CN113284206A - Information acquisition method and device, computer readable storage medium and electronic equipment - Google Patents
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Abstract
The disclosure relates to the technical field of data processing, and provides an information acquisition method and device, a computer readable storage medium and an electronic device, wherein the method comprises the following steps: acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image; projecting the reference image and the migration image by using a projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image; and performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph. According to the method and the device, the migration image and the reference image are projected to a single channel through the projection vector, the target migration result information is obtained in a non-parametric mode, and the accuracy of the target migration result information is improved.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an information acquisition method, an information acquisition apparatus, a computer-readable storage medium, and an electronic device.
Background
With the development of image processing technology, image processing software and image processing equipment have been widely used. The image color migration refers to adjusting the color information of the current input image according to the color information of the specified reference image to obtain an output image. The output image has the same color information as the reference image and the same shape information as the current input image.
At present, a plurality of image color migration schemes exist, color migration results corresponding to the plurality of color migration schemes are different, and how to quantitatively evaluate a color migration effect becomes important. In the prior art, a kernel function is used to parameterize an image, and a color migration result is obtained according to KL divergence. However, the method of approximating an image using a kernel function is low in accuracy, and further, the evaluation accuracy of the migration result is low.
Disclosure of Invention
The present disclosure is directed to an information obtaining method, an information obtaining apparatus, a computer-readable storage medium, and an electronic device, so as to solve, at least to a certain extent, the problem of low information obtaining accuracy in the prior art.
According to a first aspect of the present disclosure, there is provided an information acquisition method, the method including: acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image; projecting the reference image and the migration image by using a projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image; and performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph.
According to a second aspect of the present disclosure, there is provided an information acquisition apparatus comprising: the image acquisition module is used for acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image; the projection module is used for projecting the reference image and the migration image by using a projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image; and the information acquisition module is used for performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph.
According to a third aspect of the present disclosure, there is provided a computer-readable medium, on which a computer program is stored, which when executed by a processor, implements the information acquisition method as described in the above embodiments.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the information acquisition method as described in the above embodiments.
As can be seen from the foregoing technical solutions, the information obtaining method, apparatus, system, computer readable storage medium, and electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
firstly, acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image; secondly, projecting the reference image and the migration image by using the projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image; and finally, performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph. According to the information acquisition method, the migration image and the reference image are projected to the single channel through the projection vector, histogram matching is directly carried out on the reference projection image and the migration projection image of the single channel, the color probability distribution of the image is obtained, the target migration result information is acquired based on the color probability distribution, and the accuracy of the target migration result information is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically illustrates a schematic diagram of a system architecture of the present exemplary embodiment;
fig. 2 schematically shows a schematic view of an electronic device of the present exemplary embodiment;
fig. 3 schematically shows a flow diagram of an information acquisition method according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flowchart of a method of obtaining target migration result information, according to an embodiment of the present disclosure;
fig. 5 schematically illustrates a flowchart of a method of obtaining second migration result information according to an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flowchart of a method of obtaining target migration result information, according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow diagram of an information acquisition method according to a specific embodiment of the present disclosure;
fig. 8 schematically shows a block diagram of an information acquisition apparatus according to an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
In the related art, color migration of an image refers to modifying a color style of an input image by using colors of a reference image to obtain a migrated image having the same shape as the input image and the same color style as the reference image.
Based on the problems in the related art, the embodiments of the present disclosure first provide an information obtaining method, which is applied to a system architecture of an exemplary embodiment of the present disclosure. Fig. 1 shows a schematic diagram of a system architecture of an exemplary embodiment of the present disclosure, and as shown in fig. 1, the system architecture 100 may include: terminal 110, network 120, and server 130. The terminal 110 may be various electronic devices with audio acquisition functions, including but not limited to a mobile phone, a tablet computer, a personal computer, a smart wearable device, and the like. The medium used by network 120 to provide communications links between terminals 110 and server 130 may include various connection types, such as wired, wireless communications links, or fiber optic cables. It should be understood that the number of terminals, networks, and servers in fig. 1 are merely illustrative. There may be any number of terminals, networks, and servers, as desired for an implementation. For example, the server 130 may be a server cluster composed of a plurality of servers, and the like.
