Image processing method, system, apparatus, device, and medium

Abstract

The present disclosure provides an image processing method, system, device, equipment and medium, wherein the method comprises: in response to the need for quality optimization of a candidate image sequence, determining a current candidate frame image to be optimized and a reference frame image adjacent to the candidate frame image from the candidate image sequence; determining a target quality optimization factor of the candidate frame image based on ambient light information of the reference frame image to obtain a target tone mapping curve of the candidate frame image, wherein the target quality optimization factor is obtained based on the definition information, saturation information and brightness information corresponding to the ambient light information; and performing quality optimization on the candidate frame image based on the target tone mapping curve to obtain an optimized target frame image. The present disclosure reduces the cost of quality optimization, improves the quality optimization effect, improves the smoothness and stability of the quality transition between the reference frame image and the candidate frame image, optimizes the quality optimization effect of the candidate frame image, and optimizes the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to an image processing method, system, apparatus, device, and medium. Background Technology

[0002] With the development of technology, people have higher and higher requirements for video viewing quality. During the video recording process, the environment in which the recording subject is located may have a certain impact on the quality of the recorded video. Among related technologies, the video quality can be optimized by adjusting the display brightness of the hardware device, but the optimization effect is not good, which has a certain impact on the user's viewing experience. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first aspect of this disclosure proposes an image processing method.

[0005] The second aspect of this disclosure proposes an image processing system.

[0006] The third aspect of this disclosure provides an image processing apparatus.

[0007] The fourth aspect of this disclosure provides for a display.

[0008] The fifth aspect of this disclosure proposes an electronic device.

[0009] The sixth aspect of this disclosure proposes a computer-readable storage medium.

[0010] The seventh aspect of this disclosure provides for a chip.

[0011] The first aspect of this disclosure proposes an image processing method, comprising: in response to the need for image quality optimization of a candidate image sequence, determining a current candidate frame image to be optimized and a reference frame image adjacent to the candidate frame image from the candidate image sequence; determining a target image quality optimization factor of the candidate frame image based on ambient light information of the reference frame image to obtain a target tone mapping curve of the candidate frame image, wherein the target image quality optimization factor is obtained based on sharpness information, saturation information and brightness information corresponding to the ambient light information; and performing image quality optimization on the candidate frame image based on the target tone mapping curve to obtain an optimized target frame image.

[0012] A second aspect of this disclosure provides an image processing system, comprising: an ambient light information extraction component and a tone mapping component, wherein the ambient light information extraction component is configured to determine, from a candidate image sequence, a current candidate frame image to be optimized and a reference frame image adjacent to the candidate frame image; and, based on the ambient light information of the reference frame image, determine a target image quality optimization factor for the candidate frame image to obtain a target tone mapping curve for the candidate frame image, wherein the target image quality optimization factor is obtained based on sharpness information, saturation information, and brightness information corresponding to the ambient light information; the tone mapping component is configured to perform image quality optimization on the candidate frame image based on the target tone mapping curve to obtain an optimized target frame image.

[0013] A third aspect of this disclosure provides an image processing apparatus, comprising: a first determining module, configured to determine, in response to a candidate image sequence requiring image quality optimization, a current candidate frame image to be optimized and a reference frame image adjacent to the candidate frame image from the candidate image sequence; a second determining module, configured to determine a target image quality optimization factor for the candidate frame image based on ambient light information of the reference frame image, to obtain a target tone mapping curve for the candidate frame image, wherein the target image quality optimization factor is obtained based on sharpness information, saturation information, and brightness information corresponding to the ambient light information; and an optimization module, configured to perform image quality optimization on the candidate frame image based on the target tone mapping curve to obtain an optimized target frame image.

[0014] The fourth aspect of this disclosure provides a display capable of performing the image processing method as described in the first aspect above.

[0015] This disclosure provides a fifth aspect of an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute instructions to implement the image processing method as described in the first aspect above.

[0016] The sixth aspect of this disclosure provides a computer-readable storage medium that, when executed by a processor of an electronic device, enables the electronic device to perform the image processing method as described in the first aspect above.

[0017] A seventh aspect of this disclosure provides a chip including one or more interface circuits and one or more processors; the interface circuits are configured to receive signals and send the signals to the processors, the signals including computer instructions stored in a memory, which, when executed by the processors, cause the chip to perform the image processing method as described in the first aspect above.

[0018] The image processing method and apparatus proposed in this disclosure optimize the image quality of each candidate frame image in a candidate image sequence. Compared with the image quality adjustment that relies on hardware brightness adjustment in related technologies, this method reduces the cost of image quality optimization and improves the effect of image quality optimization. It determines a reference frame image for image quality optimization of the candidate frame images from the candidate image sequence, and then optimizes the image quality of the candidate frame images based on the ambient light information of the reference frame image. This improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image, optimizes the image quality optimization effect of the candidate frame images, and thus optimizes the user's viewing experience.

[0019] It should be understood that the description herein is not intended to identify key or essential features of the embodiments thereof, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0021] Figure 1 This is a schematic flowchart of an image processing method according to an embodiment of the present disclosure;

[0022] Figure 2 This is a schematic flowchart of an image processing method according to another embodiment of the present disclosure;

[0023] Figure 3 This is a schematic flowchart of an image processing method according to another embodiment of the present disclosure;

[0024] Figure 4 This is a schematic diagram of an image processing system according to an embodiment of the present disclosure;

[0025] Figure 5 This is a schematic diagram of an image processing system according to another embodiment of the present disclosure;

[0026] Figure 6 This is a schematic diagram of the structure of an image processing apparatus according to an embodiment of the present disclosure;

[0027] Figure 7 This is a block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0028] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0029] The following description, with reference to the accompanying drawings, outlines an image processing method, system, apparatus, device, and medium according to embodiments of this disclosure.

[0030] Figure 1 This is a schematic flowchart of an image processing method according to an embodiment of the present disclosure, as shown below. Figure 1 As shown, the method includes:

[0031] S101, in response to the need for image quality optimization of the candidate image sequence, determine the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image from the candidate image sequence.

