Exposure imaging method, device, equipment, storage medium, and program product

Abstract

The present disclosure relates to an exposure imaging method, apparatus, device, storage medium, and program product, the method including: obtaining an imaging image, which is an image captured without exposure compensation; determining multi-dimensional information of the imaging image, the multi-dimensional information including at least global luminance information, dynamic range information, and contrast information; determining exposure parameters based on the multi-dimensional information; and performing exposure imaging according to the exposure parameters to obtain a target image.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application is based on and claims priority from an application having application number CN202210224340.7 and filing date March 9, 2022, the entire disclosure of which is incorporated herein by reference.

[0002] (Technical field) The present disclosure relates to the field of image processing technology, and in particular to an exposure imaging method, apparatus, device, storage medium, and program product. [Background technology]

[0003] With the rapid development of Internet technology and terminal devices, the image signal processors of most imaging terminal devices have powerful image information processing functions, making the captured digital images as close as possible to the real scene effects seen by the human eye.

[0004] The automatic exposure algorithms in the image signal processing systems of conventional terminal devices are all statistical methods based on the raw domain, and are heavily influenced by the camera sensor of the terminal device. Each time a new camera sensor is replaced, it is necessary to recalibrate the image signal processing system and determine the exposure parameters. Summary of the Invention

[0005] According to a first aspect of the present disclosure, Obtaining an imaging image; determining multi-dimensional information of an imaging image, the multi-dimensional information including at least global luminance information, dynamic range information, and contrast information; determining exposure parameters based on the multi-dimensional information; performing exposure imaging according to the exposure parameters to obtain a target image.

[0006] According to a second aspect of the present disclosure, an image acquisition module for acquiring an imaging image; a multi-dimensional information determination module for determining multi-dimensional information of an imaging image, the multi-dimensional information including at least global luminance information, dynamic range information, and contrast information; an exposure parameter determination module for determining exposure parameters based on the multi-dimensional information; and an exposure imaging module for performing exposure imaging according to the exposure parameters to obtain a target image.

[0007] According to a third aspect of the present disclosure, one or more processors; a storage device for storing one or more programs; There is provided an electronic device that, when one or more programs are executed by one or more processors, causes the one or more processors to realize the exposure imaging method according to any one of the first aspects.

[0008] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium storing a computer program that, when executed by a processor, realizes the exposure imaging method described in any one of the first aspects above.

[0009] According to a fifth aspect of the present disclosure, an embodiment of the present disclosure provides a computer program product including a computer program or instructions that, when executed by a processor, causes the exposure imaging method according to any one of the first aspects to be realized.

[0010] According to a sixth aspect of the present disclosure, there is provided a computer program comprising instructions which, when executed by a processor, cause the computer program to implement the exposure imaging method according to any one of the first aspects. [Brief description of the drawings]

[0011] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which the same or similar elements are designated by the same or similar reference numerals in all the accompanying drawings. It should be understood that the accompanying drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0012] [Figure 1] 1 is a flowchart of an exposure imaging method in accordance with some embodiments of the present disclosure. [Diagram 2] 1 is a schematic diagram of an exposure imaging apparatus according to some embodiments of the present disclosure. [Diagram 3] FIG. 1 is a schematic diagram illustrating the configuration of an electronic device according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The following will describe the embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although several embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be realized in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the accompanying drawings and embodiments of the present disclosure are merely illustrative and do not limit the scope of protection of the present disclosure.

[0014] It should be understood that the steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. Additionally, method embodiments may include additional steps and / or omit performance of steps shown. The scope of the present disclosure is not limited in this respect.

[0015] As used herein, the term "including" and variations thereof are open-ended, i.e., meaning "including, but not limited to." The term "based on" means "based at least in part on." The term "in one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one other embodiment," and the term "some embodiments" means "at least some embodiments." Relevant definitions of other terms are provided below.

[0016] It should be noted that concepts such as "first" and "second" described in this disclosure are merely intended to distinguish different devices, modules, or units, and are not intended to limit the order or interdependence of functions performed by these devices, modules, or units.

[0017] In addition, the modifications of "one" and "multiple" described in the present disclosure are illustrative and not limiting. Unless otherwise specified, it is obvious to a person skilled in the art that it should be understood as "one or multiple".

[0018] The names of messages or information exchanged between devices in the embodiments of the present disclosure are merely descriptive and do not limit the scope of these messages or information.

[0019] With the rapid development of Internet technology and terminal devices, the image signal processors of most imaging terminal devices have powerful image information processing functions, making the captured digital images as close as possible to the real scene effects seen by the human eye.

[0020] The auto-exposure algorithms in the image signal processing systems of conventional terminal devices are all statistical methods based on the raw domain, and are heavily affected by the camera sensor of the terminal device. Every time a new camera sensor is replaced, the image signal processing system needs to be re-calibrated, resulting in a large amount of wasted effort, cost, and other resources.

[0021] In order to solve the above problems, the present disclosure relates to an exposure imaging method, an apparatus, a device, a storage medium and a program product, which obtains multi-dimensional information by image transformation, and obtains the mapping relationship between the multi-dimensional information of the image and the exposure parameters by training a model, and constantly adjusts the exposure estimation value to be close to a reasonable range, and finally determines the exposure parameters. The problem in the prior art that the image signal processing system needs to be recalibrated every time a new camera sensor is replaced is solved, and the labor and cost are saved, and the imaging effect of the image in the whole scene is improved.