The information obtaining method provided by the embodiment of the present disclosure may be executed by the terminal 110, for example, obtaining the migration image and the reference image at the terminal 110, and obtaining the target migration result information according to the migration image and the reference image.
In addition, the information obtaining method provided by the embodiment of the present disclosure may also be executed by the server 130, for example, after the terminal 110 obtains the migration image and the reference image, the migration image and the reference image are uploaded to the server 130, so that the server 130 obtains the target migration result information of the migration image according to the migration image and the reference image, and returns the target migration result information to the terminal 110, which is not limited in the present disclosure.
An exemplary embodiment of the present disclosure provides an electronic device for implementing an information acquisition method, which may be the terminal 110 or the server 130 in fig. 1. The electronic device comprises at least a processor and a memory for storing executable instructions of the processor, the processor being configured to perform the information obtaining method via execution of the executable instructions.
The electronic device may be implemented in various forms, and may include, for example, a mobile device such as a mobile phone, a tablet computer, a notebook computer, a Personal Digital Assistant (PDA), a navigation device, a wearable device, an unmanned aerial vehicle, and a stationary device such as a desktop computer and a smart television.
The following takes the mobile terminal 200 in fig. 2 as an example, and exemplifies the configuration of the electronic device. It will be appreciated by those skilled in the art that the configuration of figure 2 can also be applied to fixed type devices, in addition to components specifically intended for mobile purposes. In other embodiments, mobile terminal 200 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. The interfacing relationship between the components is only schematically illustrated and does not constitute a structural limitation of the mobile terminal 200. In other embodiments, the mobile terminal 200 may also interface differently than shown in fig. 2, or a combination of multiple interfaces.
As shown in fig. 2, the mobile terminal 200 may specifically include: the mobile terminal includes a processor 210, an internal memory 221, an external memory interface 222, a USB interface 230, a charging management Module 240, a power management Module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication Module 250, a wireless communication Module 260, an audio Module 270, a speaker 271, a microphone 272, a microphone 273, an earphone interface 274, a sensor Module 280, a display screen 290, a camera Module 291, an indicator 292, a motor 293, a button 294, a Subscriber Identity Module (SIM) card interface 295, and the like. The sensor module 280 may include a depth sensor 2801, a pressure sensor 2802, a gyroscope sensor 2803, a barometric pressure sensor 2804, and the like.
Processor 210 may include one or more processing units, such as: the Processor 210 may include an Application Processor (AP), a modem Processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a video codec, a Digital Signal Processor (DSP), a baseband Processor, and/or a Neural-Network Processing Unit (NPU), and the like. The different processing units may be separate devices or may be integrated into one or more processors.
The NPU is a Neural-Network (NN) computing processor, which processes input information quickly by using a biological Neural Network structure, for example, by using a transfer mode between neurons of a human brain, and can also learn by itself continuously. The NPU can implement applications such as intelligent recognition of the mobile terminal 200, for example: image recognition, face recognition, speech recognition, text understanding, and the like.
A memory is provided in the processor 210. The memory may store instructions for implementing six modular functions: detection instructions, connection instructions, information management instructions, analysis instructions, data transmission instructions, and notification instructions, and execution is controlled by processor 210.
The charge management module 240 is configured to receive a charging input from a charger. The power management module 241 is used for connecting the battery 242, the charging management module 240 and the processor 210. The power management module 241 receives the input of the battery 242 and/or the charging management module 240, and supplies power to the processor 210, the internal memory 221, the display screen 290, the camera module 291, the wireless communication module 260, and the like.
The wireless communication function of the mobile terminal 200 may be implemented by the antenna 1, the antenna 2, the mobile communication module 250, the wireless communication module 260, a modem processor, a baseband processor, and the like. Wherein, the antenna 1 and the antenna 2 are used for transmitting and receiving electromagnetic wave signals; the mobile communication module 250 may provide a solution including wireless communication of 2G/3G/4G/5G, etc. applied to the mobile terminal 200; the modem processor may include a modulator and a demodulator; the Wireless communication module 260 may provide a solution for Wireless communication including a Wireless Local Area Network (WLAN) (e.g., a Wireless Fidelity (Wi-Fi) network), Bluetooth (BT), and the like, applied to the mobile terminal 200. In some embodiments, antenna 1 of the mobile terminal 200 is coupled to the mobile communication module 250 and antenna 2 is coupled to the wireless communication module 260, such that the mobile terminal 200 may communicate with networks and other devices via wireless communication techniques.
