Image processing method, circuit and device, electronic equipment and chip

Abstract

The invention relates to an image processing method, circuit and device, electronic equipment and a chip, and the method comprises the steps: obtaining a to-be-processed original image, and obtaining a standard white point gain, a preference white point gain and a light source parameter; carrying out white balance correction on the original image by adopting a standard white point gain, and carrying out color correction on a white balance image obtained by white balance processing according to the light source parameters to obtain a color correction image; and performing color adaptation transformation processing on the color correction image according to the standard white dot gain and the preference white dot gain to obtain a target image. According to the invention, the standard white point gain is combined with the light source parameters to carry out white balance and color correction, the physical accuracy of image color restoration is guaranteed, the preference white point gain and the standard white point gain are introduced to carry out color adaptation transformation, stylized rendering conforming to the visual preference of human eyes is realized, and the image quality is improved. The interference on color correction based on preference white point gain in the prior art is effectively solved, and the color fidelity, stability and style flexibility of an imaging system are improved.

Description

Technical Field

[0001] This disclosure relates to the field of digital image processing technology, specifically to the field of video processing, and particularly to an image processing method, circuit, device, electronic device, and chip. Background Technology

[0002] With the continuous advancements in imaging hardware and image processing algorithms in mobile terminals (such as smartphones, tablets, and wearable devices), users have increasingly higher demands for the image quality of photos and videos, especially in terms of color fidelity and visual aesthetics. To meet this demand, modern image signal processors (ISPs) typically integrate complex color processing pipelines, including key modules such as white balance correction, demosaicing, color correction matrix (CCM), chromatic adaptation transformation (CAT), and gamma correction, forming a complete color solution. Summary of the Invention

[0003] This disclosure provides an image processing method, circuit, apparatus, electronic device, and chip. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, an image processing method is provided, comprising: acquiring an original image to be processed, and acquiring a standard white point gain, a preferred white point gain, and light source parameters; performing white balance correction on the original image using the standard white point gain, and performing color correction on the white balance image obtained by the white balance processing according to the light source parameters to obtain a color-corrected image; and performing color adaptation transformation processing on the color-corrected image according to the standard white point gain and the preferred white point gain to obtain a target image.

[0004] According to one embodiment of this disclosure, the step of color correction of the white balance image obtained by white balance processing based on the light source parameters to obtain a color-corrected image includes: determining color correction parameters based on the light source parameters; adjusting the original RGB parameters of the white balance image based on the color correction parameters to determine target RGB parameters; and color correction of the white balance image obtained by white balance processing based on the target RGB parameters to obtain the color-corrected image.

[0005] According to one embodiment of this disclosure, determining the color correction parameters based on the light source parameters includes: obtaining a light source-correction parameter mapping relationship; and matching the light source parameters in the light source-correction parameter mapping relationship to determine the successfully matched color correction parameters.

[0006] According to one embodiment of this disclosure, the step of performing color adaptation transformation processing on the color-corrected image based on the standard white point gain and the preferred white point gain to obtain a target image includes: obtaining preset color space conversion parameters; determining color adaptation parameters based on the color correction parameters, the color space conversion parameters, the standard white point gain, and the preferred white point gain; and performing color adaptation transformation processing on the color-corrected image based on the color adaptation parameters to generate the target image.

[0007] According to one embodiment of this disclosure, determining the color adaptation parameters based on the color correction parameters, the color space conversion parameters, the standard white point gain, and the preferred white point gain includes: determining the color correction parameters based on the light source parameters; determining the target color adaptation light source RGB value based on the standard white point gain and the preferred white point gain; determining the target light source ratio based on the target color adaptation light source RGB value; and determining the color adaptation parameters based on the target light source ratio and the color space conversion parameters.

[0008] According to one embodiment of this disclosure, the step of determining the color correction parameters based on the light source parameters and the target color-adaptive light source RGB value based on the standard white point gain and the preferred white point gain includes: determining the gain adjustment range of each channel of the color-corrected image based on the standard white point gain and the preferred white point gain; determining the target adjustment range based on the gain adjustment range of each channel of the color-corrected image; and obtaining the target color-adaptive light source RGB value based on the color correction parameters and the target adjustment range.

[0009] According to one embodiment of this disclosure, determining the target adjustment range based on the gain adjustment range of each channel of the color-corrected image includes: obtaining the allowable fluctuation range of the gain adjustment range of each channel of the color-corrected image; and normalizing the allowable fluctuation range of each channel of the color-corrected image to generate the target adjustment range.