[0022] Hereinafter, an exposure imaging method according to an embodiment of the present application will be described in detail with reference to the accompanying drawings.

[0023] 1 is a flowchart of an exposure imaging method according to an embodiment of the present disclosure. This embodiment is applicable to a case where a terminal device equipped with a camera sensor captures an image, and the method may be performed by an exposure imaging device, which may be realized in the form of software and / or hardware, and which may be disposed in an electronic device.

[0024] For example, the electronic device may be a mobile terminal, a fixed terminal, or a portable terminal, such as a mobile phone, a site, a unit, a device, a multimedia computer, a multimedia tablet, an Internet node, a communicator, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device, or any combination thereof, including accessories of these devices, external devices, or any combination thereof.

[0025] Also, for example, the electronic device may be a server. The server may be a real server or a cloud server. The server may be a single server or a server cluster.

[0026] As shown in FIG. 1, an exposure imaging method according to some embodiments of the present disclosure mainly includes the following steps S101 to S104.

[0027] In step S101, an imaging image that is an image captured without performing exposure compensation is obtained.

[0028] Specifically, a conventional imaging terminal processes image signals in real time by running various algorithm programs through an image signal processor (ISP). The specific processing flow of the ISP is as follows: After the lens projects an optical signal onto the photosensitive area of ​​the sensor, the sensor sends the original image in the Bayer format, which is the original picture format inside the camera, to the ISP through photoelectric conversion. The ISP outputs an image in the RGB (Red Green Blue) spatial domain to the back-end picture / video acquisition unit through algorithm processing. Here, the firmware is run to logically control the ISP, which provides appropriate control over the lens and sensor, and further completes functions such as auto iris, auto exposure, and auto white balance. In other words, image sharpening, noise reduction, color optimization, etc. are all completed by the ISP, and the ISP also plays an important role in providing hybrid focus calculations such as phase, laser, and contrast, as well as multi-camera support, making it an important part of imaging terminals' imaging and shooting. Furthermore, an image in the RGB spatial domain is derived from the RGB color model and is a model image based on additive color mixing, in which various colors are generated by additively mixing the three primary colors of red, green, and blue in different proportions.

[0029] The imaging image refers to an image captured without performing exposure compensation, that is, an image captured by an imaging terminal without employing the exposure imaging method according to this embodiment.

[0030] In some embodiments, when the imaging terminal is in a video recording mode, the imaging image may be a current video frame captured by a camera of the imaging terminal, and the exposure imaging method according to the embodiment of the present disclosure is performed on the captured current video frame to determine exposure parameters, and then the exposure parameters are used to capture a subsequent video frame.

[0031] However, while capturing subsequent video frames using the determined exposure parameters, the exposure capture method according to the embodiments of the present disclosure may be constantly used to constantly adjust the exposure parameters based on the captured images.

[0032] In some other embodiments, when the imaging terminal is in an imaging mode, the imaging image may be an image collected by a camera of the imaging terminal, where the imaging image collected by the camera may be acquired in real time when the imaging terminal detects that the camera is turned on. Further, the exposure imaging method according to the embodiment of the present disclosure is performed on the acquired imaging image, exposure parameters are determined, and when an imaging command is received, the determined exposure parameters are used to capture the image to obtain a target image.

[0033] In step S102, multi-dimensional information of the imaging image is determined, where the multi-dimensional information includes at least global brightness information, dynamic range information, and contrast information.

[0034] In some embodiments, the multi-dimensional information includes at least global luminance information, dynamic range information and contrast information, and the multi-dimensional information is not independent of each other, such that changing one parameter may lead to changes in other parameters of the image.

[0035] Dynamic range can be thought of as the range between the brightest and darkest pixel values ​​in an image.

[0036] Image brightness can be understood as the degree of lightness or darkness of an image, where brightness is an accumulation of different optical quantities, and in monochrome images, the range of the image function is finite, with the highest value corresponding to white and the lowest value corresponding to black. Global brightness information can be understood as the average brightness value of the entire imaging image.

[0037] The contrast information of an imaging image is related to the brightness of the imaging image. Contrast information refers to the magnitude of the brightness contrast of an image. The larger the difference range, the greater the contrast information. The smaller the difference range, the smaller the contrast information. The impact of image contrast on visual effect is very important. Generally speaking, the greater the contrast, the more vivid and vivid the color of the image is, and the smaller the contrast, the duller the image is.

[0038] For example, the image dynamic range refers to the interval range between the maximum and minimum pixel tone values ​​in an imaging image, and the dynamic range information can be regarded as a parameter representing the size of the image dynamic range. The larger the dynamic range information, the larger the dynamic range of the image, i.e., the imaging image is an image with a high dynamic range. The smaller the dynamic range information, the smaller the dynamic range of the image, i.e., the imaging image is an image with a low dynamic range.

[0039] In addition, the multi-dimensional information is information related to the luminance value of the imaging image. As can be seen from the above embodiment, the image captured by the camera is an image in the RGB spatial domain. Therefore, the imaging image needs to be transformed.

[0040] In some embodiments, the imaging image is transformed into an image in a YUV spatial domain, a value of the Y channel is obtained, a luminance value of the imaging image is determined, and multi-dimensional information of the image in the YUV spatial domain is calculated.