The mobile terminal 200 implements a display function through the GPU, the display screen 290, the application processor, and the like. The GPU is a microprocessor for image processing, and is connected to the display screen 290 and an application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 210 may include one or more GPUs that execute program instructions to generate or alter display information.
The mobile terminal 200 may implement a photographing function through the ISP, the camera module 291, the video codec, the GPU, the display screen 290, the application processor, and the like. The ISP is used for processing data fed back by the camera module 291; the camera module 291 is used for capturing still images or videos; the digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals; the video codec is used to compress or decompress digital video, and the mobile terminal 200 may also support one or more video codecs.
The external memory interface 222 may be used to connect an external memory card, such as a Micro SD card, to extend the memory capability of the mobile terminal 200. The external memory card communicates with the processor 210 through the external memory interface 222 to implement a data storage function. For example, files such as music, video, etc. are saved in an external memory card.
Internal memory 221 may be used to store computer-executable program code, which includes instructions. The internal memory 221 may include a program storage area and a data storage area. The storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like. The storage data area may store data (e.g., audio data, a phonebook, etc.) created during use of the mobile terminal 200, and the like. In addition, the internal memory 221 may include a high-speed random access memory, and may further include a nonvolatile memory, such as at least one magnetic disk Storage device, a Flash memory device, a Universal Flash Storage (UFS), and the like. The processor 210 executes various functional applications of the mobile terminal 200 and data processing by executing instructions stored in the internal memory 221 and/or instructions stored in a memory provided in the processor.
The mobile terminal 200 may implement an audio function through the audio module 270, the speaker 271, the receiver 272, the microphone 273, the earphone interface 274, the application processor, and the like. Such as music playing, recording, etc.
The depth sensor 2801 is used to acquire depth information of a scene. In some embodiments, a depth sensor may be provided to the camera module 291.
The pressure sensor 2802 is used to sense a pressure signal and convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 2802 may be disposed on the display screen 290. Pressure sensor 2802 can be of a wide variety, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like.
The gyro sensor 2803 may be used to determine a motion gesture of the mobile terminal 200. In some embodiments, the angular velocity of the mobile terminal 200 about three axes (i.e., x, y, and z axes) may be determined by the gyroscope sensor 2803. The gyro sensor 2803 can be used to photograph anti-shake, navigation, body-feel game scenes, and the like.
In addition, other functional sensors, such as an air pressure sensor, a magnetic sensor, an acceleration sensor, a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, a bone conduction sensor, etc., may be provided in the sensor module 280 according to actual needs.
Other devices for providing auxiliary functions may also be included in mobile terminal 200. For example, the keys 294 include a power-on key, a volume key, and the like, and a user can generate key signal inputs related to user settings and function control of the mobile terminal 200 through key inputs. Further examples include indicator 292, motor 293, SIM card interface 295, etc.
The following specifically describes an information acquisition method and an information acquisition apparatus according to exemplary embodiments of the present disclosure. Fig. 3 shows a flow chart of an information acquisition method, which, as shown in fig. 3, at least includes the following steps:
step S310: acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image;
step S320: projecting the reference image and the migration image by using the projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image;
step S330: and performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph.
According to the information acquisition method, the migration image and the reference image are projected to the single channel through the projection vector, histogram matching is directly carried out on the reference projection image and the migration projection image of the single channel, the color probability distribution of the image is obtained, the target migration result information is acquired based on the color probability distribution, and the accuracy of the target migration result information is improved.
In order to make the technical solution of the present disclosure clearer, each step of the information acquisition method is explained next.
In step S310, a migration image and a reference image are acquired, and the migration image is an image obtained after color migration is performed on the target image according to the reference image.
In an exemplary embodiment of the present disclosure, the target image refers to an image to be processed for color migration. The target image may be an image captured by the image capturing unit, may be an image drawn by image editing software, and may also be other types of images to be processed designated for color migration, which is not specifically limited in this exemplary embodiment.
