CN114219808A - Image processing method, apparatus, device, storage medium, and computer program product - Google Patents
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Abstract
The application relates to an image processing method, an apparatus, a device, a storage medium and a computer program product. The method comprises the following steps: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution. The method can improve the image display effect.
Description
Technical Field
The present application relates to the field of artificial intelligence image recognition technology, and in particular, to an image processing method, apparatus, device, storage medium, and computer program product.
Background
With the development of computer devices, in order to facilitate users to know images, it is often necessary to display images on a screen, for example, to display business data on the screen by way of text description and diagrams, so as to facilitate users to know business related conditions.
In the conventional art, the resolutions of different screens are different, and thus, when an image is displayed, the content to be displayed can be reduced or enlarged according to the resolution of the screen. For example, the resolution of the whole image is multiplied by a preset ratio to obtain a scaled resolution, and the image is adjusted to the scaled resolution and then displayed on the screen. However, the scaling method in the conventional technology has the following technical problems: the zooming mode has poor flexibility, and some contents in the image are not matched with the screen after zooming, so that the displayed image has poor effect.
Disclosure of Invention
In view of the above, it is necessary to provide an image processing method, an apparatus, a device, a computer readable storage medium and a computer program product for solving the above technical problems.
In a first aspect, the present application provides an image processing method. The method comprises the following steps: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
In an embodiment, the processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adapted to the target screen resolution includes: obtaining a plurality of candidate image processing schemes based on the resolution range corresponding to the initial image block and the target screen resolution; the candidate image processing scheme comprises adjusting resolutions respectively corresponding to the initial image blocks; processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme; inputting the candidate processing images into a trained image scoring model for scoring to obtain image scores corresponding to the candidate processing images; and selecting candidate processing images meeting the grading condition as target images based on the image grades.
In one embodiment, the training step of the image scoring model comprises: acquiring a real image and generating an image; inputting the real image into an image scoring model to be trained for scoring to obtain a first image score corresponding to the real image; inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image; obtaining a score model loss value based on the first image score and the second image score, wherein the score model loss value and the first image score are in a negative correlation relationship, and the score model loss value and the second image score are in a positive correlation relationship; and adjusting the model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
In one embodiment, the scoring condition comprises at least one of: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold.
In one embodiment, the training of the resolution recognition model comprises: acquiring a training image and acquiring a label image corresponding to the training image; partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks; acquiring training image blocks corresponding to the label image blocks in the training images; inputting the training image blocks into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image blocks; determining a resolution model loss value based on the prediction resolution range corresponding to the training image block and the resolution range corresponding to the label image block; and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
In an embodiment, the blocking the initial image to obtain a plurality of initial image blocks includes: carrying out object identification on the initial image to obtain a display object contained in the initial image; and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
In a second aspect, the present application further provides an image processing apparatus. The device comprises: the system comprises a resolution and image acquisition module, a resolution and image acquisition module and a processing module, wherein the resolution and image acquisition module is used for determining an initial image to be processed and a target screen resolution corresponding to the initial image, and the target screen resolution is the resolution of a screen displaying the initial image; the block processing module is used for carrying out block processing on the initial image to obtain a plurality of initial image blocks; a resolution range obtaining module, configured to input the initial image blocks into a trained resolution recognition model for processing, so as to obtain resolution ranges corresponding to the initial image blocks respectively; and the target image obtaining module is used for processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image matched with the target screen resolution.
In one embodiment, the target image derivation module is to: obtaining a plurality of candidate image processing schemes based on the resolution range of the initial image block pair and the target screen resolution; the candidate image processing scheme comprises adjusting resolutions respectively corresponding to the initial image blocks; processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme; inputting the candidate processing images into a trained image scoring model for scoring to obtain image scores corresponding to the candidate processing images; and selecting candidate processing images meeting the grading condition as target images based on the image grades.
In one embodiment, the training step module of the image scoring model is configured to: acquiring a real image and generating an image; inputting the real image into an image scoring model to be trained for scoring to obtain a first image score corresponding to the real image; inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image; obtaining a score model loss value based on the first image score and the second image score, wherein the score model loss value and the first image score are in a negative correlation relationship, and the score model loss value and the second image score are in a positive correlation relationship; and adjusting the model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
In one embodiment, the scoring condition comprises at least one of: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold.