[0032] In this embodiment of the disclosure, when recording video, the ambient light in the recording environment may have a certain impact on the image quality of the recorded video. In this scenario, the recorded video can be further optimized in terms of image quality. In this scenario, the frames that need to be optimized can be extracted from the video that needs to be optimized, and the sequence of the frames can be determined as the candidate image sequence that needs to be optimized in terms of image quality.

[0033] In this embodiment of the disclosure, the candidate image sequence includes multiple frame images. The image quality of the multiple frame images included in the candidate image sequence can be optimized one by one in sequence based on a set order, and the frame image that needs to be optimized is determined as the candidate frame image in the candidate image sequence.

[0034] In one possible implementation, a reference frame image can be obtained when optimizing the image quality of a candidate frame image. This reference frame image is then used as the reference frame image corresponding to the candidate frame image. Based on the ambient light information of the reference frame image, the image quality of the candidate frame image is optimized, thereby obtaining the target frame image after the image quality of the candidate frame image is optimized.

[0035] Specifically, the previous adjacent frame image of the candidate frame image in the candidate image sequence can be obtained as the reference frame image of the candidate frame image, and the image quality of the candidate frame image can be optimized based on the reference frame image.

[0036] It should be noted that using the preceding adjacent frame image of the candidate frame image as the reference frame image can achieve a smooth transition in image quality and display effect between two consecutive frame images, the candidate frame image and the reference frame image.

[0037] S102, based on the ambient light information of the reference frame image, determine the target image quality optimization factor of the candidate frame image to obtain the target tone mapping curve of the candidate frame image, wherein the target image quality optimization factor is obtained based on the sharpness information, saturation information and brightness information corresponding to the ambient light information.

[0038] In this embodiment of the disclosure, the ambient light information of the environment when the reference frame image is acquired can be determined as the ambient light information of the reference frame image. The ambient light information may include information related to the sharpness dimension, saturation dimension, and brightness dimension of the reference frame image.

[0039] In this scenario, the relevant information of the sharpness dimension corresponding to the reference frame image can be determined as the sharpness information corresponding to the ambient light information, the relevant information of the saturation dimension corresponding to the reference frame image can be determined as the saturation information corresponding to the ambient light information, and the relevant information of the brightness dimension corresponding to the reference frame image can be determined as the brightness information corresponding to the ambient light information. In this scenario, based on the extracted sharpness information, saturation information, and brightness information, the optimization factor used to optimize the image quality of the candidate frame image can be obtained, and this optimization factor can be determined as the target image quality optimization factor of the candidate frame image.

[0040] One possible implementation is to use a tone mapping curve generation algorithm in related technologies to process the target image quality factor, and then obtain the tone mapping curve corresponding to the candidate frame image based on the algorithm processing result, which can be used as the target tone mapping curve when optimizing the image quality of the candidate frame image.

[0041] S103, optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the target frame image.

[0042] In this embodiment of the present disclosure, a target tone mapping curve corresponding to a candidate frame image can be generated based on the ambient light information of a reference frame image. Based on the method of image quality optimization based on tone mapping curve in related technologies, the image quality of the candidate frame image is optimized by the target tone mapping curve, and then the optimized frame image is determined as the target frame image obtained after optimizing the candidate frame image.

[0043] The image processing method proposed in this disclosure uses the previous adjacent frame image of the candidate frame image in the candidate image sequence as the reference frame image, which improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image. Based on the ambient light information of the reference frame image, the target tone mapping curve of the candidate frame image is obtained, which improves the optimization effect of the candidate frame image when performing image quality optimization based on the target tone mapping curve, reduces the possibility of the optimized target frame image being too bright or blurry, improves the adaptability and practicality of the image quality optimization method under different ambient light scenes, and optimizes the user's viewing experience.

[0044] In the above embodiments, the acquisition of the target frame image can be combined with... Figure 2 To understand further, Figure 2 This is a flowchart illustrating an image processing method according to another embodiment of the present disclosure, as shown below. Figure 2 As shown, the method includes:

[0045] S201, in response to the need for image quality optimization in the candidate image sequence, determine the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image from the candidate image sequence.

[0046] In this embodiment of the disclosure, the acquisition of candidate frame images and reference frame images can be understood in conjunction with the relevant content in the above embodiments, and will not be repeated here.

[0047] As one possible implementation, the acquisition of candidate image sequences can also be understood in conjunction with the following:

[0048] In this embodiment of the disclosure, in response to the fact that the ambient light parameters of the image sequence corresponding to the initial image sequence do not fall within the preset ambient light illuminance value range, the initial image sequence is determined as a candidate image sequence that needs to be optimized for image quality. The ambient light illuminance value range is determined based on the dark light ambient light illuminance value and the sunlight ambient light illuminance value.

[0049] In this embodiment of the disclosure, an image sequence can be acquired from a set area based on a preset image acquisition device, and the acquired image sequence can be determined as the initial image sequence. In this scenario, the ambient light information corresponding to the initial image sequence can be obtained, and the ambient light information can be analyzed accordingly.

[0050] As one possible implementation, a preset range of ambient light illuminance values ​​can be obtained. When the ambient light corresponding to any image sequence falls within the range of ambient light illuminance values, it can be determined that the ambient light of the image sequence will not affect the image quality of the image sequence. In other words, when the ambient light illuminance value of any image sequence does not fall within the range of ambient light illuminance values, it can be determined that the ambient light of the image sequence may affect its image quality, and image quality optimization of the image sequence is required.

[0051] It should be noted that the upper limit of the ambient light illuminance value range mentioned above can be determined based on the illuminance value corresponding to sunlight, and the lower limit can be determined based on the illuminance value corresponding to dim light. The illuminance value corresponding to sunlight can be determined as the ambient light illuminance value in sunlight, and the illuminance value corresponding to dim light can be determined as the illuminance value in dim light. The ambient light illuminance value in sunlight can be 10,000 lux or other illuminance values ​​collected in sunlight, and the ambient light illuminance value in dim light can be 10 lux or other illuminance values ​​collected in dim light. No specific limitation is made here.