The reference image refers to a source image for providing texture information and color information used for color migration, and may be any one of images. For example, the reference image may be an image with a cool and warm tone, or may be an image with a canvas style, which is not specifically limited in this exemplary embodiment.
The color migration refers to extracting color features from a specified reference image, and performing color migration on the content structure of the target image by using the extracted color features on the premise of not damaging the content structure of the target image to obtain a migrated image. The obtained migration image has the structure information and the shape information of the target image, and simultaneously has the texture information and the color information of the reference image.
For example, the target image B is color-shifted based on the reference image a to obtain a shifted image C having texture information and color information similar or identical to those of the reference image a, and having structure information and shape information similar or identical to those of the target image B.
In step S320, the reference image and the transition image are projected by using the projection vector to obtain a reference projection view corresponding to the reference image and a transition projection view corresponding to the transition image.
In an exemplary embodiment of the present disclosure, a projection vector may be configured in advance, and the reference image and the migration image may be projected using the projection vector. The projection vector may be a three-dimensional projection vector (x, y, z), and x, y, and z in the three-dimensional projection vector may be any numerical value, which is not specifically limited in this example embodiment.
Specifically, the RGB pixel values of each pixel in the reference image and the migration image are projected to a single dimension according to the projection vector (x, y, z) to obtain a single-dimension pixel value corresponding to each pixel. And respectively constructing a reference projection graph and a migration projection graph corresponding to the reference image and the migration image according to the single-dimension pixel values corresponding to the pixels in the reference image and the migration image. Wherein the reference projection view and the migration projection view are single-channel images.
For example, if a pixel value on an RGB channel corresponding to a certain pixel point on the reference image is (r, g, b), the pixel point is projected by using the projection vector, and the obtained single-dimensional pixel value is v ═ r × x + g × y + b × z. And respectively projecting each pixel point in the reference image, and constructing a reference projection graph corresponding to the reference image according to the single-dimensional pixel value corresponding to each pixel point.
In addition, a plurality of projection vectors may be arranged, and the reference image and the transition image may be projected by using the plurality of projection vectors, respectively, to obtain a plurality of reference projection views and a plurality of transition projection views, respectively. Of course, the number of the projection vectors may be 216 or 360, and the number of the projection vectors is not particularly limited in the present disclosure.
In step S330, histogram matching is performed on the reference projection view and the migration projection view to obtain a projection matching view, and target migration result information of the migration image is acquired from the projection matching view and the migration projection view.
In an exemplary embodiment of the present disclosure, the target migration result information may include a target migration result score, and the target migration result score of the migration image may characterize a migration error of color migration of the target image based on the reference image. For example, the larger the target migration result score in the target migration result information is, the larger the migration error of the migration image is, and the poor migration effect is indicated.
In an exemplary embodiment of the present disclosure, histogram matching, also called histogram normalization, is an operation of transforming a gray distribution of a certain image according to a certain specific pattern. Namely, the histogram of the template image is matched to the original image to obtain a matched image. The original image and the matched image have the same size, and the template image and the matched image have the same probability distribution.
Specifically, the reference projection image is used as a template image in histogram matching, the transition projection image is used as an original image in histogram matching, and histogram matching is performed to obtain a projection matching image. The projected matching graph has the same probability distribution as the reference projected graph, and the projected matching graph has the same size as the reference projected graph.
In addition, when a plurality of reference projection views and a plurality of transition projection views are obtained from a plurality of projection vectors, histogram matching is performed on the reference projection views and the transition projection views corresponding to the same projection vector, and a plurality of projection matching views are obtained.
In an exemplary embodiment of the present disclosure, a pixel difference between a pixel value of each pixel in the projection matching image and a pixel value of a pixel point at the same position in the migration projection image is obtained, and target migration result information of the migration image is obtained according to the pixel difference of each pixel.
The absolute value of the pixel difference corresponding to each pixel point in the migration image may be calculated, and the absolute value of the pixel difference corresponding to each pixel point in the migration image may be configured as the target migration result information of the migration image. The target migration result information of the migration image is the migration error corresponding to each pixel point in the migration image, and if the absolute value of the pixel difference corresponding to a certain pixel point is larger, it indicates that the migration error of the pixel point is larger, that is, the color migration effect of the pixel point is poorer.