In one embodiment, the training module of the resolution recognition model is configured to: acquiring a training image and acquiring a label image corresponding to the training image; partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks; acquiring training image blocks corresponding to the label image blocks in the training images; inputting the training image blocks into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image blocks; determining a resolution model loss value based on the prediction resolution range corresponding to the training image block and the resolution range corresponding to the label image block; and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
In one embodiment, the block processing module is configured to: carrying out object identification on the initial image to obtain a display object contained in the initial image; and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
The image processing method, the image processing device, the computer equipment, the storage medium and the computer program product have the following technical effects: the initial image can be blocked to obtain a plurality of image blocks, and the resolution range corresponding to each initial image block is determined based on the model, so that different image blocks of the initial image can be respectively processed in a self-adaptive manner according to the resolution range, the processed target image is adaptive to the screen resolution, and the display effect can be improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of an image processing method;
FIG. 2 is a flow diagram illustrating a method for image processing according to one embodiment;
FIG. 3 is an exemplary diagram illustrating a division of an initial image into a plurality of image blocks in one embodiment;
fig. 4 is a schematic flow chart illustrating that, in an embodiment, an initial image is processed based on a resolution range corresponding to an initial image block and a target screen resolution, and the processed target image is used as an image adapted to the target screen resolution;
FIG. 5 is a schematic flow chart diagram illustrating the training steps of the image scoring model in one embodiment;
FIG. 6 is a block diagram showing the configuration of an image processing apparatus according to an embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The image processing method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. The terminal 102 may send an initial image to be displayed to the server 104, and the server 104 may obtain screen resolutions of display devices according to which display devices the initial image is to be displayed on, as target screen resolutions, execute the method of the embodiment of the present application, obtain target images with each target screen resolution being adapted, and send the target images to the corresponding display devices for display. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In an embodiment, the server 104 may also store a plurality of different target screen resolutions in advance, that is, the server 104 may generate target images corresponding to different target screen resolutions in advance, and store a corresponding relationship between the images and the target screen resolutions. In this way, when an image is to be displayed on a certain display device, the server 104 may obtain the screen resolution corresponding to the display device, and select a target image adapted to the screen resolution of the display device based on the pre-stored correspondence.
In one embodiment, the image processing method provided by the embodiment of the application can be executed by a display device.
In one embodiment, as shown in fig. 2, an image processing method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S202, an initial image to be processed and a target screen resolution corresponding to the initial image are determined.
The initial image may be an image for displaying business data, and the initial image may include different types of content such as diagrams, characters, and texts. The target screen resolution is a resolution of a screen on which the initial image is displayed, and the target screen resolution may be preset or may be a screen resolution of a device to be displayed. The screen resolution refers to the number of pixels displayed on the screen, for example, a screen resolution of 800 × 1600 indicates that there are 800 pixels in the horizontal direction and 1600 pixels in the vertical direction. The target screen resolution may be multiple, and the content provided by the embodiment of the present application may be executed for each target screen resolution.
In the embodiment of the application, the server may respond to the image processing instruction, acquire an initial image to be processed indicated by the image processing instruction, and acquire a preset target screen resolution.
Step S204, the initial image is processed in a blocking mode to obtain a plurality of initial image blocks.
Blocking is the process of dividing an image. The division may be performed based on a blank area of the image, based on a type of content displayed in the image, or based on an input of the initial image to the image segmentation model. For example, if an image of service data is displayed by different diagrams, and a blank area exists between the diagrams, a boundary may be selected on the blank area to divide the image into a plurality of image blocks, where "plurality" refers to at least two.
In the embodiment of the application, the server acquires the initial image, can analyze the initial image, determines the division mode of the initial image, and divides the image based on the division mode to obtain a plurality of initial image blocks. For example, as shown in fig. 3, one image may be divided into 4 image blocks.
Step S206, inputting the initial image blocks into the trained resolution recognition model for processing, and obtaining the resolution ranges respectively corresponding to the initial image blocks.
Wherein, the larger the resolution, the more important the content in the image block is represented. The resolution identification model is used for identifying the resolution range of an image block in the image. Multiple resolutions can be selected within a resolution range. The resolution identification model determines a resolution range corresponding to each initial image block.
In the embodiment of the application, the server may input the initial image blocks obtained by dividing one initial image into the resolution identification model together, so as to obtain the resolution ranges corresponding to each initial image block. The resolution recognition model may be a model corresponding to a target screen resolution.