[0041] In some embodiments, determining the multi-dimensional information of the imaging image comprises converting the imaging image to an image in a Lab color space and calculating the multi-dimensional information of the image in the Lab color space.

[0042] Specifically, the Lab color space can be written as CIELAB. The Lab color space is a perceptually unified space, in which a given numerical change corresponds to a similar perceived color change. The Lab color space represents colors by three values, where "L" represents the perceived luminance, and "a" and "b*" represent the four distinct colors of human vision, namely red, green, blue, and yellow. The three basic coordinates L represent the luminance of the color, where L=0 produces black, L=100 produces white, a negative value of a represents green, a positive value represents magenta, and a negative value of b represents blue, and a positive value represents yellow.

[0043] In the embodiment of the present disclosure, the multi-dimensional information such as the global luminance information, the dynamic range information, and the contrast information are all determined based on the luminance value L of the Lab color space model, and are not related to the "a*" and "b*" color values, because the human eye is much more sensitive to changes in luminance than it is to changes in color.

[0044] The exposure parameters obtained by determining the multi-dimensional information based on the luminance value L of the Lab color space model are more accurate, so that the images captured later are more perfect.

[0045] In some embodiments, determining multi-dimensional information of an imaging image includes obtaining brightness values ​​of a plurality of pixel points in the imaging image and determining an average value of the brightness values ​​of the plurality of pixel points as global brightness information of the imaging image.

[0046] In some embodiments, the imaging image may be converted into an image in the YUV spatial domain and the Y channel value may be obtained as the luminance value of the imaging image, or the imaging image may be converted into an image in the Lab color space and the L channel value may be obtained as the luminance value of the imaging image. Note that in the following embodiments, a case where the luminance value of the imaging image is the L channel value in the Lab color space will be described as an example.

[0047] In some embodiments, the luminance value L of each pixel point in an image in Lab color space may be obtained, and the average value of the luminance values ​​L of all pixel points may be calculated as global luminance information of the imaging image.

[0048] In some embodiments, determining multi-dimensional information of the imaging image includes determining a first number of pixel points of the imaging image having brightness values ​​greater than a first brightness threshold, determining a second number of pixel points of the imaging image having brightness values ​​less than a second brightness threshold, the first brightness threshold being greater than the second brightness threshold, and determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points and a total number of pixel points in the imaging image.

[0049] The first brightness threshold may be a threshold value that indicates that the imaging image is overexposed. If the brightness value of a pixel point in the imaging image is greater than the first brightness threshold, it indicates that the pixel point is overexposed and has a high brightness. In some embodiments, each pixel point in the imaging image is compared with the first brightness threshold in sequence, and the number of pixel points whose brightness value is greater than the first brightness threshold is counted to obtain the number of first pixel points.

[0050] The second brightness threshold may be a threshold representing that the imaging image is underexposed. If the brightness value of a pixel point in the imaging image is smaller than the second brightness threshold, it indicates that the pixel point is underexposed and has a low brightness. In some embodiments, each pixel point in the imaging image is compared with the second brightness threshold in sequence, and the number of pixel points whose brightness value is smaller than the second brightness threshold is counted to obtain the number of the second pixel points.

[0051] In some embodiments, a method for determining a first number of pixel points and a second number of pixel points is provided by determining a histogram corresponding to an imaging image, accumulating a number of pixel points in the histogram whose luminance value is greater than a first luminance threshold to determine the first number of pixel points, and accumulating a number of pixel points in the histogram whose luminance value is less than a second luminance threshold to determine the second number of pixel points.

[0052] Specifically, a histogram is an accurate graphical representation of a numerical data distribution, and in the embodiment of the present disclosure, the histogram may be a function of image luminance values, and is a statistic of the distribution of image luminance values. Furthermore, the luminance histogram counts the occurrences of all pixel points in an image according to the magnitude of their luminance values. The luminance histogram represents the number of pixel points with a certain luminance value in an image, and can reflect the frequency of occurrence of a certain luminance value in an image. The luminance histogram is commonly used in threshold selection in image processing.

[0053] Specifically, the luminance values ​​of all pixel points in the imaging image are plotted in a luminance histogram. The distribution of luminance in the imaging image can be intuitively understood from the histogram. The number of pixel points in the histogram whose luminance value is greater than a first luminance threshold is accumulated to obtain the first number of pixel points. The number of pixel points in the histogram whose luminance value is less than a second luminance threshold is accumulated to obtain the second number of pixel points.

[0054] In some embodiments, determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points and the total number of pixel points in the imaging image includes calculating a first ratio between the first number of pixel points and the total number of pixel points, calculating a second ratio between the second number of pixel points and the total number of pixel points, and determining a sum of the first ratio and the second ratio as the dynamic range information of the imaging image.

[0055] In some embodiments, a first ratio of the number of overexposed pixel points to the total number of imaging image pixel points is calculated, and a second ratio of the number of underexposed pixel points to the total number of imaging image pixel points is calculated, and a value of the sum of the two ratios represents dynamic range information of the entire imaging image. A larger value of the sum of the two ratios indicates a larger dynamic range of the imaging image. A smaller value of the sum of the two ratios indicates a smaller dynamic range of the imaging image.

[0056] In this embodiment, the dynamic range of an image is represented by the sum of the ratio of overexposed pixel points and the ratio of underexposed pixel points, and a method for quantizing the dynamic range is provided to facilitate subsequent processing.