[0010] According to one embodiment of this disclosure, determining the target light source ratio based on the RGB values ​​of the target color-adaptive light source includes: determining the target light source ratio based on the D56 white point and the RGB values ​​of the target color-adaptive light source.

[0011] According to one embodiment of this disclosure, the color space conversion parameters include a first conversion parameter from CIE XYZ color space to RGB color space, a second conversion parameter from LMS color space to XYZ color space, a third conversion parameter from XYZ color space to LMS color space, and a fourth conversion parameter from RGB color space to XYZ color space. The step of determining the color adaptation parameter based on the target light source ratio and the color space conversion parameters includes: determining the color adaptation parameter based on the first conversion parameter, the second conversion parameter, the third conversion parameter, the fourth conversion parameter, and the target light source ratio.

[0012] According to one embodiment of this disclosure, after performing color adaptation transformation on the color-corrected image based on the standard white point gain and the preferred white point gain, the method further includes: obtaining a standard color gamut requirement; determining a target gamma curve based on the standard color gamut requirement; and performing gamma correction on the transformed image after adaptation transformation based on the color correction parameters based on the target gamma curve to generate the target image.

[0013] According to one embodiment of this disclosure, obtaining the standard color gamut requirement includes: obtaining a user's requirement instruction; and determining the standard color gamut requirement based on the requirement instruction.

[0014] According to a second aspect of the present disclosure, an electronic device is provided, comprising: an image processing module and a white balance module; the white balance module is configured to output a standard white point gain, a preferred white point gain, and light source parameters to the image processing module; the image processing module is configured to acquire the standard white point gain, the preferred white point gain, and the light source parameters output by the white balance module, and perform the method described in the first aspect embodiment.

[0015] According to one embodiment of this disclosure, the white balance module is further configured to: acquire image sensor data; calculate a standard white point gain and a light source type based on the image sensor data; calculate a correlated color temperature (CCT) based on the standard white point gain; calculate a preferred white point gain based on the standard white point gain, the CCT, and the image sensor data; and use the CCT and the light source type as the light source parameters.

[0016] According to one embodiment of this disclosure, the image sensor data further includes a raw image.

[0017] According to a third aspect of the present disclosure, an image processing apparatus is provided, comprising: an acquisition module, configured to acquire an original image to be processed, and acquire a standard white point gain, a preferred white point gain, and light source parameters; a correction module, configured to perform white balance correction on the original image using the standard white point gain, and to perform color correction on the white balance image obtained by the white balance processing according to the light source parameters, to obtain a color-corrected image; and a transformation module, configured to perform color adaptation transformation processing on the color-corrected image according to the standard white point gain and the preferred white point gain, to obtain a target image.

[0018] According to a fourth aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the steps of the image processing method described in the first aspect of the present disclosure.

[0019] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the image processing method described in the first aspect of the present disclosure.

[0020] According to a sixth aspect of the present disclosure, a chip is provided, the chip including an interface circuit and a processing circuit coupled to each other, the interface circuit being used to input or output signals, and the processing circuit being configured to implement the steps of the image processing method described in the first aspect of the present disclosure.

[0021] According to a seventh aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the image processing method described in the first aspect of the present disclosure.

[0022] The technical solutions provided by the embodiments of this disclosure bring at least the following beneficial effects: This disclosure uses standard white point gain combined with light source parameters for white balance and color correction, which ensures the physical accuracy of image color reproduction. It introduces preferred white point gain and standard white point gain for color adaptation transformation, realizes stylized rendering that conforms to human visual preferences, effectively solves the interference of preferred white point gain on color correction in the current technology, and improves the color fidelity, stability and style flexibility of the imaging system.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0025] Figure 1 This is a flowchart illustrating an image processing method according to an exemplary embodiment.

[0026] Figure 2 This is a flowchart illustrating an image processing method according to another exemplary embodiment.

[0027] Figure 3 This is a flowchart illustrating an image processing method according to another exemplary embodiment.

[0028] Figure 4 This is a flowchart illustrating an image processing method according to another exemplary embodiment.

[0029] Figure 5 This is a schematic diagram of an electronic device according to an exemplary embodiment.

[0030] Figure 6 This is a schematic diagram of the structure of an image processing apparatus according to an exemplary embodiment.

[0031] Figure 7 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment.

[0032] Figure 8 This is a schematic diagram of the structure of a chip according to an exemplary embodiment. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0034] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0035] Digital image processing refers to the technical field of using computers or specialized hardware to perform a series of operations on images, including acquisition, transformation, enhancement, analysis, and understanding. Its core objective is to extract useful information from images or improve their visual quality to meet the needs of human perception or machine analysis.