[0052] In this scenario, ambient light information corresponding to the initial image sequence can be obtained, and the illuminance value corresponding to the ambient light information can be determined. The illuminance value is then compared with the aforementioned ambient light illuminance value range. When the illuminance value falls within the ambient light illuminance value range, it can be determined that the ambient light of the initial image sequence will not affect its image quality.

[0053] Accordingly, when the illuminance value does not fall within the range of ambient light illuminance values, it can be determined that the ambient light of the initial image sequence may have a certain impact on its image quality. In this scenario, the initial image sequence needs to be optimized for image quality. As a possible implementation method, the initial image sequence in this scenario can be identified as a candidate image sequence that needs to be optimized for image quality.

[0054] S202, extract the sharpness factor, saturation factor and brightness factor corresponding to the candidate frame image from the ambient light information.

[0055] In this embodiment of the present disclosure, sharpness information can be extracted based on the ambient light information of the reference frame image to obtain the adjustment factor used when optimizing the image quality of the candidate frame image in the sharpness dimension, which serves as the sharpness factor corresponding to the candidate frame image.

[0056] Additionally, saturation information is extracted from the ambient light information of the reference frame image to obtain the adjustment factor used for saturation dimension image quality optimization of the candidate frame image, which serves as the saturation factor corresponding to the candidate frame image.

[0057] Additionally, brightness information is extracted from the ambient light information of the reference frame image to obtain the adjustment factor used for brightness dimension image quality optimization of the candidate frame image, which serves as the brightness factor corresponding to the candidate frame image.

[0058] As one possible implementation, reference image sharpness information of the reference frame image is extracted from ambient light information to determine the sharpness factor of the candidate frame image.

[0059] Specifically, the average second-order differential pixel residual value in the sharpness information of the reference image can be obtained. Based on the average second-order differential pixel residual value, the sharpness factor of the candidate frame image is determined by looking up the table in the preset sharpness factor register table.

[0060] In this embodiment of the disclosure, the sharpness information extracted from the reference frame image can be determined as the reference image sharpness information corresponding to the reference frame image. The reference image sharpness information can be processed by a preset algorithm, and the adjustment factor used for image quality optimization in the sharpness dimension of the candidate frame image can be obtained based on the result of the algorithm processing, namely the sharpness factor.

[0061] As an example, the sharpness factor of a candidate frame image can be obtained based on the following formula:

[0062]

[0063]

[0064] In the above formula, Represents the sharpness factor. This represents the average second-order differential pixel residual value obtained based on the sharpness information of the reference image. This represents the residual map obtained when acquiring the average second-order derivative pixel residual values. This represents the preset sharpness factor register table. This represents the total number of pixels in the image. The identifier representing the residual plot. This represents a table lookup function.

[0065] Among them, the residual map proposed in the above-mentioned content diagram is used to obtain the average second-order differential pixel residual value. It can be obtained by subtracting the blurred image after Gaussian blurring from the reference frame image, or it can be obtained based on other methods, which are not specifically limited here.

[0066] As one possible implementation, reference image saturation information of the reference frame image is extracted from ambient light information to determine the saturation factor of the candidate frame image.

[0067] Specifically, the average saturation value of each pixel in the saturation information of the reference image is obtained, and the saturation factor of the candidate frame image is determined by looking up the average saturation value in the preset saturation factor register table.

[0068] In this embodiment of the present disclosure, the saturation information of the image extracted from the ambient light information of the reference frame image can be determined as the reference image saturation information of the reference frame image. The reference image saturation information can be processed by a preset algorithm, and the adjustment factor used for image quality optimization in the saturation dimension of the candidate frame image can be obtained based on the result of the algorithm processing, namely the saturation factor.

[0069] As an example, the saturation factor of the candidate frame image can be obtained based on the reference image saturation information of the reference frame image, according to the algorithm shown in the following formula.

[0070]

[0071]

[0072] In the above formula, Represents the saturation factor. This represents the average saturation value of the pixels obtained based on the saturation information of the reference image. This indicates a saturation map extracted from a reference frame image when obtaining the average saturation value of each pixel. This represents the preset saturation factor register table. This represents the total number of pixels in the image. The identifier representing the saturation map. This represents a table lookup function.

[0073] As one possible implementation, a reference brightness histogram of the reference frame image is obtained based on the brightness information in the ambient light information to determine the brightness factor of the candidate frame image.

[0074] Specifically, the weighted average of the number of pixels in the reference brightness histogram is obtained, and the brightness factor of the candidate frame image is determined by looking up the preset brightness factor register table based on the weighted average of the number of pixels.

[0075] In this embodiment of the present disclosure, the brightness information of the image extracted from the ambient light information of the reference frame image can be determined as the reference image brightness information of the reference frame image. The reference image brightness information can be processed by a preset algorithm, and then the adjustment factor used for image quality optimization in the brightness dimension of the candidate frame image can be obtained according to the result of the algorithm processing, namely the brightness factor.

[0076] As one possible implementation, the reference frame image can be processed by an algorithm for obtaining a luminance histogram in related technologies, and then the luminance histogram of the reference frame image can be obtained based on the result of the algorithm processing. As a reference luminance histogram of the reference frame image, as another possible implementation, the weighted average of the number of pixels in the reference luminance histogram can be extracted based on a preset algorithm, and then the luminance factor can be obtained based on the weighted average of the number of pixels.

[0077] The algorithm for obtaining the brightness factor can be understood using the following formula:

[0078]

[0079]

[0080] In the above formula, Indicates the brightness factor. This represents the weighted average of the number of pixels in the brightness histogram. This represents the brightness histogram corresponding to the reference frame image. This represents the preset brightness factor register table. This represents the total number of bins in the brightness histogram. The identifier representing the brightness histogram. This represents a table lookup function.

[0081] S203 determines the target image quality optimization factor based on the sharpness factor, saturation factor, and brightness factor.