[0057] In some embodiments, determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points and the total number of pixel points in the imaging image includes calculating a sum value of the first number of pixel points and the second number of pixel points, and determining a ratio of the sum value to the total number of pixel points as the dynamic range information of the imaging image.

[0058] In some embodiments, determining multi-dimensional information of an imaging image includes calculating a standard deviation of brightness values ​​of a plurality of pixel points in the imaging image and an average value of a plurality of pixel points, respectively, and determining a ratio of the standard deviation to the average value as contrast information.

[0059] Specifically, the mean value and standard deviation are the two most important indexes that describe the concentration tendency and the degree of discreteness of data. Here, the mean value refers to the average number, that is, the sum of all data in a set of data is divided by the number of data in this set. The definition of standard deviation is the square root of the arithmetic mean of the variance, that is, subtract the sum of the squares of the mean values ​​from all data in a set of data, divide the result obtained by the number of sets, and then find the square root of the obtained value, and the final value obtained is the standard deviation of this set of data.

[0060] In the embodiment of the present disclosure, the brightness values ​​of all pixel points of an imaging image are taken as a set of data, and the standard deviation and average value of the data are calculated, and finally, the coefficient of variation, i.e., the standard deviation divided by the average value, is taken as the contrast information of the imaging image. The coefficient of variation is a statistical quantity that measures the degree of variation of each value in the data, and does not need to refer to the average value of the data, and is a dimensionless quantity. When comparing the degree of variation of multiple sets of data, the smaller the coefficient of variation, the smaller the degree of variation.

[0061] In this embodiment, the ratio of the standard deviation to the average value of the luminance values ​​of all pixel points is taken as the contrast information, and a method for quantizing the contrast information is provided to facilitate the calculation of subsequent exposure parameters.

[0062] In S103, exposure parameters are determined based on the multi-dimensional information.

[0063] In imaging, the exposure parameter (Exposure Value, EV) represents all camera aperture and shutter combinations that can give the same exposure, and is intended to simplify the procedure of selecting from equivalent imaging parameters. The exposure parameter is a logarithmic scale system with the base 2. In theory, all imaging parameters with the same exposure value give the same exposure, but they do not necessarily produce exactly the same photographs. This is because the shutter speed determines the degree of motion blur, and the aperture determines the depth of field.

[0064] In addition, in the embodiment of the present disclosure, the multi-dimensional information of the imaging image is adopted to determine the exposure parameters, that is, the global brightness information, the dynamic range information, the contrast information, etc. are adopted to determine the exposure parameters. For example, in imaging, the multi-dimensional information of the current frame image is calculated to adjust the exposure parameters, so that the subsequent video frame has a better imaging effect.

[0065] In some embodiments, a scene type of the imaging image is determined, a mapping relationship between multi-dimensional information and exposure parameters pre-stored for the scene type is obtained, and exposure parameters are determined based on the multi-dimensional information and the mapping relationship.

[0066] The scene type of an imaging image refers to whether the imaging image is day or night. Note that day can be regarded as a scene with high scene brightness of the imaging image, and a lighted room at night can also be regarded as a day scene. A dark room during the day can also be regarded as a night scene. That is, whether the scene type in this embodiment is day or night is determined based on the brightness of the scene, and is not determined based on the normal time.

[0067] In some embodiments, a pre-trained scene detection model is employed to determine the scene type of an imaging image.

[0068] Specifically, the imaging image is input to a pre-trained scene detection model, and the scene detection model outputs a scene type, i.e., whether the scene of the imaging image is day or night. Here, the scene detection model adopts a network structure called mobilenet, and obtains a dataset through manual labeling in the previous stage, and trains to obtain a two-classification result. Note that the scene in the embodiment of the present disclosure only has two cases, namely, day scene and night scene.

[0069] The moilenet network model maintains a balance between the fast-growing fields of computer vision and deep learning and the limitations of the mobile environment as much as possible. Since the imaging terminal in the embodiment of the present disclosure is often a portable device that can be carried around, the moilenet network model can be adopted to achieve a balance between the fields of computer vision and deep learning and the limitations of the mobile environment.

[0070] Specifically, for different types of scenes, a mapping relationship between multi-dimensional information and exposure parameters is set, that is, a mapping relationship between a set of multi-dimensional information and exposure parameters is set for a daytime scene, and a mapping relationship between a set of multi-dimensional information and exposure parameters is set for a nighttime scene. The above mapping relationship may be determined based on experience or obtained by model training. In this embodiment, the method of determining the above mapping relationship is not limited.

[0071] Furthermore, after determining the mapping relationship, the exposure parameters are determined based on the multi-dimensional information and the mapping relationship.

[0072] In some embodiments, after determining exposure parameters based on the multi-dimensional information and the mapping relationship, an exposure range for the scene type is obtained, and if the exposure parameters are not in the exposure range, the exposure parameters are adjusted so that the adjusted exposure parameters are in the exposure range.

[0073] Specifically, during the training of the scene detection model, there is a large difference between the exposure parameters of the day scene and the night scene, and during the training of the scene detection model, the exposure ranges of the two scenes are obtained, and only the exposure parameters within the exposure range of the scene can be used as the final exposure parameters for exposure imaging of the subsequent images. If the exposure parameters are not within the exposure range of the scene, it indicates that the exposure parameters are inappropriate as the final exposure parameters.