In an embodiment, the resolution recognition model is an artificial intelligence model obtained by pre-training, and when the model is trained, a training image and a corresponding image tag may be obtained, where the image tag includes a resolution corresponding to each training image block in the training image, the resolution corresponding to the training image block is determined based on a value corresponding to the training image block, and the higher the value is, the higher the resolution is. For example, the resolution corresponding to the training image block is determined manually based on the value of the training image block in the training image. For another example, an image pair may be obtained, where the image pair includes a training image and a label image corresponding to the training image, and the label image may be an image that is displayed with a good effect on a display device corresponding to the target screen resolution after the training image is manually adjusted. The resolution of the label image is less than or equal to the target screen resolution. The server may obtain a resolution of the label image on each image block as a resolution corresponding to a training image block in the image label. The training image can be input into a resolution recognition model to be trained for resolution recognition, a resolution range to which a predicted training image block belongs is obtained, a resolution range corresponding to the training image block in the image label is determined, and the smaller the difference between the predicted resolution range and the resolution range actually corresponding to the image label is, the smaller the model loss value is. For example, the corresponding relationship between the difference and the loss value may be preset, and assuming that the difference is 1 range, the loss value is a-b, and the difference is two ranges, the loss value is a. a and b are positive numbers. The server can adjust the model parameters of the resolution recognition model to be trained towards the direction that the model loss value becomes smaller, and the trained resolution recognition model is obtained.
And step S208, processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
The resolution of the image refers to the number of pixels in the image block. The resolution of the target image is smaller than or equal to the resolution of the target screen, that is, the number of the pixel points of the target image is smaller than or equal to the number of the pixel points corresponding to the resolution of the target screen in the horizontal direction and the vertical direction.
In this embodiment of the application, the server may select one of the resolutions from the resolution range corresponding to the initial image block, and adjust the resolution of the initial image block to a resolution matched with the selected resolution. And then combining the adjusted image blocks according to the positions in the initial image to obtain a target image.
In an embodiment, the resolution range output by the model may include a range of the number of pixels in the horizontal direction and a range of the number of pixels in the vertical direction, and when the resolution is selected, the resolution may be selected according to a ratio of the number of pixels in the horizontal direction to the number of pixels in the vertical direction in the initial image block, so that a difference between the ratio of the number of pixels selected in the horizontal direction to the number of pixels selected in the vertical direction is close to an original ratio of the number of pixels in the initial image block, and the close condition may be smaller than a threshold. For example, if there are 400 pixels in the horizontal direction and 800 pixels in the vertical direction on the initial image block, the ratio of the number of pixels in the horizontal direction to the number of pixels in the vertical direction is 1:2, and when the resolution is selected from the resolution range output by the model, the ratio of the number of pixels selected in the horizontal direction to the number of pixels selected in the vertical direction is as close as 1:2, which is better, for example, the difference from 1:2 is smaller than 0.01.
In the image processing method, the initial image can be blocked to obtain a plurality of image blocks, and the resolution range corresponding to each initial image block is determined based on the model, so that different image blocks of the initial image can be respectively processed according to the resolution ranges in a self-adaptive manner, the processed target image is adaptive to the screen resolution, and the display effect can be improved.
In an embodiment, as shown in fig. 4, processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adapted to the target screen resolution includes:
step S402, obtaining a plurality of candidate image processing schemes based on the resolution range corresponding to the initial image block and the target screen resolution; the candidate image processing scheme comprises the adjusted resolution ratio corresponding to each initial image block.
One candidate processing scheme includes the adjusted resolution of each initial image block in the initial image, which is referred to as an adjusted resolution for short. The initial image is processed based on the candidate processing scheme, and the resulting resolution of the target image is less than or equal to the screen resolution. That is, the adjusted resolutions of each initial image block in a candidate image processing scheme are added, and the sum of the obtained resolutions is smaller than the screen resolution. It can be understood that when the resolutions are added, the added number of the pixel points corresponding to different directions is smaller than the number of the pixel points corresponding to the screen resolution in the direction. For example, assuming that the screen resolution is 800 × 1600, the number of pixels of the screen in the horizontal direction is 800, and the number of pixels in the vertical direction is 1600, assuming that the initial image is divided into 4 image blocks on average as shown in fig. 3: A. b, C and D, the sum of the number of pixels of A and B in the horizontal direction, or the sum of the number of pixels of C and D in the horizontal direction in each candidate processing scheme is less than 800. The sum of the number of the pixels of A and C in the vertical direction, or the sum of the number of the pixels of B and D in the vertical direction is less than 1600.