[0082] Specifically, the sharpness factor, saturation factor, and brightness factor can be weighted and integrated to obtain integrated candidate image quality optimization factors. The candidate image quality optimization factors are then subjected to amplitude limiting to determine the target image quality optimization factor.

[0083] In this embodiment of the disclosure, the sharpness factor, saturation factor and brightness factor can be integrated based on a set integration method, and the integrated adjustment factor can be determined as the target image quality optimization factor used when optimizing the image quality of the candidate frame image.

[0084] As an example, the sharpness factor, saturation factor, and brightness factor can be integrated based on the following formula:

[0085]

[0086] In the above formula, Indicates candidate image quality optimization factors. Represents the sharpness factor. Represents the saturation factor. This represents the brightness factor.

[0087] As one possible implementation, the candidate image quality factors obtained by the above formula are subjected to a limiting process to obtain the target image quality optimization factor. The limiting process can be understood in conjunction with the following formula:

[0088]

[0089] In the above formula, This represents the target image quality optimization factor. This indicates the upper limit value corresponding to the image quality optimization factor. This represents the lower limit value corresponding to the image quality optimization factor.

[0090] As can be seen from the above formula, when the candidate image quality optimization factor is less than the lower limit value corresponding to the image quality optimization factor shown in the above formula, the lower limit value is determined as the target image quality optimization factor; when the candidate image quality optimization factor is greater than the upper limit value corresponding to the image quality optimization factor shown in the above formula, the upper limit value is determined as the target image quality optimization factor; and when the candidate image quality optimization factor falls within the interval formed by the upper limit value and the lower limit value corresponding to the above formula, the candidate image quality optimization factor is determined as the target image quality optimization factor.

[0091] S204. Based on the reference frame image, an initial tone mapping curve is generated, and the initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain the target tone mapping curve.

[0092] In this embodiment of the disclosure, the reference frame image can be processed by an algorithm based on the tone mapping curve generation algorithm in the related art, and then the corresponding tone mapping curve can be generated according to the result of the algorithm processing, and determined as the initial tone mapping curve.

[0093] The process of generating the initial tone mapping curve can be understood in conjunction with the following:

[0094] As one possible implementation, the reference luminance histogram is divided into intervals based on a preset group interval, and the number of pixels in each interval of the reference luminance histogram is accumulated to obtain an accumulated luminance histogram, and an initial tone mapping curve is generated based on the accumulated luminance histogram.

[0095] In this embodiment of the disclosure, the reference brightness histogram can be divided into intervals based on a preset interval division method and a preset group interval to obtain each image interval (bin) divided by the reference brightness histogram. In this scenario, the number of pixels in each interval of the reference brightness histogram can be counted based on the statistical method of the number of pixels in the interval in the related technology, so as to obtain the statistical value of the number of pixels in each interval.

[0096] As one possible implementation, based on the cumulative histogram acquisition algorithm in related technologies, the algorithm processes the statistical values ​​of the number of pixels in each interval of the brightness histogram by accumulation, and then obtains the cumulative histogram corresponding to the reference frame image based on the result of the algorithm processing.

[0097] As one possible implementation, the cumulative histogram is processed by an algorithm based on the tone mapping curve generation algorithm in the related art, and then the tone mapping curve generated based on the cumulative histogram is obtained according to the result of the algorithm processing. This tone mapping curve can be determined as the initial tone mapping curve.

[0098] As an example, if the luminance histogram of the reference frame image is divided into 256 bins, the algorithm for calculating the number of pixels in each bin can be understood using the following formula:

[0099]

[0100] In the above formula, This represents the statistical value of the number of pixels in the m-th bin, and hist represents the reference brightness histogram.

[0101] As one possible implementation, the initial tone mapping curve is subjected to relevant curve smoothing processing to obtain a smoothed initial tone mapping curve, wherein the algorithm formula is as follows:

[0102]

[0103] In the above formula, This represents the initial tone mapping curve after smoothing. and This represents two adjacent points in the initial tone mapping curve that have not been smoothed, and m represents the bin identifier.

[0104] As one possible implementation, the initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain an adjusted candidate tone mapping curve.

[0105] As an example, an algorithm based on the following formula can be used to adjust the initial tone mapping curve based on the target image quality optimization factor, and determine the adjusted curve as a candidate tone mapping curve. The algorithm formula is as follows:

[0106]

[0107] In the above formula, Represents the candidate tone mapping curve. This represents the target image quality optimization factor. This represents the initial tone mapping curve after smoothing, where, .

[0108] As one possible implementation, the candidate tone mapping curve is subjected to temporal smoothing to obtain the target tone mapping curve.

[0109] As an example, the candidate tone mapping curve can be temporally smoothed based on the algorithm shown in the following formula, and the temporally smoothed curve can be determined as the target tone mapping curve, where the formula is as follows:

[0110]

[0111] In the above formula, This represents the target tone mapping curve. Represents the candidate tone mapping curve. Represents the time-domain smoothing weights, where, The value can be 0.8 or other settings; no specific restrictions are made here.

[0112] S205, optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image.

[0113] As one possible implementation, based on the target tone mapping curve, tone mapping is performed on each pixel of the candidate frame image to obtain the target mapped pixel value of each pixel in the candidate frame image. Based on the target mapped pixel value of each pixel, the image quality of the candidate frame image is optimized to obtain the target frame image.

[0114] In this embodiment of the present disclosure, the pixel value of each pixel in the candidate frame image can be obtained, and the mapped pixel value corresponding to each pixel can be obtained based on the target tone mapping curve, thereby realizing the mapping of each pixel of the candidate frame image by the target tone mapping curve, and determining the mapped pixel value of each pixel as the target mapped pixel value of each pixel.

[0115] As one possible implementation, the candidate frame image is optimized based on the target mapped pixel value of each pixel, and the optimized frame image is determined as the target frame image.