In addition, the sum of the absolute values of the pixel differences corresponding to all the pixel points in the migration image can be calculated, and the sum of the absolute values corresponding to all the pixel points in the migration image is configured as the target migration result information of the migration image. And the target migration result information of the migration image is the migration error corresponding to the migration image, and if the sum of the absolute values corresponding to all the pixel points in the migration image is larger, the overall color migration effect of the migration image is poor.
In addition, the average value of the absolute values of the pixel differences corresponding to all the pixel points in the migration image may be calculated, and the average value of the absolute values of the pixel differences corresponding to all the pixel points in the migration image is configured as the target migration result information of the migration image.
In an exemplary embodiment of the present disclosure, if there are a plurality of projection matching maps, a pixel difference between each pixel value in the migration projection map and a pixel value of a pixel point at the same position in each projection matching map is calculated, respectively. And each pixel point in the migration projection graph corresponds to a plurality of pixel differences, and the sum of the absolute values of the pixel differences corresponding to each pixel point or the average value of the absolute values of the pixel differences is used as target migration result information corresponding to each pixel point.
In addition, the sum of the target migration result information corresponding to all the pixel points in the migration image, or the average value of the target migration result information corresponding to all the pixel points may be used as the target migration result information of the migration image.
For example, if N projection vectors are configured, first, N migration projection graphs and N reference projection graphs are obtained according to the N projection vectors, and histogram matching is performed on the migration projection graphs and the reference projection graphs obtained by using the same projection vector, so as to obtain N projection matching graphs;
then, respectively calculating pixel differences between each pixel point in each migration projection image and pixel values of pixel points at the same position in the corresponding projection matching image, namely obtaining N pixel differences corresponding to each pixel point, and calculating the average pixel difference of the N pixel differences corresponding to each pixel point;
and finally, calculating the sum of the average pixel differences of all the pixel points in the migration image or the average value of the average pixel differences corresponding to all the pixel points to obtain target migration result information of the migration image.
Note that the L1 norm may be used to calculate an error between the migration projection diagram and the projection matching diagram, and the error between the migration projection diagram and the projection matching diagram may be used as the target migration result information of the migration image. That is, the error between the migration projection pattern E and the projection matching pattern F is mean (abs (E-F)).
In an exemplary embodiment of the disclosure, fig. 4 is a schematic flowchart illustrating a method for obtaining target migration result information, and as shown in fig. 4, the flowchart at least includes steps S410 to S430, which are described in detail as follows:
in step S410, first transition result information of the transition image is acquired from the pixel values of the respective pixels in the projection matching map and the transition projection map.
In an exemplary embodiment of the present disclosure, a pixel difference between a pixel value of each pixel in the projection matching map and a pixel value of a pixel at the same position in the migration projection map may be acquired, and a sum of the pixel differences corresponding to each pixel may be configured as the first migration result information. The method for obtaining the first migration result information of the migration image according to the pixel values of the pixels in the projection matching graph and the migration projection graph is the same as the method for obtaining the target migration result information of the migration image according to the pixel values of the pixels in the projection matching graph and the migration projection graph in the above embodiment, and has been described in detail in the above embodiment, and details are not repeated here.
In step S420, a distribution model of the reference image is constructed from the pixel information of the reference image, and second migration result information corresponding to the migration image is calculated from the distribution model.
In an exemplary embodiment of the present disclosure, pixel values of a reference image are input into a gaussian mixture model to obtain a probability distribution model corresponding to the reference image.
Specifically, a Gaussian Mixed Model (GMM) is a Model that accurately quantizes objects by using a Gaussian probability density function and decomposes the objects into a plurality of objects based on the Gaussian probability density function (normal distribution curve). The GMM model may perform a mixed gaussian distribution on the input reference image to obtain a probability distribution model corresponding to the reference image.
Different reference images correspond to different probability distribution models, the probability distribution models corresponding to the reference images can be constructed in advance, and the probability distribution models are stored in a database. And when the color of the target image is transferred according to the reference image, directly acquiring a probability distribution model corresponding to the reference image from the database.
