically includes:
  • the extraction unit is used to extract the training samples in the training sample set, and sequentially import each training sample into the coding layer of the image generation network;
  • the first training unit is used to train the coding layer in the image generation network by using each training sample to obtain several trained convolution kernels;
  • the compression unit is used to screen several convolution kernels after training based on the deep learning compression algorithm, and remove redundant items in several convolution kernels;
  • the construction unit is used to construct an image feature extractor using several convolution kernels after removing redundant items.
  • the device for image quality evaluation also includes:
  • the acquisition unit is used to collect the training results of each convolution kernel in the coding layer
  • the second training unit is used to import the training result of each convolution kernel into the corresponding deconvolution kernel, train the corresponding deconvolution kernel through the training result of each convolution kernel, and obtain several training completed inverse convolution kernels. convolution kernel.
  • the device for image quality evaluation also includes:
  • the verification unit is used to extract the verification samples in the verification data set, and import the verification data set into the image generation network;
  • the convolution unit is used to extract the features of the verification samples by using several trained convolution kernels to obtain the feature extraction results of several verification samples;
  • the restoration unit is used to import the feature extraction results of several verification samples into the corresponding deconvolution kernels for feature restoration, and obtain the feature restoration results;
  • the fitting unit is used to restore the result and the verification sample based on the feature, and use the back-propagation algorithm to perform fitting to obtain the prediction error;
  • the iterative unit is configured to compare the prediction error with a preset threshold, and if the prediction error is greater than the preset threshold, iteratively update the image generation network until the prediction error is less than or equal to the preset threshold, and acquire the image generation network.
  • the conversion module specifically includes:
  • the transformation unit is used to transform the image features of the image to be evaluated based on the spatial pyramid pooling, and convert the image features into feature vectors.
  • evaluation module 504 specifically includes:
  • the function construction unit is used to construct the initial regression function based on the Bayesian algorithm
  • a parameter extraction unit for extracting parameters of the image feature extractor, and calculating feature weights based on the parameters of the image feature extractor
  • the import unit is used to import the feature weights into the initial regression function to obtain the network regression function
  • the evaluation unit is used to import the feature vector into the network regression function, calculate the regression value of the feature vector, and determine the quality of the image to be evaluated according to the regression value of the feature vector.
  • the application discloses an image quality evaluation device, belonging to the technical field of artificial intelligence.
  • the application constructs an image generation network, trains the image generation network through a training sample set in a preset database, and obtains an image feature extractor; Evaluate the image, and use the image feature extractor to extract the image features of the image to be evaluated; perform feature vector transformation on the image features of the image to be evaluated, and convert the image features into feature vectors; build a network regression function, and use the network regression function to calculate the regression of the feature vector value, and determine the quality of the image to be evaluated according to the regression value of the feature vector.
  • the present application constructs an image quality evaluation system by simplifying the deep learning network and adopting machine regression.
  • the image features are first obtained through the trained deep learning network, and then the image features are calculated based on the network regression function. Regression value, and finally determine the quality of the image to be evaluated based on the regression value of the image features.
  • the image quality evaluation system constructed in this application has a simple structure, does not occupy too many server resources, effectively reduces computing resource consumption, and can meet the deployment and use requirements of mobile terminals .
  • the image quality is finally evaluated by the network regression function, and a mathematical explanation can be made for the evaluation result, which is convenient for users to analyze the problem intuitively.
  • FIG. 6 is a block diagram of the basic structure of a computer device according to this embodiment.
  • the computer device 6 includes a memory 61 , a processor 62 , and a network interface 63 that communicate with each other through a system bus. It should be pointed out that only the computer device 6 with components 61-63 is shown in the figure, but it should be understood that it is not required to implement all of the shown components, and more or less components may be implemented instead.
  • the computer device here is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, special-purpose Integrated circuit (Application Specific Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Signal Processor
  • the computer equipment may be a desktop computer, a notebook computer, a palmtop computer, a cloud server and other computing equipment.