[0116] It should be noted that the candidate frame image is the image in the candidate image sequence that currently needs to be optimized in terms of image quality. In this scenario, once the image quality of each frame in the candidate image sequence has been optimized, the target frame image obtained by optimizing each frame image can be used to optimize the image quality of the candidate image sequence.

[0117] As one possible implementation, a target image sequence after image quality optimization is obtained from the candidate image sequence based on the target image optimized from each candidate frame image.

[0118] In other words, each frame in the candidate image sequence can be updated and overwritten based on each target frame image, thereby optimizing the image quality of the candidate image sequence and determining the optimized image sequence as the target image sequence.

[0119] As an example, such as Figure 3 As shown, it is possible to extract from the reference frame image. Figure 3 The sharpness information, saturation information, and luminance histogram shown are used to obtain the sharpness factor corresponding to the sharpness information, the saturation factor corresponding to the saturation information, and the luminance factor corresponding to the luminance histogram. As one possible implementation method, this is achieved through... Figure 3 The integrated module shown performs weighted integration of sharpness factor, saturation factor and brightness factor to obtain the target image quality optimization factor corresponding to the candidate frame image.

[0120] As one possible implementation method, through Figure 3 The histogram equalization module shown obtains a smoothed initial tone mapping curve based on the luminance histogram and transmits it to... Figure 3 The mapping curve adjustment module shown can be used to adjust the mapping curve. Figure 3The illustrated tone mapping curve adjustment module further adjusts the smoothed initial tone mapping curve based on the target image quality optimization factor to obtain the corresponding candidate tone mapping curve, and then transmits it to... Figure 3 The time-domain smoothing module is shown.

[0121] like Figure 3 As shown, the candidate tone mapping curve is smoothed in the temporal domain using a temporal smoothing module, thereby obtaining the target tone mapping curve used for image quality optimization of the candidate frame image. Then, through... Figure 3 The image mapping module shown implements image quality optimization of candidate frame images to obtain optimized target frame images.

[0122] The image processing method proposed in this disclosure uses the previous adjacent frame image of the candidate frame image in the candidate image sequence as the reference frame image, which improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image. Based on the ambient light information of the reference frame image, the sharpness factor, saturation factor and brightness factor are obtained to obtain the target tone mapping curve of the candidate frame image, which improves the optimization effect of the candidate frame image when performing image quality optimization based on the target tone mapping curve, reduces the possibility of the optimized target frame image being too bright or blurry, and improves the adaptability and practicality of the image quality optimization method under different ambient light scenarios. The candidate tone mapping curve is subjected to temporal domain smoothing to obtain the target tone mapping curve, which reduces the possibility of jitter or flickering between consecutive frame images, improves the continuity and stability of the display transition of consecutive frame images, and optimizes the user's viewing experience.

[0123] This disclosure also proposes an image processing system that can be combined with Figure 4 understand, Figure 4 This is a schematic diagram of an image processing system according to an embodiment of the present disclosure, as shown below. Figure 4 As shown, the image processing system 400 includes an ambient light information extraction component 41 and a tone mapping component 42, wherein,

[0124] Ambient light information extraction component 41 is used to determine the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image from the candidate image sequence.

[0125] Furthermore, based on the ambient light information of the reference frame image, the target image quality optimization factor of the candidate frame image is determined to obtain the target tone mapping curve of the candidate frame image. The target image quality optimization factor is obtained based on the sharpness information, saturation information and brightness information corresponding to the ambient light information.

[0126] The tone mapping component 42 is used to optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image.

[0127] In this embodiment of the disclosure, it can be achieved through... Figure 4 The ambient light information extraction component 41 shown obtains the previous adjacent frame image of the candidate frame image from the candidate image sequence as a reference frame image, and obtains relevant optimization information used for image quality optimization of the candidate frame image based on the sharpness information, saturation information and brightness information included in the ambient light information of the reference frame image. Based on the preset algorithm, it generates the optimization factor used for image quality optimization of the candidate frame image based on the optimization information, which serves as the target image quality optimization factor.

[0128] like Figure 4 As shown, through Figure 4 The ambient light information extraction component 41 shown can also perform algorithmic processing on the target image quality optimization factor based on the tone mapping curve generation algorithm in related technologies, and then use the tone mapping curve obtained based on the algorithm processing result as the target tone mapping curve of the candidate frame image.

[0129] like Figure 4 As shown, the image processing system 400 also includes a tone mapping component 42, which can optimize the image quality of candidate frame images based on the acquired target tone mapping curve, and determine the optimized frame image as the target frame image.

[0130] The image processing system proposed in this disclosure reduces the cost of image quality optimization and improves the effect of image quality optimization compared to related technologies that rely on hardware brightness adjustment. It determines a reference frame image for image quality optimization from a candidate image sequence, and then optimizes the image quality of the candidate frame image based on the ambient light information of the reference frame image. This improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image, optimizes the image quality optimization effect of the candidate frame image, and thus optimizes the user's viewing experience.

[0131] In the above embodiments, the image processing system can also be combined with... Figure 5 To understand further, Figure 5 This is a schematic diagram of an image processing system according to another embodiment of the present disclosure, such as... Figure 5 As shown, the image processing system 500 includes an ambient light information extraction component 51 and a tone mapping component 52. The ambient light information extraction component 51 further includes an image sharpening component 511 and a color management component 512.

[0132] In one possible implementation, the image sharpening component 511 is used to determine the sharpness factor corresponding to the candidate frame image based on the average second-order differential pixel residual value of the reference frame image, and to transmit the sharpness factor to the tone mapping component.

[0133] like Figure 5As shown, the ambient light information extraction assembly 51 (not shown in the figure) also includes Figure 5 The image sharpening component 511 shown can extract sharpness information from the ambient light information of the reference frame image, and based on the sharpness factor acquisition process proposed in the above embodiments, extract the corresponding sharpness factor based on the sharpness information and transmit it to... Figure 5 The tone mapping component 52 is shown.

[0134] As one possible implementation, the ambient light information extraction assembly 51 (not shown) further includes a color management component 512, which determines the saturation factor of the candidate frame image based on the average pixel saturation value of the reference frame image, and transmits the saturation factor to the tone mapping component.