In this embodiment of the present application, when forming a scheme, the server may select one resolution from a resolution range corresponding to each initial image block. The server may obtain a plurality of solutions, for example, 100 solutions, and then discard the solution having a resolution greater than the screen resolution among the solutions, and take the remaining solutions as candidate processing solutions.
In an embodiment, the resolution range output by the model may include a range of pixel numbers in a horizontal direction and a range of pixel numbers in a vertical direction, and when the candidate processing scheme is selected from the range of pixel numbers in the horizontal direction and the range of pixel numbers in the vertical direction, the candidate processing scheme may be selected according to a ratio of the pixel numbers in the horizontal direction and the vertical direction in the initial image block, so that a difference between the ratio of the pixel numbers selected in the horizontal direction and the vertical direction is close to an original ratio of the pixel numbers in the initial image block, and the close condition may be smaller than a threshold. For example, if there are 400 pixels in the horizontal direction and 800 pixels in the vertical direction on the initial image block, the ratio of the number of pixels in the horizontal direction to the number of pixels in the vertical direction is 1:2, and when the resolution is selected from the resolution range output by the model, the ratio of the number of pixels selected in the horizontal direction to the number of pixels selected in the vertical direction is as close as 1:2, which is better, for example, the difference from 1:2 is smaller than 0.01.
And step S404, processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme.
For example, the initial image is divided into 4 image blocks: A. b, C and D, wherein one candidate processing scheme is that A corresponds to a resolution of 100 × 200, B corresponds to a resolution of 300 × 400, C corresponds to a resolution of 100 × 300, D corresponds to a resolution of 200 × 200, if the original resolution of A is greater than 100 × 200, A is compressed to make the resolution of 100 × 200, and B, C and D can be processed, and A, B, C and D obtained by processing are combined into a candidate processing image corresponding to the candidate processing scheme.
Step S406, inputting the candidate processing images into an image scoring model for scoring to obtain image scores corresponding to the candidate processing images.
The image scoring model may be an artificial intelligence model for evaluating the presentation quality of the image or a strategy for manually determining the judgment of the presentation quality. The higher the score, the better the representative quality. For example, the image scoring model is an artificial intelligence model for judging whether the image is real or not, and the scoring model can be a discrimination model in the generation of a confrontation model. The discriminative model can be used to discriminate whether an image is a naturally captured image or a machine-based algorithmically synthesized image. If the strategy for judging the display quality is determined manually, the strategy can be to determine the difference between the proportion of the pixel point numbers of each image block in the candidate processing image in the horizontal direction and the vertical direction and the proportion of the pixel points of the image block in the initial image, and if the difference is smaller, the score is higher.
In the embodiment of the application, the candidate processing images are respectively input into the image scoring model for processing, and the image scoring model scores the quality of the candidate processing images to obtain the image scores corresponding to the candidate processing images.
In step S408, a candidate processing image satisfying the scoring condition is selected as a target image based on the image score.
In the embodiment of the present application, the candidate processing image may be selected only based on the scoring condition, or may be selected in combination with other conditions, for example, in combination with the resolution condition. The server may take the candidate processed image satisfying the flat-scoring condition and the resolution condition as the target image. The resolution condition may be, for example, that the resolution ordering is after a preset ordering, wherein the resolutions are ordered from large to small, so that the resolution of the resulting target image is as small as possible.
In one embodiment, the scoring condition includes at least one of the following conditions: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold. For example, the image with the largest image score among all candidate processed images is used as the target image. The sorting threshold and the scoring threshold can be set as required. For example, the ranking threshold may be 2 and the scoring threshold may be 80. The image scores are sorted in order from large to small. I.e., the higher the score, the higher the ranking.
In one embodiment, each candidate processing image may be sent to the terminal, the terminal receives a selection operation of the user, and the candidate processing image selected by the user is used as a target image adapted to the target screen resolution. That is, a plurality of candidate processing solutions may be formed, and processed images corresponding to the candidate processing solutions may be acquired and manually selected again. Because the resolution in the candidate processing scheme is in the resolution range identified by the resolution model, the candidate processing schemes are relatively less and more appropriate, so that the efficiency of manual selection can be improved, and an appropriate image can be selected.
In the embodiment of the application, the resolution of the image block is selected according to the resolution range to form a candidate processing scheme, and then the content scoring model is favorable for scoring.