  • the computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad or a voice control device.
  • the memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disk, Optical Disk, etc.
  • the memory 61 may be an internal storage unit of the computer device 6 , such as a hard disk or a memory of the computer device 6 .
  • the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the memory 61 may also include both the internal storage unit of the computer device 6 and its external storage device.
  • the memory 61 is generally used to store the operating system and various application software installed on the computer device 6 , such as computer-readable instructions of a method for evaluating image quality.
  • the memory 61 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 62 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. This processor 62 is typically used to control the overall operation of the computer device 6 . In this embodiment, the processor 62 is configured to execute computer-readable instructions stored in the memory 61 or process data, for example, computer-readable instructions for executing the image quality evaluation method.
  • CPU Central Processing Unit
  • controller a microcontroller
  • microprocessor microprocessor
  • This processor 62 is typically used to control the overall operation of the computer device 6 .
  • the processor 62 is configured to execute computer-readable instructions stored in the memory 61 or process data, for example, computer-readable instructions for executing the image quality evaluation method.
  • the network interface 63 may include a wireless network interface or a wired network interface, and the network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.
  • the application discloses a computer device, which belongs to the technical field of artificial intelligence.
  • the application constructs an image generation network, trains the image generation network through a training sample set in a preset database, and obtains an image feature extractor; receives an image to be evaluated, And use the image feature extractor to extract the image features of the image to be evaluated; transform the image features of the image to be evaluated into feature vectors, and convert the image features into feature vectors; build a network regression function, use the network regression function to calculate the regression value of the feature vector, and The quality of the image to be evaluated is determined according to the regression value of the feature vector.
  • the present application constructs an image quality evaluation system by simplifying the deep learning network and adopting machine regression.
  • the image features are first obtained through the trained deep learning network, and then the image features are calculated based on the network regression function. Regression value, and finally determine the quality of the image to be evaluated based on the regression value of the image features.
  • the image quality evaluation system constructed in this application has a simple structure, does not occupy too many server resources, effectively reduces computing resource consumption, and can meet the deployment and use requirements of mobile terminals .
  • the image quality is finally evaluated by the network regression function, and a mathematical explanation can be made for the evaluation result, which is convenient for users to analyze the problem intuitively.
  • the present application also provides another implementation manner, that is, to provide a computer-readable storage medium
  • the computer-readable storage medium may be non-volatile or volatile
  • the computer-readable storage medium stores Computer readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the method for image quality assessment as described above.
  • the application discloses a storage medium, which belongs to the technical field of artificial intelligence.
  • the application constructs an image generation network, trains the image generation network through a training sample set in a preset database, and obtains an image feature extractor; receives an image to be evaluated, And use the image feature extractor to extract the image features of the image to be evaluated; transform the image features of the image to be evaluated into feature vectors, and convert the image features into feature vectors; build a network regression function, use the network regression function to calculate the regression value of the feature vector, and The quality of the image to be evaluated is determined according to the regression value of the feature vector.
  • the present application constructs an image quality evaluation system by simplifying the deep learning network and adopting machine regression.
  • the image features are first obtained through the trained deep learning network, and then the image features are calculated based on the network regression function. Regression value, and finally determine the quality of the image to be evaluated based on the regression value of the image features.
  • the image quality evaluation system constructed in this application has a simple structure, does not occupy too many server resources, effectively reduces computing resource consumption, and can meet the deployment and use requirements of mobile terminals .
  • the image quality is finally evaluated by the network regression function, and a mathematical explanation can be made for the evaluation result, which is convenient for users to analyze the problem intuitively.
  • the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of a software product in essence or in a part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of this application.
  • a storage medium such as ROM/RAM, magnetic disk, CD-ROM