[0135] like Figure 5 As shown, the ambient light information extraction assembly 51 (not shown in the figure) also includes Figure 5 The color management component 512 shown can extract saturation information from the ambient light information of the reference frame image, and based on the saturation factor acquisition process proposed in the above embodiments, extract the corresponding saturation factor based on the saturation information and transmit it to... Figure 5 The tone mapping component 52 is shown.

[0136] In one possible implementation, tone mapping component 52 is also used to: determine the brightness factor of the candidate frame image based on the weighted average of the number of pixels in the reference brightness histogram of the reference frame image.

[0137] like Figure 5 As shown, the brightness histogram of the reference frame image can be obtained through the tone mapping component, and the weighted average of the number of pixels corresponding to the brightness histogram can be calculated. Then, the brightness factor of the candidate frame image can be obtained based on the brightness factor acquisition process proposed in the above embodiment.

[0138] In one possible implementation, tone mapping component 52 can obtain the target image quality optimization factor required for image quality optimization of candidate frame images based on the sharpness factor transmitted from image sharpening component 511, the saturation factor transmitted from color management component 512, and the brightness factor extracted by tone mapping component 52 itself, and adjust the initial tone mapping curve based on the target image quality optimization factor to obtain the adjusted target tone mapping curve.

[0139] As one possible implementation, each pixel in the candidate frame image is mapped based on the target tone mapping curve, thereby optimizing the image quality of the candidate frame image and obtaining the optimized target frame image.

[0140] In one possible implementation, tone mapping component 52 is further configured to determine a sharpness factor based on the average second-order differential pixel residual value of the reference frame image, a saturation factor based on the average pixel saturation value of the reference frame image, and a luminance factor based on the pixel number weighted average value of the reference luminance histogram of the reference frame image, so as to obtain a target tone mapping curve for the candidate frame image.

[0141] In this embodiment of the disclosure, the image processing system 500 may only include the tone mapping component 52, excluding the image sharpening component and color management component in the ambient light information extraction component 51 proposed in the above embodiment. In this scenario, the sharpness factor and saturation factor acquisition algorithms can be deployed in the tone mapping component 52, and the reference frame image can be processed by the tone mapping component 52 with the corresponding algorithms to extract the sharpness factor and luminance factor.

[0142] As one possible implementation, based on the sharpness factor, saturation factor and luminance factor extracted by tone mapping component 52, a target tone mapping factor required for image quality optimization of candidate frame images is obtained, and the initial tone mapping curve is adjusted based on the target tone mapping factor to obtain the adjusted target tone mapping curve.

[0143] The image processing system proposed in this disclosure uses the previous adjacent frame image of the candidate frame image in the candidate image sequence as the reference frame image, which improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image. Based on the ambient light information of the reference frame image, the sharpness factor, saturation factor and brightness factor are obtained to obtain the target tone mapping curve of the candidate frame image, which improves the optimization effect of the candidate frame image when performing image quality optimization based on the target tone mapping curve, reduces the possibility of the optimized target frame image being too bright or blurry, and improves the adaptability and practicality of the image quality optimization method under different ambient light scenes. The candidate tone mapping curve is subjected to temporal domain smoothing to obtain the target tone mapping curve, which reduces the possibility of jitter or flickering between consecutive frame images, improves the continuity and stability of the display transition of consecutive frame images, and optimizes the user's viewing experience.

[0144] Corresponding to the image processing methods proposed in the above embodiments, one embodiment of this disclosure also proposes a test case generation device. Since the test case generation device proposed in this disclosure corresponds to the image processing methods proposed in the above embodiments, the implementation methods of the above image processing methods are also applicable to the test case generation device proposed in this disclosure, and will not be described in detail in the following embodiments.

[0145] Figure 6This is a schematic diagram of the structure of a test case generation device according to an embodiment of the present disclosure, as shown below. Figure 6 As shown, the image processing apparatus 600 includes a first determining module 61, a second determining module 62, and an optimization module 63, wherein:

[0146] The first determining module 61 is used to determine, in response to the need for image quality optimization of the candidate image sequence, the current candidate frame image to be optimized and the reference frame image adjacent to the previous candidate frame image from the candidate image sequence.

[0147] The second determining module 62 is used to determine the target image quality optimization factor of the candidate frame image based on the ambient light information of the reference frame image, so as to obtain the target tone mapping curve of the candidate frame image. The target image quality optimization factor is obtained based on the sharpness information, saturation information and brightness information corresponding to the ambient light information.

[0148] The optimization module 63 is used to optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image.

[0149] In this embodiment of the present disclosure, the second determining module 62 is further configured to: extract the sharpness factor, saturation factor and luminance factor corresponding to the candidate frame image from the ambient light information; determine the target image quality optimization factor based on the sharpness factor, saturation factor and luminance factor; generate an initial tone mapping curve based on the reference frame image, and adjust the initial tone mapping curve based on the target image quality optimization factor to obtain the target tone mapping curve.

[0150] In this embodiment of the present disclosure, the second determining module 62 is further configured to: extract reference image sharpness information of the reference frame image from the ambient light information to determine the sharpness factor of the candidate frame image; extract reference image saturation information of the reference frame image from the ambient light information to determine the saturation factor of the candidate frame image; and obtain a reference luminance histogram of the reference frame image based on the luminance information in the ambient light information to determine the luminance factor of the candidate frame image.

[0151] In this embodiment of the present disclosure, the second determining module 62 is further configured to: obtain the average second-order differential pixel residual value in the sharpness information of the reference image; and, based on the average second-order differential pixel residual value, perform a lookup in a preset sharpness factor register table to determine the sharpness factor of the candidate frame image.

[0152] In this embodiment of the present disclosure, the second determining module 62 is further configured to: obtain the average saturation value of pixels in the saturation information of the reference image; and, based on the average saturation value of pixels, look up the value in a preset saturation factor register table to determine the saturation factor of the candidate frame image.