The artificial intelligence model may be obtained through supervised training. In one embodiment, as shown in fig. 5, the training step of the image scoring model includes:
step S502, acquiring a real image and generating an image.
The generated image is an image generated by an artificial intelligence model, for example, an image generated by an image generation model. The real image is not generated by an artificial intelligence model, for example, an image obtained by shooting. When the real image is displayed in the display device corresponding to the target screen resolution, the quality of the real image is better than that of the generated image. The real image and the generated image may be preset, for example, may be manually selected.
Step S504, the real image is input into the image scoring model to be trained for scoring, and a first image score corresponding to the real image is obtained.
The image score model is, for example, a discrimination model in the generated countermeasure model, and the first image score is determined by using a discrimination value of the image outputted by the discrimination model as a true image. The higher the discrimination value, the higher the possibility that the representative image is a real image.
Step S506, inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image.
And taking the discrimination value of the real image output by the discrimination model as the second image score corresponding to the generated image. The higher the discrimination value, the higher the possibility that the representative image is a real image. The lower the discrimination value, the higher the likelihood that the representative image is a real image.
Step S508, a score model loss value is obtained based on the first image score and the second image score.
The model loss value is in a negative correlation relationship with the first image score, that is, the higher the first image score is, the better the model can recognize the real image as the real image, so the model loss value is smaller. The model loss value is positively correlated with the second image score. That is, the higher the second image score, the higher the probability that the model will generate an image that is wrong as a true image, and the worse the recognition capability, the larger the loss value of the model.
The model loss value can be calculated by using a cross entropy loss value calculation mode. The server may obtain a first loss value based on the first image score, obtain a second loss value based on the second image score, and add the first loss value and the second loss value to obtain a score model loss value.
And step S510, adjusting model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
In this embodiment of the application, the model parameters may be adjusted toward a direction in which the model loss value becomes smaller, so that the scoring capability of the model becomes higher and higher, where the model parameters may be adjusted multiple times, for example, when the training times reach a threshold value or the model loss value is smaller than the threshold value, the training is stopped, so as to obtain a trained image scoring model with high accuracy.
In the embodiment of the application, the image scoring model is obtained by training based on the real image and the generated image, so that the trained image scoring model has good identification capability for identifying whether one image is the real image or the generated image, a candidate processing image with better quality can be selected from the images as the target image, and the display effect of the target image is improved.
In some embodiments, the training of the resolution recognition model comprises: acquiring a training image and acquiring a label image corresponding to the training image; partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks; acquiring training image blocks corresponding to label image blocks in a training image; inputting the training image block into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image block; determining a resolution model loss value based on a predicted resolution range corresponding to the training image block and a resolution range corresponding to the label image block; and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
The label image and the training image are images having the same image content but different resolutions. The label image is adapted to the target screen resolution, while the training image is not. The resolution of different image blocks in the label image may be different, and the server may block the label image based on the resolution of the label image, so that at least two of the divided label image blocks have different resolutions, and it can be understood that an image area corresponding to one image block is continuous.
The training image is divided according to the division mode of the label image blocks, so that the label image blocks and the training image blocks have one-to-one correspondence, and the corresponding positions and the image contents of the image blocks with the correspondence are the same.
The prediction resolution range is obtained by identification based on the current model parameters of the resolution identification model. The resolution model loss value may be determined based on a difference between the prediction resolution range and the resolution range corresponding to the tag image block, wherein the greater the difference, the greater the loss value. For example, a loss value corresponding to each difference may be set, for example, if the difference is set to be one range, the loss value is c, and if the difference is set to be two ranges, the loss value is c + d, and c and d are both positive numbers. The server may perform parameter adjustment on the resolution recognition model in a direction in which the loss value becomes smaller, and use the adjusted model as a trained resolution recognition model, and it is understood that the model may be obtained through multiple times of training.
In the embodiment of the application, the loss value of the model is determined based on the label image of the training image during training, so that the difference between the resolution range output by the trained model and the resolution range of the label image is smaller through multiple times of training, and the resolution of the label image is adaptive to that of the target screen, so that the resolution range obtained by the recognition of the resolution recognition model is matched with the resolution of the target screen, and the recognition capability of the resolution recognition model is improved.