[0074] In the embodiment of the present disclosure, an exposure compensation value is obtained from the difference between the exposure parameter and the exposure range, and the exposure parameter is adjusted through the dual roles of the exposure parameter and the exposure compensation value to gradually make the exposure parameter approach the exposure range of the scene.

[0075] In step S104, exposure imaging is performed according to the exposure parameters to obtain a target image.

[0076] In some embodiments, when the imaging terminal is in a video recording mode, after determining the exposure parameters, it performs exposure according to the exposure parameters to capture subsequent video frames.

[0077] In some other embodiments, when the imaging terminal is in an imaging mode, after determining exposure parameters, upon receiving an imaging command, exposure imaging is performed according to the exposure parameters to obtain a target image.

[0078] The exposure imaging method according to the present disclosure includes: acquiring an imaging image; determining multi-dimensional information of the imaging image, the multi-dimensional information including at least global luminance information, dynamic range information, and contrast information; determining exposure parameters based on the multi-dimensional information; and performing exposure imaging according to the exposure parameters to obtain a target image. The embodiment of the present disclosure acquires multi-dimensional information in an image and calculates exposure parameters based on the multi-dimensional information, thereby solving the problem in the prior art that the image signal processing system needs to be recalibrated every time a new camera sensor is replaced, thereby saving resources such as labor and cost, and improving the imaging effect of images in all scenes.

[0079] 2 is a schematic diagram of an exposure imaging device according to some embodiments of the present disclosure. This embodiment is applicable to adjusting exposure parameters of an equipment, and the exposure imaging device may be realized in the form of software and / or hardware, and the configuration management device may be arranged in an electronic device.

[0080] As shown in FIG. 2 , the exposure imaging apparatus 20 according to the embodiment of the present disclosure mainly includes an image acquisition module 21, a multi-dimensional information determining module 22, an exposure parameter determining module 23 and an exposure imaging module 24.

[0081] The image acquisition module 21 is for acquiring an imaging image, which is an image captured without performing exposure compensation. The multidimensional information determination module 22 is for determining multidimensional information of the imaging image, where the multidimensional information includes at least global luminance information, dynamic range information, and contrast information. The exposure imaging module 23 is for determining exposure parameters based on the multidimensional information. The exposure imaging module 24 is for performing exposure imaging according to the exposure parameters to obtain a target image.

[0082] An embodiment of the present disclosure provides an exposure imaging device for performing the following flow: Obtain an imaging image, which is an image captured without performing exposure compensation; Determine multidimensional information of the imaging image, where the multidimensional information includes at least global luminance information, dynamic range information, and contrast information; and Determine exposure parameters based on the multidimensional information. The embodiment of the present disclosure obtains the multidimensional information through image transformation, and trains a model to obtain a mapping relationship between the multidimensional information of the image and the exposure parameters, and constantly adjusts the exposure estimation value to be close to a reasonable interval, and finally determines the exposure parameters of the image, thereby solving the problems in the prior art, such as the need to recalibrate the image signal processing system every time a new camera sensor is replaced, saving resources such as labor and cost, and improving the final imaging effect in all scenes.

[0083] In some embodiments, the multidimensional information determination module 22 comprises: The imaging device includes an image conversion unit for converting the imaging image into an image in a Lab color space, and a multidimensional information calculation unit for calculating multidimensional information of the image in the Lab color space.

[0084] In some embodiments, the multidimensional information determination module 22 comprises: The imaging device includes a brightness value acquisition unit for acquiring brightness values ​​of a plurality of pixel points in the imaging image, and a global brightness information determination unit for determining an average value of the brightness values ​​of the plurality of pixel points as global brightness information of the imaging image.

[0085] In some embodiments, the multidimensional information determination module 22 includes a first pixel point number determination unit for determining a first number of pixel points of the imaging image whose brightness values ​​are greater than a first brightness threshold, a second pixel point number determination unit for determining a second number of pixel points of the imaging image whose brightness values ​​are less than a second brightness threshold, the first brightness threshold being greater than the second brightness threshold, and a brightness range information determination unit for determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points and the total number of pixel points in the imaging image.

[0086] In some embodiments, the brightness range information determination unit is specifically used for calculating a first ratio between the number of the first pixel points and the total number of pixel points, calculating a second ratio between the number of the second pixel points and the total number of pixel points, and determining a sum of the first ratio and the second ratio as dynamic range information of the imaging image.

[0087] In some embodiments, the multidimensional information determination module 22 includes a standard deviation / average value calculation unit for calculating the standard deviation of brightness values ​​of multiple pixel points in the imaging image and the average value of multiple pixel points, respectively, and a contrast information determination unit that determines the ratio of the standard deviation to the average value as contrast information.

[0088] In some embodiments, the exposure parameter determination module 23 includes: The imaging device includes a scene type determination unit for determining a scene type of the imaging image, a mapping relationship determination unit for acquiring a mapping relationship between multidimensional information pre-stored in advance for the scene type and exposure parameters, and an exposure parameter determination unit for determining exposure parameters based on the multidimensional information and the mapping relationship.

[0089] In some embodiments, the exposure parameter determination module 23 further includes an exposure range acquisition unit for acquiring an exposure range for the scene type after determining the exposure parameters based on the multidimensional information and the mapping relationship, and an exposure parameter adjustment unit for adjusting the exposure parameters if the exposure parameters are not in the exposure range, so that the adjusted exposure parameters are in the exposure range.