In an exemplary embodiment of the disclosure, fig. 5 is a flowchart illustrating a method for obtaining second migration result information, and as shown in fig. 5, the flowchart at least includes step S510 to step S520, which is described in detail as follows: in step S510, inputting the pixel value of each pixel in the migration image into the probability distribution model to obtain distribution information corresponding to each pixel in the migration image; in step S520, the distribution information corresponding to each pixel in the migration image is summed to obtain second migration result information.
Specifically, the distribution information corresponding to each pixel in the migration image is summed, that is, the distribution information corresponding to all pixels in the migration image is added, and the result obtained by the addition is the second migration result information of the migration image. Wherein the size of the second migration result information represents the color difference between the migration image and the reference image. The larger the second migration result information is, the larger the difference in color between the migrated image and the reference image is, indicating that the color migration effect of the target image based on the reference image is worse.
In step S430, target migration result information of the migration image is acquired according to the first migration result information and the second migration result information.
In an exemplary embodiment of the present disclosure, after the first migration result information and the second migration result information are obtained, a weight value is assigned to the first migration result information and the second migration result information, and the target migration result information of the migration image is calculated according to the weight value corresponding to the first migration result information and the second migration result information.
In particular, the first migration junction may be formedThe weight values of the fruit information and the second migration result information are respectively configured as k1And k2Then, the target migration result information may be obtained according to formula (1), where formula (1) is as follows:
w＝k1a+k2b (1)
wherein a is first migration result information, b is second migration result information, and w is target migration result information. k is a radical of1And k2The value of (a) can be set according to the actual scene, for example, k1Can be set to 1, k2May be set to 10, etc., and the present disclosure does not specifically limit this.
In addition, if the first migration result information includes the pixel position of each pixel in the migration image and the first migration result information corresponding to each pixel position. The distribution information corresponding to each pixel in the migration image may be configured as second migration result information, and then the target migration result information corresponding to each pixel in the migration image is obtained by using the formula (1).
In an exemplary embodiment of the disclosure, fig. 6 is a schematic flowchart illustrating a method for obtaining target migration result information, and as shown in fig. 6, the flowchart at least includes steps S610 to S630, which are described in detail as follows:
in step S610, inputting pixel values of pixels in the reference image into a gaussian mixture model to obtain a probability distribution model corresponding to the reference image;
in step S620, respectively inputting the pixel value of each pixel in the migration image into the probability distribution model corresponding to the reference image to obtain distribution information corresponding to each pixel in the migration image;
in step S630, the distribution information corresponding to all pixels in the migration image is summed, and the summation result is configured as target migration result information of the migration image.
In the method for acquiring the target migration result information in the embodiment, the probability of the occurrence of the pixel in the migration image in the reference image is calculated by using the maximum likelihood, and the result of color migration is analyzed from the global perspective.
In an exemplary embodiment of the present disclosure, when the target migration result information of the migration image does not satisfy the preset migration condition, weighted average processing is performed on pixel values of pixels in the migration image and the reference image, and the migration image is updated according to a result of the weighted average processing.
Specifically, whether the target migration result information of the migration image meets a preset migration condition is determined, where the preset migration condition may be that a target migration result score in the target migration result information is smaller than a score threshold, and if the target migration result score is smaller than the score threshold, it is determined that the target migration result information meets the preset migration condition. The preset migration condition and the score threshold may be set only according to an actual application scenario, which is not specifically limited by the present disclosure.
And when the target migration result information of the migration image does not meet the preset migration condition, performing weighted average on all pixel values at the same position in the migration image and the reference image, and taking the pixel value after weighted average as the pixel value of the updated migration image. For example, if the RGB pixel value at a certain pixel point in the migration image is (100,100,100), the RGB pixel value at the same pixel point in the reference image is (60,60, 60). If the same weight value of 0.5 can be set for the pixel values of the reference image and the migration image, the average pixel value at the same pixel point in the migration image and the reference image can be obtained as (80,80,80), and the RGB pixel value at the pixel point in the migration image is updated as (80,80, 80). Of course, it is also possible to set different weight values for the pixel values of the reference image and the migration image, and update the RGB pixel values at the pixel point in the migration image to (70,70, 70). The weighted values of the pixel values of the reference image and the migration image may be set according to the actual application scenario, which is not specifically limited by the present disclosure.