In one embodiment, the blocking the initial image to obtain a plurality of initial image blocks includes: carrying out object identification on the initial image to obtain a display object contained in the initial image; and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
The display object in the initial image is a complete display unit, which may be, for example, a table, a person, or a trend graph. When the blocking processing is performed, blocking is performed based on the display object, so that each initial image block includes a complete object, for example, one initial image block may correspond to one display object. For example, assuming that there is a table and a portrait in an image, the image is divided into two image blocks: image blocks comprising tables and image blocks comprising human images. Because the initial image is subjected to the blocking processing according to the display object, the resolution of the image is determined by taking the display object as a dimension, and the values contained in one display object have consistency, so that the display effect is improved.
In one embodiment, the display objects may be divided according to display purposes, and divided into display objects corresponding to the display purposes and display objects corresponding to non-display purposes. For example, if there is only one person in a diagram, and the display purpose of the diagram is to show the change of expression, the display objects include two: a face display object and a non-face display object.
With the development of the technology, a large data visualization screen becomes a requirement for a very explosive fire, but a very important problem of the large data screen is screen adaptation, and the method provided by the embodiment of the application can adaptively select corresponding images to be displayed based on the resolution of the large screen, for example, the resolution corresponding to each large screen can be obtained in advance, and an image to be displayed in the large screen is obtained, wherein the resolution of the image is generally higher than that of the screen, so that different content in the image can be allocated with different resolution ranges, and the finally obtained target image can be adapted to the resolution corresponding to the large screen and is an image with a relatively smaller resolution.
It should be understood that, although the steps in the flowcharts related to the embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides an image processing apparatus for implementing the image processing method. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the image processing apparatus provided below can be referred to the limitations of the image processing method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 6, there is provided an image processing apparatus including:
a resolution and image obtaining module 602, configured to determine an initial image to be processed and a target screen resolution corresponding to the initial image, where the target screen resolution is a resolution of a screen displaying the initial image;
a block processing module 604, configured to perform block processing on the initial image to obtain a plurality of initial image blocks;
a resolution range obtaining module 606, configured to input the initial image blocks into the trained resolution recognition model for processing, so as to obtain resolution ranges corresponding to the initial image blocks respectively;
and a target image obtaining module 608, configured to process the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and use the processed target image as an image adapted to the target screen resolution.
In one embodiment, the target image derivation module is to: obtaining a plurality of candidate image processing schemes based on the resolution range of the initial image block pair and the target screen resolution; the candidate image processing scheme comprises adjusting resolution ratios corresponding to the initial image blocks respectively; processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme; inputting the candidate processing images into a trained image scoring model for scoring to obtain image scores corresponding to the candidate processing images; and selecting candidate processing images meeting the grading condition as target images based on the image grades.
In one embodiment, the training step module of the image scoring model is configured to: acquiring a real image and generating an image; inputting the real image into an image scoring model to be trained for scoring to obtain a first image score corresponding to the real image; inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image; obtaining a score model loss value based on the first image score and the second image score, wherein the score model loss value and the first image score form a negative correlation relationship, and the score model loss value and the second image score form a positive correlation relationship; and adjusting the model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
In one embodiment, the scoring condition includes at least one of the following conditions: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold.
In one embodiment, the training module of the resolution recognition model is to: acquiring a training image and acquiring a label image corresponding to the training image; partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks; acquiring training image blocks corresponding to label image blocks in a training image; inputting the training image block into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image block; determining a resolution model loss value based on a predicted resolution range corresponding to the training image block and a resolution range corresponding to the label image block; and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
In one embodiment, the block processing module is to: carrying out object identification on the initial image to obtain a display object contained in the initial image; and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
The respective modules in the image processing apparatus described above may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing resolution data and picture data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an image processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of: determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image; carrying out blocking processing on the initial image to obtain a plurality of initial image blocks; inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively; and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (15)
1. An image processing method, characterized in that the method comprises:
determining an initial image to be processed and a target screen resolution corresponding to the initial image, wherein the target screen resolution is the resolution of a screen displaying the initial image;
carrying out blocking processing on the initial image to obtain a plurality of initial image blocks;
inputting the initial image blocks into a trained resolution recognition model for processing to obtain resolution ranges corresponding to the initial image blocks respectively;
and processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image adaptive to the target screen resolution.