[0090] The exposure imaging apparatus according to the embodiments of the present disclosure can perform the steps performed in the exposure imaging method according to the method embodiments of the present disclosure, and the performing steps and beneficial effects provided therein will not be further described here.

[0091] FIG. 3 is a schematic diagram of an electronic device in an embodiment of the present disclosure. The following specifically refers to FIG. 3, which shows a schematic diagram of an electronic device 300 suitable for implementing an embodiment of the present disclosure. The electronic device 300 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and wearable terminal devices, and fixed terminals such as digital TVs, desktop computers, and smart home devices. The terminal devices shown in FIG. 3 are merely examples and do not impose any limitations on the functions and scope of use of the embodiment of the present disclosure.

[0092] As shown in Fig. 3, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphic processor, etc.) 301, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303 to realize the picture rendering method according to the embodiment of the present disclosure. The RAM 303 further stores various programs and data required for the operation of the terminal device 300. The processing device 301, the ROM 302 and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0093] Typically, input devices 306 including, for example, a touch screen, touch panel, keyboard, mouse, camera, microphone, accelerometer, gyro, etc., output devices 307 including, for example, a liquid crystal display (LCD), speaker, oscillator, etc., storage devices 308 including, for example, a magnetic tape, hard disk, etc., and communication devices 309 may be connected to the I / O interface 305. The communication devices 309 enable the terminal device 300 to communicate wirelessly or via wires with other devices to exchange data. It should be understood that while the terminal device 300 is shown in FIG. 3 with various devices, it is not intended to require the implementation or inclusion of all of the devices shown. More or less devices may alternatively be implemented or included.

[0094] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowcharts may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program, which is mounted on a non-transitory computer-readable medium and includes a program code for performing the method shown in the flowcharts, thereby implementing the page jump method. In such an embodiment, the computer program may be downloaded and installed from a network by the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, it performs the above-mentioned functions defined in the method of the embodiment of the present disclosure.

[0095] In addition, the computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. The computer-readable storage medium may include, but is not limited to, an electrical connection having one or more conductors, a portable computer 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 compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used in or in combination with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or a data signal propagated in part in a carrier wave, with computer-readable program code therein. The data signal thus propagated may take many forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may transmit, propagate, or transmit a program for use in or in connection with an instruction execution system, apparatus, or device. The program code contained in the computer-readable medium may be transmitted over any suitable medium, including, but not limited to, electrical wires, optical cables, RF (radio frequency), or the like, or any suitable combination thereof.

[0096] In some embodiments, clients and servers may communicate using any now known or later developed network protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the internet), and an end-to-end network (e.g., an ad hoc end-to-end network), as well as any now known or later developed network.

[0097] The computer-readable medium may be included in the electronic device, or may be a separate entity not mounted on the electronic device.

[0098] The computer-readable medium is equipped with one or more programs, which, when executed by the terminal device, cause the terminal device to obtain an imaging image, which is an image captured without exposure compensation; determine multidimensional information of the imaging image, where the multidimensional information includes at least global brightness information, dynamic range information, and contrast information; determine exposure parameters based on the multidimensional information; and perform exposure imaging according to the exposure parameters to obtain a target image.

[0099] Optionally, when the one or more programs are executed by the terminal device, the terminal device may execute other steps described in the above embodiments.

[0100] Computer program code for carrying out the operations of the present disclosure can be written using one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and general procedural programming languages ​​such as "C" or similar programming languages, but are not limited to these. The program code can run entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer by any network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate possible system architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, program segment, or part of code, which includes one or more executable instructions for implementing a specified logical function. It should be noted that in some permutation implementations, the functions depicted in the blocks may be implemented in a different order than that depicted in the accompanying drawings. For example, two consecutively depicted blocks may be executed substantially simultaneously, or, depending on the functionality, they may be executed in the reverse order. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated system based on hardware that executes the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0102] The units according to the embodiments of the present disclosure may be realized by software or hardware, and the names of the units may not limit the units themselves.

[0103] The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, exemplary hardware logic components that may be used include, but are not limited to, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), etc.

[0104] In the present disclosure, a machine-readable medium may be a tangible medium, and may contain or store a program used by or in combination with an instruction execution system, device, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the above. More specific examples of machine-readable storage media include one or more wire-based electrical connections, portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), optical fibers, compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0105] According to some embodiments of the present disclosure, the present disclosure provides an exposure imaging method including: obtaining an imaging image, which is an image captured without exposure compensation; determining multidimensional information of the imaging image, the multidimensional information including at least global brightness information, dynamic range information, and contrast information; determining exposure parameters based on the multidimensional information; and performing exposure imaging according to the exposure parameters to obtain a target image.

[0106] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multi-dimensional information of the imaging image as described above includes converting the imaging image to an image in Lab color space and calculating the multi-dimensional information of the image in the Lab color space.

[0107] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multidimensional information of the imaging image as described above includes obtaining brightness values ​​of a plurality of pixel points in the imaging image, and determining an average value of the brightness values ​​of the plurality of pixel points as global brightness information of the imaging image.