Fig. 7 is a schematic flowchart illustrating an information obtaining method according to this embodiment, and as shown in fig. 7, the flowchart at least includes steps S710 to S780, and the following is described in detail:
in step S710, a migration image and a reference image are acquired;
the migration image is an image obtained after color migration is carried out on the target image according to the reference image.
In step S720, projecting the transition image of the reference image by using the projection vector to obtain a reference projection map corresponding to the reference image and a transition projection map corresponding to the transition image;
wherein, a plurality of projection vectors can be set, and a plurality of reference projection drawings and transition projection drawings are obtained based on the plurality of projection vectors.
In step S730, histogram matching is performed on the reference projection drawing and the migration projection drawing to obtain a projection matching drawing;
in step S740, a pixel difference between a pixel value of each pixel in the projection matching image and a pixel value of each pixel in the migration projection image is obtained, an average value of pixel differences corresponding to all pixel values is calculated, and the average value of pixel differences is configured as first migration result information;
in step S750, inputting the pixel values of the reference image into a gaussian mixture model to obtain a probability distribution model corresponding to the reference image;
in step S760, inputting the pixel value of each pixel in the migration image into the probability distribution model corresponding to the reference image to obtain distribution information corresponding to each pixel in the migration image;
in step S770, summing the distribution information corresponding to all pixels in the migration image to obtain second migration result information of the migration image;
in step S780, a weight value is assigned to the first migration result information and the second migration result information of the migration image, and the target migration result information is obtained according to the weight values corresponding to the first migration result information and the second migration result information.
In the method for acquiring target migration result information in the embodiment, first, the probability of occurrence of a pixel in a migration image in a reference image is calculated by using maximum likelihood to obtain first migration result information; then, by utilizing the marginal probability error, the reference image and the migration image are projected to a single channel, histogram matching is carried out on the projection image of the single channel, and second migration result information is obtained according to the matching result; and finally, obtaining target migration result information according to the first migration result information and the second migration result information. According to the information acquisition method, the color migration result is objectively analyzed from the overall and detail angles, so that the accuracy of the target migration result information is improved; and the migration image is updated according to the color migration result, so that the accuracy of color migration is improved, and further the user experience is improved.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. The computer program, when executed by the CPU, performs the functions defined by the method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following describes embodiments of the apparatus of the present disclosure, which may be used to perform the above-mentioned information acquisition method of the present disclosure. For details that are not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the information obtaining method described above in the present disclosure.
Fig. 8 schematically shows a block diagram of an information acquisition apparatus according to one embodiment of the present disclosure.
Referring to fig. 8, an information acquisition apparatus 800 according to an embodiment of the present disclosure, the information acquisition apparatus 800 includes: an image acquisition module 801, an image projection module 802, and an information acquisition module 803. Specifically, the method comprises the following steps:
an image obtaining module 801, configured to obtain a migration image and a reference image, where the migration image is an image obtained after color migration is performed on a target image according to the reference image;
an image projection module 802, configured to project the reference image and the migration image by using the projection vector to obtain a reference projection view corresponding to the reference image and a migration projection view corresponding to the migration image;
an information obtaining module 803, configured to perform histogram matching on the reference projection drawing and the migration projection drawing to obtain a projection matching drawing, and obtain target migration result information of the migration image according to the projection matching drawing and the migration projection drawing.
In an exemplary embodiment of the present disclosure, the information acquisition module 803 may further include a first information acquisition unit, a second information acquisition unit, and a target information acquisition unit, wherein:
a first information acquisition unit, configured to acquire first migration result information of the migration image according to the pixel values of the pixels in the projection matching graph and the migration projection graph;
the second information acquisition unit is used for constructing a distribution model of the reference image according to the pixel information of the reference image and calculating second migration result information corresponding to the migration image according to the distribution model;
and the target information acquisition unit is used for acquiring the target migration result information of the migration image according to the first migration result information and the second migration result information.
In an exemplary embodiment of the present disclosure, the second information obtaining unit may be further configured to input pixel values of the reference image into a gaussian mixture model to obtain a probability distribution model corresponding to the reference image.
In an exemplary embodiment of the present disclosure, the second information obtaining unit may be further configured to input a pixel value of each pixel in the migration image into the probability distribution model to obtain distribution information corresponding to each pixel in the migration image; and summing the distribution information corresponding to each pixel in the migration image to obtain second migration result information.