2. The method according to claim 1, wherein the processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and the taking the processed target image as the image adapted to the target screen resolution comprises:
obtaining a plurality of candidate image processing schemes based on the resolution range corresponding to the initial image block and the target screen resolution; the candidate image processing scheme comprises adjusting resolutions respectively corresponding to the initial image blocks;
processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme;
inputting the candidate processing images into a trained image scoring model for scoring to obtain image scores corresponding to the candidate processing images;
and selecting candidate processing images meeting the grading condition as target images based on the image grades.
3. The method of claim 2, wherein the step of training the image scoring model comprises:
acquiring a real image and generating an image;
inputting the real image into an image scoring model to be trained for scoring to obtain a first image score corresponding to the real image;
inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image;
obtaining a score model loss value based on the first image score and the second image score, wherein the score model loss value and the first image score are in a negative correlation relationship, and the score model loss value and the second image score are in a positive correlation relationship;
and adjusting the model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
4. The method of claim 2, wherein the scoring condition comprises at least one of: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold.
5. The method of claim 1, wherein the step of training the resolution recognition model comprises:
acquiring a training image and acquiring a label image corresponding to the training image;
partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks;
acquiring training image blocks corresponding to the label image blocks in the training images;
inputting the training image blocks into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image blocks;
determining a resolution model loss value based on the prediction resolution range corresponding to the training image block and the resolution range corresponding to the label image block;
and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
6. The method of claim 1, wherein the blocking the initial image to obtain a plurality of initial image blocks comprises:
carrying out object identification on the initial image to obtain a display object contained in the initial image;
and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
7. An image processing apparatus, characterized in that the apparatus comprises:
the system comprises a resolution and image acquisition module, a resolution and image acquisition module and a processing module, wherein the resolution and image acquisition module is used for determining an initial image to be processed and a target screen resolution corresponding to the initial image, and the target screen resolution is the resolution of a screen displaying the initial image;
the block processing module is used for carrying out block processing on the initial image to obtain a plurality of initial image blocks;
a resolution range obtaining module, configured to input the initial image blocks into a trained resolution recognition model for processing, so as to obtain resolution ranges corresponding to the initial image blocks respectively;
and the target image obtaining module is used for processing the initial image based on the resolution range corresponding to the initial image block and the target screen resolution, and taking the processed target image as an image matched with the target screen resolution.
8. The apparatus of claim 7, wherein the target image derivation module is configured to:
obtaining a plurality of candidate image processing schemes based on the resolution range corresponding to the initial image block and the target screen resolution; the candidate image processing scheme comprises adjusting resolutions respectively corresponding to the initial image blocks;
processing the initial image based on the candidate image processing scheme to obtain a candidate processing image corresponding to the candidate processing scheme;
inputting the candidate processing images into a trained image scoring model for scoring to obtain image scores corresponding to the candidate processing images;
and selecting candidate processing images meeting the grading condition as target images based on the image grades.
9. The apparatus of claim 8, wherein the training step of the image scoring model is configured to:
acquiring a real image and generating an image;
inputting the real image into an image scoring model to be trained for scoring to obtain a first image score corresponding to the real image;
inputting the generated image into an image scoring model to be trained for scoring to obtain a second image score corresponding to the generated image;
obtaining a score model loss value based on the first image score and the second image score, wherein the score model loss value and the first image score are in a negative correlation relationship, and the score model loss value and the second image score are in a positive correlation relationship;
and adjusting the model parameters of the image scoring model to be trained based on the scoring model loss value to obtain the trained image scoring model.
10. The apparatus of claim 8, wherein the scoring condition comprises at least one of: the ranking of the image scores is before the ranking threshold, or the image scores are greater than the score threshold.
11. The apparatus of claim 7, wherein the training module of the resolution recognition model is configured to:
acquiring a training image and acquiring a label image corresponding to the training image;
partitioning the label image based on the resolution of the label image to obtain a plurality of label image blocks;
acquiring training image blocks corresponding to the label image blocks in the training images;
inputting the training image blocks into a resolution recognition model to be trained for processing to obtain a prediction resolution range corresponding to the training image blocks;
determining a resolution model loss value based on the prediction resolution range corresponding to the training image block and the resolution range corresponding to the label image block;
and adjusting parameters of the resolution recognition model to be trained based on the loss value of the resolution model to obtain the trained resolution recognition model.
12. The apparatus of claim 7, wherein the block processing module is configured to:
carrying out object identification on the initial image to obtain a display object contained in the initial image;
and carrying out block processing on the initial image based on the display object to obtain a plurality of initial image blocks.
13. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
15. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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