[0108] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multidimensional information of the imaging image as described above includes determining a number of first pixel points of the imaging image whose brightness values ​​are greater than a first brightness threshold, and determining a number of second pixel points of the imaging image whose brightness values ​​are less than a second brightness threshold, wherein the first brightness threshold is greater than the second brightness threshold, and determining dynamic range information of the imaging image based on the number of the first pixel points, the number of the second pixel points and a total number of pixel points in the imaging image.

[0109] According to some embodiments of the present disclosure, in an exposure imaging method according to the present disclosure, determining multidimensional information of the imaging image based on the first number of pixel points, the second number of pixel points and the number of pixel points in the imaging image includes calculating a first ratio between the first number of pixel points and the total number of pixel points, calculating a second ratio between the second number of pixel points and the total number of pixel points, and determining a sum of the first ratio and the second ratio as dynamic range information of the imaging image.

[0110] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multidimensional information of the imaging image as described above includes calculating a standard deviation of brightness values ​​of a plurality of pixel points in the imaging image and an average value of a plurality of pixel points, respectively, and determining the ratio of the standard deviation to the average value as contrast information.

[0111] According to some embodiments of the present disclosure, in an exposure imaging method according to the present disclosure, determining exposure parameters based on the multidimensional information includes determining a scene type of the imaging image, obtaining a mapping relationship between multidimensional information and exposure parameters pre-stored for the scene type, and determining exposure parameters based on the multidimensional information and the mapping relationship.

[0112] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, after determining exposure parameters based on the multidimensional information and the mapping relationship, an exposure range for the scene type is obtained, and if the exposure parameters are not within the exposure range, the exposure parameters are adjusted so that the adjusted exposure parameters are within the exposure range.

[0113] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multi-dimensional information of the imaging image as described above includes converting the imaging image to an image in a YUV spatial domain, obtaining a value of a Y channel, determining a brightness value of the imaging image, and calculating the multi-dimensional information of the image in the YUV spatial domain.

[0114] According to some embodiments of the present disclosure, in the exposure imaging method of the present disclosure, determining the multidimensional information of the imaging image as described above includes calculating a standard deviation of brightness values ​​of a plurality of pixel points in the imaging image and an average value of a plurality of pixel points, respectively, and determining the ratio of the standard deviation to the average value as the contrast information.

[0115] According to some embodiments of the present disclosure, the present disclosure provides an exposure imaging device including an image acquisition module for acquiring an imaging image, which is an image captured without exposure compensation; a multidimensional information determination module for determining multidimensional information of the imaging image, where the multidimensional information includes at least global brightness information, dynamic range information, and contrast information; an exposure parameter determination module for determining exposure parameters based on the multidimensional information; and an exposure imaging module for performing exposure imaging according to the exposure parameters to obtain a target image.

[0116] In some embodiments, in an exposure imaging apparatus according to the present disclosure, the multidimensional information determination module includes an image conversion unit for converting the imaging image into an image in Lab color space, and a multidimensional information calculation unit for calculating multidimensional information of the image in the Lab color space.

[0117] According to some embodiments of the present disclosure, in an exposure imaging device according to the present disclosure, a multidimensional information determination module includes a brightness value acquisition unit for acquiring brightness values ​​of a plurality of pixel points in the imaging image, and a global brightness information determination unit for setting an average value of the brightness values ​​of the plurality of pixel points as global brightness information of the imaging image.

[0118] According to some embodiments of the present disclosure, in an exposure imaging device according to the present disclosure, the multidimensional information determination module includes a first pixel point number determination unit for determining a first number of pixel points of the imaging image whose brightness values ​​are greater than a first brightness threshold, a second pixel point number determination unit for determining a second number of pixel points of the imaging image whose brightness values ​​are less than a second brightness threshold, wherein the first brightness threshold is greater than the second brightness threshold, and a brightness range information determination unit for determining brightness range information of the imaging image based on the first number of pixel points, the second number of pixel points and the total number of pixel points in the imaging image.

[0119] According to some embodiments of the present disclosure, in an exposure imaging device according to the present disclosure, the brightness range information determination unit is specifically used for calculating a first ratio between the number of the first pixel points and the total number of pixel points, calculating a second ratio between the number of the second pixel points and the total number of pixel points, and determining the sum of the first ratio and the second ratio as the brightness range of the imaging image.

[0120] According to some embodiments of the present disclosure, in an exposure imaging device according to the present disclosure, a multidimensional information determination module includes a standard deviation / average value calculation unit for calculating a standard deviation of brightness values ​​of a plurality of pixel points in the imaging image and an average value of the plurality of pixel points, respectively, and a contrast information determination unit that determines the ratio of the standard deviation and the average value as contrast information.

[0121] According to some embodiments of the present disclosure, in an exposure imaging device according to the present disclosure, an exposure parameter determination module includes a scene type determination unit for determining a scene type of the imaging image, a mapping relationship determination unit for acquiring a mapping relationship between multidimensional information pre-stored in the scene type and exposure parameters, and an exposure parameter determination unit for determining exposure parameters based on the multidimensional information and the mapping relationship.

[0122] According to some embodiments of the present disclosure, in the exposure imaging apparatus of the present disclosure, the exposure parameter determination module further includes an exposure range acquisition unit for acquiring an exposure range for the scene type after determining exposure parameters based on the multidimensional information and the mapping relationship, and an exposure parameter adjustment unit for adjusting the exposure parameters, if the exposure parameters are not within the exposure range, so that the adjusted exposure parameters are within the exposure range.