In an exemplary embodiment of the present disclosure, the first information acquiring unit may be further configured to acquire pixel differences between pixel values of each pixel in the projection matching image and pixel values of each pixel in the migration projection image, and configure a sum of the pixel differences corresponding to each pixel as the first migration result information.
In an exemplary embodiment of the present disclosure, the target information obtaining unit may be further configured to assign a weight value to the first migration result information and the second migration result information; and calculating target migration result information of the migration image according to the weight values corresponding to the first migration result information and the second migration result information.
In an exemplary embodiment of the present disclosure, the information obtaining apparatus 800 may further include an image updating module (not shown in the figure), configured to perform weighted average processing on pixel values of each pixel in the migration image and the reference image when the target migration result information of the migration image does not satisfy the preset migration condition, and update the migration image according to a result of the weighted average processing.
The specific details of each module in the information acquisition apparatus have been described in detail in the embodiment of the information acquisition method section, and details that are not disclosed may refer to the embodiment of the information acquisition method section, and thus are not described again.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
Exemplary embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product including program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the above-mentioned "exemplary methods" section of this specification, when the program product is run on the terminal device, for example, any one or more of the steps in fig. 3 to 7 may be performed.
Exemplary embodiments of the present disclosure also provide a program product for implementing the above method, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An information acquisition method, comprising:
acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image;
projecting the reference image and the migration image by using a projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image;
and performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph.
2. The information acquisition method according to claim 1, wherein acquiring target migration result information of the migration image from the projection matching map and the migration projection map includes:
acquiring first migration result information of the migration image according to the projection matching image and the pixel value of each pixel in the migration projection image; and
constructing a distribution model of the reference image according to the pixel information of the reference image, and calculating second migration result information corresponding to the migration image according to the distribution model;
and acquiring target migration result information of the migration image according to the first migration result information and the second migration result information.
3. The information acquisition method according to claim 2, wherein constructing the distribution model of the reference image from the pixel information of the reference image comprises:
and inputting the pixel values of the reference image into a Gaussian mixture model to obtain a probability distribution model corresponding to the reference image.
4. The information acquisition method according to claim 3, wherein calculating second migration result information corresponding to the migration image from the probability distribution model includes:
inputting the pixel value of each pixel in the migration image into the probability distribution model to obtain distribution information corresponding to each pixel in the migration image;
and summing the distribution information corresponding to each pixel in the migration image to obtain the second migration result information.
5. The information acquisition method according to claim 2, wherein acquiring first transition result information of the transition image from pixel values of respective pixels in the projection matching map and the transition projection map includes:
and acquiring pixel differences between the pixel values of the pixels in the projection matching image and the pixel values of the pixels in the migration projection image, and configuring the sum of the pixel differences corresponding to the pixels as the first migration result information.
6. The information acquisition method according to claim 1, wherein acquiring target migration result information of the migration image based on the first migration result information and the second migration result information includes:
assigning a weight value to the first migration result information and the second migration result information;
and calculating target migration result information of the migration image according to the weight values corresponding to the first migration result information and the second migration result information.
7. The information acquisition method according to claim 1, characterized in that the method further comprises:
and when the target migration result information of the migration image does not meet the preset migration condition, performing weighted average processing on the pixel values of the pixels in the migration image and the reference image, and updating the migration image according to the weighted average processing result.
8. An information acquisition apparatus characterized by comprising:
the image acquisition module is used for acquiring a migration image and a reference image, wherein the migration image is an image obtained after color migration is carried out on a target image according to the reference image;
the image projection module is used for projecting the reference image and the migration image by using a projection vector to obtain a reference projection image corresponding to the reference image and a migration projection image corresponding to the migration image;
and the information acquisition module is used for performing histogram matching on the reference projection graph and the migration projection graph to obtain a projection matching graph, and acquiring target migration result information of the migration image according to the projection matching graph and the migration projection graph.
9. A computer-readable storage medium on which a computer program is stored, the program implementing the information acquisition method according to any one of claims 1 to 7 when executed by a processor.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the information acquisition method according to any one of claims 1 to 7.
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