[0036] Video processing is an important branch of digital image processing that focuses on the acquisition, encoding, enhancement, analysis, and understanding of continuous time-series image frames (i.e., video) to improve visual quality, extract useful information, or support intelligent decision-making.

[0037] The following description, with reference to the accompanying drawings, describes an image processing method, air conditioning equipment, apparatus, electronic device, chip, and storage medium according to embodiments of the present disclosure.

[0038] Current color solutions can only achieve generally accurate color reproduction, and require significant adjustments. The reasons are as follows: 1. In the Auto White Balance (AWB) unit, the output is usually a preferred white point gain adjusted by gain preference. However, the gain preference adjustment part is usually a simple multiplication operation to adjust the white balance gain, without fully taking into account the color adaptation transformation in color theory. Therefore, the color reproduction effect is poor or does not meet expectations.

[0039] 2. In ISP, since its color correction parameters are pre-calibrated based on a certain AWB preference standard, when the preference white point gain of the AWB output changes, the color reproduction effect will not match the expectations during calibration.

[0040] 3. In ISP, because its color correction parameters are pre-calibrated based on a small number of standard light sources, and the triggering conditions are usually the color temperature and brightness information of the current scene, it is impossible to apply color correction parameters with similar color temperatures but different light source types differently.

[0041] 4. During debugging, when the AWB preference standard changes, it will cause the color correction parameters to be recalibrated. This mutual interference is not conducive to the rapid convergence of color effects and will significantly increase the workload of color debugging.

[0042] To address the aforementioned problems, this disclosure proposes an image processing method. Figure 1 This is a flowchart illustrating an image processing method according to an exemplary embodiment, such as... Figure 1 As shown, the image processing method of this disclosure includes the following steps.

[0043] S101, acquire the original image to be processed, and acquire the standard white point gain, preferred white point gain, and light source parameters.

[0044] It should be noted that the execution subject of the image processing method in this embodiment is an electronic device, such as a server or chip. The server may include a cloud server or a distributed server. The image processing method in this embodiment can be executed by the image processing device, which can be configured in any electronic device to execute the image processing method. The image processing method in this embodiment will be described below using a server as an example.

[0045] It should be noted that the Standard White Point Gain is the gain coefficient of the red, green, and blue channels used to achieve physically neutral white balance. It is usually calculated by the Automatic White Balance (AWB) module based on the light source estimation results.

[0046] Preferred White Point Gain is a white balance gain adjusted based on human visual preferences (such as warming skin tones and restoring ambient atmosphere) on top of the standard white balance, used to enhance the subjective perception of an image.

[0047] Illuminant parameters are information used to characterize the current ambient lighting characteristics, including but not limited to correlated color temperature (CCT), light source type (such as D65, A, TL84), and white point coordinates in the Red-Green-Blue (RGB) or XYZ domain.

[0048] In this embodiment of the disclosure, the original image is an image acquired by an image sensor. The original image may be an image that has undergone initial processing, such as an image that has undergone de-mosaic processing.

[0049] It should be noted that the quasi-white point gain, preferred white point gain, and light source parameters can be generated by the light sensing and adjustment module. The light sensing and adjustment module is a key functional unit in image signal processing (ISP), computer vision, or intelligent imaging systems used to automatically identify ambient lighting characteristics and dynamically adjust image parameters to optimize color performance. For example, this light sensing and adjustment module can be an automatic white balance (AWB) unit.

[0050] S102, perform white balance correction on the original image using standard white point gain, and perform color correction on the white balance image obtained from the white balance processing according to the light source parameters to obtain a color-corrected image.

[0051] In this embodiment, the white balance correction method based on standard white point gain can more accurately reflect the lighting conditions in real-world scenarios. It typically relies on the measurement or estimation of ambient light, thus better adapting to various lighting conditions rather than being limited to certain preset conditions. Compared to current white balance correction based on preferred white point gain, this method can incorporate color adaptation transformation from color theory to improve color reproduction.

[0052] When the color correction matrix is ​​adjusted according to specific light source parameters, more accurate color reproduction can be achieved. Using this method, users can flexibly select suitable white point gain and color correction parameters according to their actual needs. Compared to current technologies where preference settings may be limited by specific presets, color correction of the white balance image obtained from white balance processing based on light source parameters can achieve optimal imaging results in different environments.

[0053] S103, based on the standard white point gain and the preferred white point gain, perform color adaptation transformation on the color-corrected image to obtain the target image.