[0153] In this embodiment of the present disclosure, the second determining module 62 is further configured to: obtain the weighted average value of the number of pixels in the reference brightness histogram; and, based on the weighted average value of the number of pixels, look up the value in a preset brightness factor register table to determine the brightness factor of the candidate frame image.

[0154] In this embodiment of the present disclosure, the second determining module 62 is further configured to: perform weighted integration of the sharpness factor, saturation factor and brightness factor to obtain integrated candidate image quality optimization factors; and perform amplitude limiting processing on the candidate image quality optimization factors to determine the target image quality optimization factor.

[0155] In this embodiment of the present disclosure, the second determining module 62 is further configured to: divide the reference brightness histogram into intervals based on a preset group interval, accumulate the number of pixels in each interval of the reference brightness histogram to obtain an accumulated brightness histogram, and generate an initial tone mapping curve based on the accumulated brightness histogram; adjust the initial tone mapping curve based on the target image quality optimization factor to obtain an adjusted candidate tone mapping curve; and perform temporal smoothing on the candidate tone mapping curve to obtain the target tone mapping curve.

[0156] In this embodiment of the present disclosure, the optimization module 63 is further configured to: perform tone mapping on each pixel of the candidate frame image based on the target tone mapping curve to obtain the target mapped pixel value of each pixel in the candidate frame image; and perform image quality optimization on the candidate frame image based on the target mapped pixel value of each pixel to obtain the target frame image.

[0157] In this embodiment of the present disclosure, the device further includes an identification module, configured to: determine the initial image sequence as a candidate image sequence that needs to be optimized in response to the fact that the ambient light parameters of the image sequence corresponding to the initial image sequence do not fall within a preset range of ambient light illuminance values, wherein the range of ambient light illuminance values ​​is determined based on the dark light ambient light illuminance value and the sunlight ambient light illuminance value.

[0158] In this embodiment of the disclosure, the optimization module 63 is further configured to: obtain a target image sequence after image quality optimization of the candidate image sequence based on the target image processed by each candidate frame image.

[0159] The image processing apparatus disclosed herein uses the previous adjacent frame image of the candidate frame image in the candidate image sequence as the reference frame image, which improves the smoothness and stability of the image quality transition between the optimized reference frame image and the candidate frame image. Based on the ambient light information of the reference frame image, the sharpness factor, saturation factor, and brightness factor are obtained to obtain the target tone mapping curve of the candidate frame image, which improves the optimization effect when the candidate frame image is optimized based on the target tone mapping curve, reduces the possibility of the optimized target frame image being too bright or blurry, and improves the adaptability and practicality of the image quality optimization method under different ambient light scenarios. The candidate tone mapping curve is subjected to temporal domain smoothing to obtain the target tone mapping curve, which reduces the possibility of jitter or flickering between consecutive frame images, improves the continuity and stability of the display transition of consecutive frame images, and optimizes the user's viewing experience.

[0160] To achieve the above embodiments, this disclosure also proposes a display capable of executing the image processing method provided in the above embodiments.

[0161] To achieve the above embodiments, this disclosure also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0162] Figure 7 This is a block diagram of an electronic device 700 according to an embodiment of the present disclosure, as follows: Figure 7 As shown, the electronic device 700 includes a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor 702. When the processor 702 executes program instructions, it implements the image processing method provided in the above embodiments.

[0163] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the image processing method provided in the above embodiments.

[0164] To implement the above embodiments, this disclosure also proposes a computer program product on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the image processing method provided in the above embodiments.

[0165] To implement the above embodiments, this disclosure also proposes a chip, including one or more interface circuits and one or more processors; the interface circuits are used to receive signals and send the signals to the processors, the signals including computer instructions stored in a memory, and when the processor executes the computer instructions, the chip causes the chip to execute the image processing method provided in the above embodiments.

[0166] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0167] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0168] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0169] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and compact disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0170] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, and application-specific integrated circuits having suitable combinational logic gates.

[0171] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0172] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0173] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

[0174] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0175] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An image processing method, characterized in that, The method includes: In response to the need for image quality optimization in the candidate image sequence, the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image are determined from the candidate image sequence. Based on the ambient light information of the reference frame image, the target image quality optimization factor of the candidate frame image is determined, wherein the target image quality optimization factor is obtained based on the sharpness information, saturation information and brightness information corresponding to the ambient light information; Based on the reference frame image, an initial tone mapping curve is generated, and the initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain the target tone mapping curve; The candidate frame image is optimized based on the target tone mapping curve to obtain the optimized target frame image.

2. The method according to claim 1, characterized in that, The step of determining the target image quality optimization factor for the candidate frame image based on the ambient light information of the reference frame image, wherein the target image quality optimization factor is obtained based on the sharpness information, saturation information, and brightness information corresponding to the ambient light information, including: Extract the sharpness factor, saturation factor, and brightness factor corresponding to the candidate frame image from the ambient light information; The target image quality optimization factor is determined based on the sharpness factor, the saturation factor, and the brightness factor.

3. The method according to claim 2, characterized in that, The step of extracting the sharpness factor, saturation factor, and brightness factor corresponding to the candidate frame image from the ambient light information includes: The reference image sharpness information of the reference frame image is extracted from the ambient light information to determine the sharpness factor of the candidate frame image; The reference image saturation information of the reference frame image is extracted from the ambient light information to determine the saturation factor of the candidate frame image; Based on the brightness information in the ambient light information, a reference brightness histogram of the reference frame image is obtained to determine the brightness factor of the candidate frame image.

4. The method according to claim 3, characterized in that, The step of extracting reference image sharpness information from the ambient light information to determine the sharpness factor of the candidate frame image includes: Obtain the average second-order differential pixel residual value from the sharpness information of the reference image; Based on the average second-order differential pixel residual value, the sharpness factor of the candidate frame image is determined by looking up the value in a preset sharpness factor register table.