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Abstract

La présente demande concerne des technologies de vision artificielle dans le domaine de l'intelligence artificielle. L'invention concerne un procédé et un dispositif d'évaluation de qualité d'image, ainsi qu'un dispositif informatique et un support de stockage. En construisant un réseau de génération d'image et en formant le réseau de génération d'image avec un ensemble d'échantillons d'apprentissage dans une base de données prédéfinie, la présente demande acquiert un extracteur de caractéristiques d'image ; reçoit une image à évaluer et utilise l'extracteur de caractéristiques d'image afin d'extraire une caractéristique de ladite image ; effectue une transformation de vecteur propre de la caractéristique d'image de ladite image afin de transformer la caractéristique d'image en vecteur propre ; construit une fonction de régression de réseau, utilise la fonction de régression de réseau pour calculer une valeur de régression du vecteur propre, puis détermine la qualité de ladite image d'après la valeur de régression du vecteur propre. De plus, la présente demande concerne également la technologie des chaînes de blocs dans le sens où ladite image peut être stockée dans une chaîne de blocs. La présente demande construit un système d'évaluation de qualité d'image en simplifiant un réseau d'apprentissage profond et en utilisant un schéma de régression automatique. Le système d'évaluation de qualité d'image est rapidement adaptable à divers scénarios.
PCT/CN2021/090416 2020-11-17 2021-04-28 Procédé et dispositif d'évaluation de qualité d'image, dispositif informatique et support de stockage WO2022105117A1 (fr)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115984843A (zh) * 2022-12-06 2023-04-18 北京信息科技大学 一种再制造原料评估方法、装置、存储介质及电子设备
CN117830246A (zh) * 2023-12-27 2024-04-05 广州极点三维信息科技有限公司 一种图像分析与质量评价方法及系统

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112418292B (zh) * 2020-11-17 2024-05-10 平安科技（深圳）有限公司 一种图像质量评价的方法、装置、计算机设备及存储介质
WO2022217496A1 (fr) * 2021-04-14 2022-10-20 中国科学院深圳先进技术研究院 Procédé et appareil d'évaluation de qualité de données d'image, dispositif terminal et support de stockage lisible
CN113112518B (zh) * 2021-04-19 2024-03-26 深圳思谋信息科技有限公司 基于拼接图像的特征提取器生成方法、装置和计算机设备
CN113486939A (zh) * 2021-06-30 2021-10-08 平安证券股份有限公司 一种处理图片的方法、装置、终端及存储介质
CN117135306A (zh) * 2022-09-15 2023-11-28 深圳Tcl新技术有限公司 电视清晰度的调试方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109949277A (zh) * 2019-03-04 2019-06-28 西北大学 一种基于排序学习和简化残差网络的oct图像质量评价方法
CN110033446A (zh) * 2019-04-10 2019-07-19 西安电子科技大学 基于孪生网络的增强图像质量评价方法
CN111242036A (zh) * 2020-01-14 2020-06-05 西安建筑科技大学 一种基于编码-解码结构多尺度卷积神经网络的人群计数方法
US20200234141A1 (en) * 2017-10-24 2020-07-23 Deepnorth Inc. Image Quality Assessment Using Similar Scenes as Reference
CN112418292A (zh) * 2020-11-17 2021-02-26 平安科技（深圳）有限公司 一种图像质量评价的方法、装置、计算机设备及存储介质

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10002415B2 (en) * 2016-04-12 2018-06-19 Adobe Systems Incorporated Utilizing deep learning for rating aesthetics of digital images
JP7202091B2 (ja) * 2018-07-13 2023-01-11 日本放送協会 画質評価装置、学習装置及びプログラム
CN110766658B (zh) * 2019-09-23 2022-06-14 华中科技大学 一种无参考激光干扰图像质量评价方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200234141A1 (en) * 2017-10-24 2020-07-23 Deepnorth Inc. Image Quality Assessment Using Similar Scenes as Reference
CN109949277A (zh) * 2019-03-04 2019-06-28 西北大学 一种基于排序学习和简化残差网络的oct图像质量评价方法
CN110033446A (zh) * 2019-04-10 2019-07-19 西安电子科技大学 基于孪生网络的增强图像质量评价方法
CN111242036A (zh) * 2020-01-14 2020-06-05 西安建筑科技大学 一种基于编码-解码结构多尺度卷积神经网络的人群计数方法
CN112418292A (zh) * 2020-11-17 2021-02-26 平安科技（深圳）有限公司 一种图像质量评价的方法、装置、计算机设备及存储介质

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115984843A (zh) * 2022-12-06 2023-04-18 北京信息科技大学 一种再制造原料评估方法、装置、存储介质及电子设备
CN117830246A (zh) * 2023-12-27 2024-04-05 广州极点三维信息科技有限公司 一种图像分析与质量评价方法及系统

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