[0054] Preferred white point gain can perform color adaptation transformation on the color-corrected image according to human visual preferences, while standard white point gain can perform color adaptation transformation on the color-corrected image from a physical perspective. By combining standard white point gain and preferred white point gain to perform color adaptation transformation on the color-corrected image, the adaptiveness of human cone cells and the visual processing mechanism of the brain are fully considered. Compared with traditional schemes that perform simple gain adjustments within AWB, this disclosure achieves more accurate color reproduction based on the concept of color adaptation.

[0055] In this embodiment, the original image to be processed is first acquired, along with the standard white point gain, preferred white point gain, and light source parameters. Then, white balance correction is performed on the original image using the standard white point gain, and color correction is applied to the white balance image obtained from the white balance processing based on the light source parameters to obtain a color-corrected image. Finally, color adaptation transformation processing is performed on the color-corrected image based on the standard white point gain and preferred white point gain to obtain the target image. This disclosure uses standard white point gain combined with light source parameters for white balance and color correction, ensuring the physical accuracy of image color reproduction. The introduction of preferred and standard white point gains for color adaptation transformation achieves stylized rendering that conforms to human visual preferences, effectively solving the interference of preferred white point gain on color correction in current technologies, and improving the color fidelity, stability, and stylistic flexibility of the imaging system.

[0056] In the above embodiments, color correction is performed on the white balance image obtained from white balance processing based on the light source parameters to obtain a color-corrected image. Furthermore, it can be achieved through... Figure 2 To further explain, the method includes: S201, determine the color correction parameters based on the light source parameters.

[0057] In this embodiment of the disclosure, the color correction parameters are determined based on the light source parameters. First, the light source-correction parameter mapping relationship can be obtained, and then the light source parameters can be matched in the light source-correction parameter mapping relationship to determine the successfully matched color correction parameters.

[0058] It should be noted that the light source-correction parameter mapping relationship in the embodiments of this disclosure is generated based on the light source parameters calculated by AWB. The light source parameters may include light source type, light source spectrum, CCT, etc., and are not limited here.

[0059] In another possible implementation, the light source-correction parameter mapping relationship can be pre-built through calibration experiments before the device leaves the factory: standard color chart images are taken under a variety of known standard light sources, the optimal color correction parameters corresponding to each light source are obtained by minimizing the reconstructed color difference, and these parameters are bound and stored with the light source parameters.

[0060] It should be noted that the mapping relationship between the light source and the correction parameters is pre-designed and can be changed according to actual design needs; no restrictions are imposed here.

[0061] It should be noted that the light source parameters may include the light source type and the correlated color temperature (CCT).

[0062] Correlated color temperature (CCT) refers to the absolute temperature of a blackbody radiation source at a certain temperature when the color of the light emitted is closest to that of the light source being tested.

[0063] Light source type is a classification label for the actual lighting environment, indicating which physical light source is producing the current illumination. It includes not only color temperature information but also spectral distribution characteristics.

[0064] Therefore, the light source-correction parameter mapping relationship in this embodiment can be a mapping relationship based on the current correlated color temperature, light source type, and color correction parameters. After obtaining the correlated color temperature and light source type, the correlated color temperature and light source type can be matched with the light source-correction parameter mapping relationship to determine the currently preferred color correction parameters. This allows different color correction parameters to be applied to scenes with different color temperatures and different light source types, thereby improving the color reproduction effect.

[0065] S202, adjust the original RGB parameters of the white balance image according to the color correction parameters to determine the target RGB parameters.

[0066] It should be noted that the target RGB parameter is used to convert one color space (such as device-dependent RGB) to another color space (such as standard sRGB or CIE XYZ).

[0067] The process of adjusting the original RGB parameters of a white balance image based on color correction parameters to determine the target RGB parameters aims to map the colors captured by the sensor and affected by the light source and sensor response characteristics to the human eye's perceived colors in a standard color space (such as sRGB).

[0068] In the embodiments of this disclosure, there are various methods for adjusting the original RGB parameters of the white balance image according to the color correction parameters to determine the target RGB parameters. No limitation is made here. For example, the 3×3 linear color correction matrix method, lookup table method, piecewise linearization / regional correction, etc. can be used.

[0069] S203, perform color correction on the white balance image obtained from white balance processing according to the target RGB parameters to obtain a color-corrected image.

[0070] In the embodiments of this disclosure, there are various methods for adjusting the original RGB parameters of the white balance image according to the color correction parameters, and no limitation is made here. For example, the correction methods may include methods such as "3*3 linear color correction (LCC) matrix", "polynomial color correction (PCC)", "root polynomial color correction (RPCC)", "3D lookup table (3D LUT)", "color correction that preserves the hue plane" and "neural network".

[0071] For example, taking the basic "3x3 linear color correction (LCC) matrix" method, the correction process can be described as follows:

[0072] Among them, R out G out B out This represents the RGB color value of a pixel after color correction or transformation.