5. The method according to claim 3, characterized in that, The step of extracting reference image saturation information from the ambient light information to determine the saturation factor of the candidate frame image includes: Obtain the average saturation value of each pixel in the saturation information of the reference image; Based on the average saturation value of the pixels, the saturation factor of the candidate frame image is determined by looking up the value in a preset saturation factor register table.

6. The method according to claim 3, characterized in that, The step of obtaining a reference luminance histogram of the reference frame image based on the luminance information in the ambient light information, and determining the luminance factor of the candidate frame image, includes: Obtain the weighted average of the number of pixels in the reference brightness histogram; Based on the weighted average of the number of pixels, the brightness factor of the candidate frame image is determined by looking up the value in a preset brightness factor register table.

7. The method according to claim 2, characterized in that, The determination of the target image quality optimization factor based on the sharpness factor, the saturation factor, and the brightness factor includes: The sharpness factor, the saturation factor, and the brightness factor are weighted and integrated to obtain integrated candidate image quality optimization factors; The candidate image quality optimization factors are subjected to amplitude limiting processing to determine the target image quality optimization factor.

8. The method according to claim 2, characterized in that, The process of generating an initial tone mapping curve based on the reference frame image and adjusting the initial tone mapping curve based on the target image quality optimization factor to obtain the target tone mapping curve includes: The reference brightness histogram is divided into intervals based on a preset group interval, and the number of pixels in each interval of the reference brightness histogram is accumulated to obtain an accumulated brightness histogram. The initial tone mapping curve is generated based on the accumulated brightness histogram. The initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain the adjusted candidate tone mapping curve; The candidate tone mapping curve is smoothed in the temporal domain to obtain the target tone mapping curve.

9. The method according to claim 1, characterized in that, The step of optimizing the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image includes: Based on the target tone mapping curve, tone mapping is performed on each pixel of the candidate frame image to obtain the target mapped pixel value of each pixel in the candidate frame image. Based on the target mapping pixel values ​​of each pixel, the candidate frame image is optimized to obtain the target frame image.

10. The method according to any one of claims 1-9, characterized in that, The method further includes: In response to the initial image sequence's ambient light parameters not falling within a preset range of ambient light illuminance values, the initial image sequence is determined to be the candidate image sequence requiring image quality optimization. The ambient light illuminance value range is determined based on both dark and sunlight ambient light illuminance values.

11. The method according to any one of claims 1-9, characterized in that, The method further includes: Based on the target frame image after processing each candidate frame image, the target image sequence after image quality optimization of the candidate image sequence is obtained.

12. An image processing system, characterized in that, The image processing system includes an ambient light information extraction component and a tone mapping component, wherein... The ambient light information extraction component is used to determine the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image from the candidate image sequence. Furthermore, based on the ambient light information of the reference frame image, a target image quality optimization factor for the candidate frame image is determined, wherein the target image quality optimization factor is obtained based on the sharpness information, saturation information, and brightness information corresponding to the ambient light information; based on the reference frame image, an initial tone mapping curve is generated, and the initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain a target tone mapping curve; The tone mapping component is used to optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image.

13. The system according to claim 12, characterized in that, The ambient light information extraction component includes an image sharpening component, which is used to determine the sharpness factor corresponding to the candidate frame image based on the average second-order differential pixel residual value of the reference frame image, and transmit the sharpness factor to the tone mapping component.

14. The system according to claim 12, characterized in that, The ambient light information extraction component includes a color management component, which is used to determine the saturation factor of the candidate frame image based on the average pixel saturation value of the reference frame image, and transmit the saturation factor to the tone mapping component.

15. The system according to claim 12, characterized in that, The tone mapping component is also used for: The brightness factor of the candidate frame image is determined by the weighted average of the number of pixels in the reference brightness histogram of the reference frame image.

16. The system according to claim 12, characterized in that, The tone mapping component is further configured to determine a sharpness factor based on the average second-order differential pixel residual value of the reference frame image, a saturation factor based on the average pixel saturation value of the reference frame image, and a luminance factor based on the pixel number weighted average value of the reference luminance histogram of the reference frame image, so as to obtain the target image quality optimization factor of the candidate frame image.

17. An image processing apparatus, characterized in that, The device includes: The first determining module is used to determine, in response to the need for image quality optimization of the candidate image sequence, the current candidate frame image to be optimized and the reference frame image adjacent to the candidate frame image from the candidate image sequence. The second determining module is used to determine the target image quality optimization factor of the candidate frame image based on the ambient light information of the reference frame image, wherein the target image quality optimization factor is obtained based on the sharpness information, saturation information and brightness information corresponding to the ambient light information; and to generate an initial tone mapping curve based on the reference frame image, and to adjust the initial tone mapping curve based on the target image quality optimization factor to obtain the target tone mapping curve. The optimization module is used to optimize the image quality of the candidate frame image based on the target tone mapping curve to obtain the optimized target frame image.

18. The apparatus according to claim 17, characterized in that, The second determining module is further configured to: Extract the sharpness factor, saturation factor, and brightness factor corresponding to the candidate frame image from the ambient light information; The target image quality optimization factor is determined based on the sharpness factor, the saturation factor, and the brightness factor.

19. The apparatus according to claim 18, characterized in that, The second determining module is further configured to: The reference brightness histogram is divided into intervals based on a preset group interval, and the number of pixels in each interval of the reference brightness histogram is accumulated to obtain an accumulated brightness histogram. The initial tone mapping curve is generated based on the accumulated brightness histogram. The initial tone mapping curve is adjusted based on the target image quality optimization factor to obtain the adjusted candidate tone mapping curve; The candidate tone mapping curve is smoothed in the temporal domain to obtain the target tone mapping curve.

20. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute instructions to implement the method as described in any one of claims 1-11.

21. A computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as described in any one of claims 1-11.

22. A chip, characterized in that, The device includes one or more interface circuits and one or more processors; the interface circuits are used to receive signals and send the signals to the processors, the signals including computer instructions stored in a memory, which, when executed by the processor, cause the chip to perform the steps of the method according to any one of claims 1-11.

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