[0123] According to some embodiments of the present disclosure, the present disclosure provides a method for producing a method for manufacturing a semiconductor device comprising: one or more processors; a memory for storing one or more programs; The present invention provides an electronic device that, when the one or more programs are executed by the one or more processors, causes the one or more processors to realize any one of the exposure imaging methods according to the present disclosure.

[0124] According to some embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the computer to realize any one of the exposure imaging methods according to the present disclosure.

[0125] An embodiment of the present disclosure further provides a computer program product including a computer program or instructions which, when executed by a processor, causes the exposure imaging method of any of the above embodiments to be implemented.

[0126] According to some embodiments of the present disclosure, the present disclosure provides a computer program comprising instructions which, when executed by a processor, cause the computer program to implement the exposure imaging method of any of the above embodiments.

[0127] The above is merely a description of the preferred embodiments and the technical principles applied in the present disclosure. It is obvious to those skilled in the art that the scope of the disclosure of the present application is not limited to the technical solution based on the specific combination of the above technical features, but should also include other technical solutions formed by any combination of the above technical features or features equivalent thereto without departing from the concept of the above disclosure. For example, it also includes technical solutions formed by replacing the above features with technical features having similar functions disclosed in the present disclosure (not limited to those).

[0128] Also, although operations are described in a particular order, this should not be understood as requiring such operations to be performed in the particular order shown, or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although certain specific implementation details are included in the above description, they should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of a single embodiment can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

[0129] Although the present subject matter has been described in language specific to structural features and / or methodological operations, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or operations described above. Rather, the specific features and operations described above are merely example forms of implementing the claims.

Claims

1. Obtaining an imaging image that is an image captured without performing exposure compensation; determining multi-dimensional information of the imaging image, the multi-dimensional information including at least global luminance information, dynamic range information, and contrast information; determining exposure parameters based on the multi-dimensional information; and performing exposure imaging according to the exposure parameters to obtain a target image; Exposure imaging method.

2. Determining multi-dimensional information of the imaging image includes: converting the image into an image in Lab color space; Calculating multi-dimensional information of the image in the Lab color space. The exposure imaging method according to claim 1 .

3. Determining multi-dimensional information of the imaging image includes: Obtaining luminance values ​​of a plurality of pixel points in the image; and determining an average value of the luminance values ​​of the plurality of pixel points as global luminance information of the imaging image. The exposure imaging method according to claim 1 .

4. Determining multi-dimensional information of the imaging image includes: determining a number of first pixel points in the imaging image having a luminance value greater than a first luminance threshold; determining a second number of pixel points in the imaging image having a luminance value less than a second luminance threshold, the first luminance threshold being greater than the second luminance threshold; determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points and a total number of pixel points in the imaging image. The exposure imaging method according to claim 1 .

5. Determining dynamic range information of the imaging image based on the first number of pixel points, the second number of pixel points, and a total number of pixel points in the imaging image includes: calculating a first ratio between the first number of pixel points and the total number of pixel points; calculating a second ratio between the second number of pixel points and the total number of pixel points; determining a sum of the first ratio and the second ratio as dynamic range information of the imaging image; The exposure imaging method according to claim 4 .

6. Determining multi-dimensional information of the imaging image includes: Calculating a standard deviation of brightness values ​​of a plurality of pixel points in the image and an average value of the plurality of pixel points, respectively; and determining a ratio of the standard deviation to the average value as contrast information. The exposure imaging method according to claim 1 .

7. Determining exposure parameters based on the multi-dimensional information includes: determining a scene type of the image; and Obtaining a mapping relationship between multi-dimensional information and exposure parameters pre-stored for the scene type; determining exposure parameters based on the multi-dimensional information and the mapping relationship. The exposure imaging method according to claim 1 .

8. determining exposure parameters based on the multi-dimensional information and the mapping relationship; obtaining an exposure range for said scene type; If the exposure parameters are not within the exposure range, adjusting the exposure parameters so that the adjusted exposure parameters are within the exposure range. The exposure imaging method according to claim 7.

9. Determining multi-dimensional information of the imaging image includes: converting the imaging image into an image in the YUV spatial domain; obtaining a Y channel value, determining a brightness value of the imaging image, and calculating multi-dimensional information of the image in the YUV spatial domain; The exposure imaging method according to claim 1 .

10. Determining multi-dimensional information of the imaging image includes: Calculating a standard deviation of brightness values ​​of a plurality of pixel points in the image and an average value of the plurality of pixel points, respectively; and determining a ratio of the standard deviation to the average value as the contrast information. The exposure imaging method according to claim 1 .

11. an image acquisition module for acquiring an imaging image that is an image captured without performing exposure compensation; a multidimensional information determination module for determining multidimensional information of the imaging image, the multidimensional information including at least global luminance information, dynamic range information, and contrast information; an exposure parameter determination module for determining exposure parameters based on the multi-dimensional information; an exposure imaging module for performing exposure imaging according to the exposure parameters to obtain a target image; Exposure imaging device.

12. one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors are caused to realize the exposure imaging method according to any one of claims 1 to 10. electronic equipment.

13. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the exposure imaging method according to any one of claims 1 to 10 to be realized.

14. A computer program comprising instructions which, when executed by a processor, causes the computer to implement the exposure imaging method according to any one of claims 1 to 10.

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