[0073] C 11 ~C 33 The target RGB parameters corresponding to the 3*3 color correction parameters for a specific light source type are used to convert one color space (such as device-dependent RGB) to another color space (such as standard sRGB or CIE XYZ).

[0074]

R bin G bin B bin

[0075] In this embodiment of the disclosure, color correction parameters are first determined based on light source parameters, then the original RGB parameters of the white balance image are adjusted based on the color correction parameters to determine the target RGB parameters, and finally the color-corrected image is determined based on the target RGB parameters.

[0076] In the above embodiments, the color-corrected image is subjected to color adaptation transformation processing based on the standard white point gain and the preferred white point gain to obtain the target image. Furthermore, it can be achieved through... Figure 3 To further explain, the method includes: S301, obtain the preset color space conversion parameters.

[0077] In this embodiment, the color space conversion parameters can be various, pre-designed, and can be changed according to actual design needs; no limitations are made here. For example, the RGB color space conversion parameters include a first conversion parameter from the CIE XYZ color space to the RGB color space, a second conversion parameter from the LMS color space to the XYZ color space, a third conversion parameter from the XYZ color space to the LMS color space, and a fourth conversion parameter from the RGB color space to the XYZ color space. See the table below:

[0078] S302 determines the color adaptation parameters based on color correction parameters, color space conversion parameters, standard white point gain, and preferred white point gain.

[0079] It should be noted that chromatic adaptation parameters refer to a set of parameters used to simulate or achieve a relatively constant perception of color by the human eye under different lighting conditions. The core objective is that even if the light source changes, the color of an object still appears essentially unchanged—a phenomenon known as "color constancy."

[0080] In this embodiment of the disclosure, the color correction parameters determined based on the light source parameters, and the RGB values ​​of the target color-adaptive light source determined based on the standard white point gain and the preferred white point gain, are then used to determine the target light source ratio based on the RGB values ​​of the target color-adaptive light source, and finally, the color adaptation parameters are determined based on the target light source ratio and the color space conversion parameters.

[0081] The steps for determining the color correction parameters based on the light source parameters can be referred to the content in the above embodiments, and will not be repeated here.

[0082] In this embodiment of the disclosure, the color correction parameters are determined based on the light source parameters, and the target color adapts to the light source RGB value is determined based on the standard white point gain and the preferred white point gain. First, the gain adjustment range of each channel of the color correction image is determined based on the standard white point gain and the preferred white point gain. Then, the target adjustment range is determined based on the gain adjustment range of each channel of the color correction image. Finally, the target color adapts to the light source RGB value is obtained based on the color correction parameters and the target adjustment range.

[0083] In this embodiment of the disclosure, the target adjustment range is determined based on the gain adjustment range of each channel of the color-corrected image. First, the allowable fluctuation range of the gain adjustment range of each channel of the color-corrected image can be obtained, and then the allowable fluctuation range of each channel of the color-corrected image can be normalized to generate the target adjustment range.

[0084] In this embodiment of the disclosure, the gain adjustment magnitude (r_gain_adjust, g_gain_adjust, b_gain_adjust) can be calculated based on the standard white point gain (r_gain_std, g_gain_std, b_gain_std) and the preferred white point gain (r_gain_pref, g_gain_pref, b_gain_pref):

[0085]

[0086]

[0087] The standard white point gain (r_gain_std, g_gain_std, b_gain_std) represents the gain of the R, G, and B channels under a standard light source (such as D65).

[0088] Preferred white point gain: (r_gain_pref, g_gain_pref, b_gain_pref), represents the desired color style or preference gain of the R, G, and B channels.

[0089] r_gain_adjust represents the adjustment factor of the red channel relative to the standard white point; g_gain_adjust represents the adjustment factor of the green channel relative to the standard white point; b_gain_adjust represents the adjustment factor of the blue channel relative to the standard white point.

[0090] It should be noted that the allowable fluctuation range of gain adjustment amplitude for each channel of a color-corrected image refers to the permissible fluctuation range (i.e., the maximum allowable deviation or tolerance range) of gain adjustment amplitude for each channel of a color-corrected image within an image processing system. This range is used to ensure the stability, consistency, and visual acceptability of color reproduction. This range is not a fixed value but is determined comprehensively based on the application scenario, equipment capabilities, standards and specifications, and the characteristics of human eye perception.

[0091] To eliminate absolute gain differences between different light sources, sensors, or devices, the allowable fluctuation range of gain adjustment amplitude for each channel of the color-corrected image is transformed into a uniform and comparable relative scale, thereby eliminating the influence of light source intensity differences. At the same time, through normalization, gain fluctuations can be converted into adjustment amplitudes that are perceptible to the human eye.

[0092] It should be noted that there are various methods for normalizing the allowable fluctuation range of each channel of the color-corrected image, and no limitation is made here.

[0093] For example, the normalized gain adjustment magnitudes (r_gain_adjust, g_gain_adjust, b_gain_adjust) can be maximized to obtain the normalized gain adjustment magnitudes (norm_r_gain_adjust, norm_g_gain_adjust, norm_b_gain_adjust):

[0094]

[0095]

[0096]

[0097] It should be noted that max_gain_adjust is the maximum gain adjustment factor among the three channels; norm_r_gain_adjust, norm_g_gain_adjust, and norm_b_gain_adjust are the normalized gain adjustment magnitudes of the red, green, and blue channels, respectively, serving as the target adjustment magnitudes.

[0098] In this embodiment of the disclosure, after obtaining the target adjustment range, the target adjustment range can be processed according to the color correction parameters to generate the target color-adaptive light source RGB value.

[0099] It should be noted that the target color adaptation light source RGB value refers to the RGB color value of the image under the target light source that is expected to match the image after chromatic adaptation transformation in the image processing process. It is the "ideal RGB" value that is expected to be displayed or output after processing such as white balance, color adaptation, and color correction.

[0100] In the embodiments disclosed herein, the correction method may be of various types, and no limitation is made here. The specific method may be limited according to the actual design requirements.

[0101] In this embodiment, the formula for correcting the normalized gain adjustment magnitude (norm_r_gain_adjust, norm_g_gain_adjust, norm_b_gain_adjust) to the target color-adaptive light source RGB value tgt_ca_ill_rgb can be as follows, assuming the color correction parameter is a 3*3 color correction matrix:

[0102] Where (tgt_ca_ill_rgb[0], tgt_ca_ill_rgb[1], tgt_ca_ill_rgb[2]) are the RGB values ​​of the target color-adapted light source.

[0103] In this embodiment of the disclosure, after obtaining the RGB value of the target color-adaptive light source, it is necessary to determine the proportion of the target light source based on the RGB value of the target color-adaptive light source.

[0104] It should be noted that the target light source ratio refers to the relative intensity ratio between the R, G, and B channels under the target color-adapted light source. It is usually based on the green channel (normalized to 1) or normalized to the maximum value.

[0105] Since RGB values ​​can vary depending on exposure, the ratio of the target light source can reflect the true color temperature. At the same time, different sensors have different sensitivities, making the ratio more universal.

[0106] In this embodiment of the disclosure, the target light source ratio can be determined based on the D56 white point and the RGB value of the target color-adaptive light source.

[0107] It should be noted that D56 white point is the international standard illuminant D65, representing average daylight. The target light source ratio is the RGB scaling factor of the target color-adapted light source RGB value relative to D65, used for white balance or light source normalization.

[0108] In this embodiment of the disclosure, the RGB value of "white" under the target light source can be obtained first, the transformation matrix of the RGB value can be calculated to convert it into the ideal white under the D56 white point, and the proportion of the target light source can be determined based on the transformation matrix.

[0109] In one possible implementation, the RGB values ​​of the target light source can be determined first, and then a color adaptation method can be applied to transform the overall color from the current state to the target state.

[0110] This disclosure does not limit the methods used for color adaptation. However, to more clearly illustrate the color adaptation transformation, the following description and steps will be based on the CIECAM16 color appearance model. Based on the "target D65 light source RGB value tgt_d65_ill_rgb" and the known 3*3 rgb2xyz matrix parameters, the "target D65 light source XYZ value norm_tgt_d65_ill_xyz based on Y normalization" is calculated as follows:

[0111]

[0112] Based on the above method, we can also obtain "the target color adaptive light source XYZ value norm_tgt_ca_ill_xyz based on Y normalization".

[0113] Then, based on the known 3*3 xyz2lms matrix parameters of the CIECAM16 color appearance model, the "target D65 light source XYZ value norm_tgt_d65_ill_xyz based on Y normalization" is converted into the "target D65 light source LMS value tgt_d65_ill_lms". The process is as follows:

[0114] Based on the above method, the "target color-adaptive light source LMS value tgt_ca_ill_lms" can also be obtained.

[0115] Finally, based on the "target color-adaptive light source LMS value tgt_ca_ill_lms" and the "target D65 light source LMS value tgt_d65_ill_lms", the "target light source ratio tgt_ill_ratio" is calculated. The process is as follows:

[0116] In this embodiment of the disclosure, the color space conversion parameters include a first conversion parameter from CIE XYZ color space to RGB color space, a second conversion parameter from LMS color space to XYZ color space, a third conversion parameter from XYZ color space to LMS color space, and a fourth conversion parameter from RGB color space to XYZ color space. The color adaptation parameters are determined based on the target light source ratio and the color space conversion parameters. After obtaining the target light source ratio, the color adaptation parameters can be determined based on the first conversion parameter, the second conversion parameter, the third conversion parameter, the fourth conversion parameter, and the target light source ratio.

[0117] It should be noted that there are various methods for determining the color adaptation parameters based on the first, second, third, and fourth conversion parameters and the target light source ratio; no limitation is made here. For example, it can be calculated using the following formula:

[0118] in, For color adaptation parameters, xyz2rgb, lms2xyz, xyz2lms, and rgb2xyz are the first, second, third, and fourth transformation parameters, respectively. The proportion of the target light source.

[0119] S303 performs color adaptation transformation on the color-corrected image based on color adaptation parameters to generate the target image.

[0120] In this embodiment of the disclosure, the method of performing color adaptation transformation on the color-corrected image based on color adaptation parameters to generate the target image can be multi-red, without any limitation. For example, the transformation can be performed based on the standard color adaptation model and the color gamut mapping matrix method. Alternatively, the transformation can be performed using the lookup table method. Alternatively, the transformation can be performed based on the color gamut clipping and remapping of the perceptual uniform color space, etc.

[0121] In another possible approach, the target image can be generated by performing color adaptation transformation on the color-corrected image based on color adaptation parameters using a pre-trained model or artificial intelligence.

[0122] In this embodiment, preset color space conversion parameters are first obtained. Then, based on the color correction parameters, color space conversion parameters, standard white point gain, and preferred white point gain, color adaptation parameters are determined. Finally, color adaptation transformation processing is performed on the color-corrected image based on the color adaptation parameters to generate the target image. Compared to the simple gain adjustment within AWB in the current technology, this disclosure achieves a more accurate color reproduction effect based on the concept of color adaptation. The color adaptation transformation parameters are calculated based on standard white gain and preferred white gain, and fully consider the adaptability of human cone cells and the visual processing mechanism of the brain.

[0123] In the above embodiments, after performing color adaptation transformation on the color-corrected image based on the standard white point gain and the preferred white point gain, the resulting image can be further processed, such as... Figure 4 As shown: S401, obtain standard color gamut requirements.

[0124] Standard color gamut requirements refer to the standardized color range specifications followed in image acquisition, processing, display, or printing to ensure color consistency and compatibility. Standard color gamut requirements can be pre-designed or customized by the user; no limitations are imposed here.

[0125] In one possible implementation, the user's requirements can be obtained, and then the standard color gamut requirements can be determined based on the requirements.

[0126] S402 determines the color correction parameters and target gamma curve according to standard color gamut requirements.

[0127] It should be noted that gamma correction converts the image from linear light signals (linear RGB) to non-linear encoded values ​​(encoded RGB) in order to match the non-linear perception of brightness by the human eye, while adapting to the physical response characteristics of the display device and preventing banding artifacts in dark areas.

[0128] Different standard color gamuts not only define the "range of colors" but also specify "how brightness is mapped." The "gamma curve" is essentially a photoelectric conversion function (OETF / EOTF), which determines whether the human eye perceives midtone details smoothly and how image data values ​​correspond to actual brightness. Therefore, different standard color gamuts correspond to different target gamma curves, and the target gamma curve needs to be determined according to the requirements of the standard color gamut.

[0129] In this embodiment of the disclosure, the target gamma curve can be determined by a preset mapping relationship and by standard color gamut requirements.

[0130] S403, perform gamma correction on the converted image after color adaptation transformation based on color correction parameters according to the target gamma curve to generate the target image.

[0131] It should be noted that, firstly, color correction parameters are used to map the RGB of the linear sensor color gamut to the RGB of the linear standard color gamut. Then, gamma is used to map the RGB of the linear standard color gamut to the RGB of the standard color gamut. Thus, through the collaborative processing of color correction parameters and gamma curves, gamma correction of the converted image is achieved.

[0132] In this embodiment, standard color gamut requirements are first obtained. Then, color correction parameters and a target gamma curve are determined based on the standard color gamut requirements. Finally, gamma correction is performed on the converted image after color adaptation transformation based on the color correction parameters according to the target gamma curve to generate the target image. Thus, gamma correction optimizes brightness distribution, enhances detail and tonal range, and dynamically selects the target gamma curve according to different application scenarios, supporting flexible switching between multiple modes and enhancing system adaptability.

[0133] Figure 5 This is a schematic diagram of an electronic device according to an exemplary embodiment, such as... Figure 5 As shown, the electronic device includes an image processing module 510 and a white balance module 520.

[0134] The white balance module is used to output standard white point gain, preferred white point gain, and light source parameters to the image processing module.

[0135] The image processing module is used to obtain the standard white point gain, preferred white point gain, and light source parameters output by the white balance module, and to perform operations such as... Figures 1-4 Image processing method of the embodiment.

[0136] In this embodiment of the disclosure, the white balance module is further configured to: acquire image sensor data; calculate standard white point gain and light source type based on the image sensor data; calculate correlated color temperature (CCT) based on the standard white point gain; calculate preferred white point gain based on the standard white point gain, CCT, and image sensor data, and use the CCT and light source type as light source parameters.

[0137] It should be noted that the image sensor can be a complementary metal-oxide-semiconductor image sensor (CMOS) and / or an ambient light sensor. The image sensor can generate standard white point gain and light source parameters based on the acquired data.

[0138] The white balance module determines the preferred white point gain (r_gain_pref, g_gain_pref, b_gain_pref) based on the standard white point gain (r_gain_std, g_gain_std, b_gain_std) and the correlated color temperature (CCT).

[0139] The determination method can be shown in the following formula: r_gain_pref=r_gian_std*r_gain_adjust g_gain_pref=1.0 b_gain_pref=b_gian_std*b_gain_adjust It should be noted that the image sensor data also includes the raw image. r_gain_adjust, g_gain_pref, and b_gain_adjust represent the gain adjustment magnitudes for the R, G, and B channels, respectively.

[0140] Figure 6 This is a schematic diagram of the structure of an image processing apparatus according to an exemplary embodiment.

[0141] Reference Figure 6 The image processing apparatus 600 of this disclosure includes: an acquisition module 610, a correction module 620, and a transformation module 630.

[0142] The acquisition module 610 is used to acquire the original image to be processed, and to acquire the standard white point gain, preferred white point gain, and light source parameters.

[0143] The correction module 620 is used to perform white balance correction on the original image using standard white point gain, and to perform color correction on the white balance image obtained by white balance processing according to the light source parameters, so as to obtain a color-corrected image.

[0144] The transformation module 630 is used to perform color adaptation transformation on the color-corrected image based on the standard white point gain and the preferred white point gain to obtain the target image.

[0145] According to one embodiment of this disclosure, color correction is performed on a white balance image obtained through white balance processing based on light source parameters to obtain a color-corrected image. The method includes: determining color correction parameters based on light source parameters; adjusting the original RGB parameters of the white balance image based on the color correction parameters to determine target RGB parameters; and performing color correction on the white balance image obtained through white balance processing based on the target RGB parameters to obtain a color-corrected image.

[0146] According to one embodiment of this disclosure, determining color correction parameters based on light source parameters includes: obtaining a light source-correction parameter mapping relationship; and matching the light source parameters in the light source-correction parameter mapping relationship to determine the successfully matched color correction parameters.

[0147] According to one embodiment of this disclosure, a color-adaptive transformation is performed on a color-corrected image based on a standard white point gain and a preferred white point gain to obtain a target image, including: obtaining preset color space conversion parameters; determining color adaptation parameters based on the color correction parameters, the color space conversion parameters, the standard white point gain, and the preferred white point gain; and performing a color adaptation transformation on the color-corrected image based on the color adaptation parameters to generate the target image.

[0148] According to one embodiment of this disclosure, color adaptation parameters are determined based on color correction parameters, color space conversion parameters, standard white point gain, and preferred white point gain. This includes: determining color correction parameters based on light source parameters, and determining the RGB values ​​of the target color adaptation light source based on the standard white point gain and preferred white point gain; determining the target light source ratio based on the RGB values ​​of the target color adaptation light source; and determining the color adaptation parameters based on the target light source ratio and the color space conversion parameters.

[0149] According to one embodiment of this disclosure, determining the target color-adaptive light source RGB value based on the color correction parameters determined by the light source parameters and the standard white point gain and preferred white point gain includes: determining the gain adjustment range of each channel of the color-corrected image based on the standard white point gain and preferred white point gain; determining the target adjustment range based on the gain adjustment range of each channel of the color-corrected image; and obtaining the target color-adaptive light source RGB value based on the color correction parameters and the target adjustment range.

[0150] According to one embodiment of this disclosure, determining a target adjustment range based on the gain adjustment range of each channel of a color-corrected image includes: obtaining the allowable fluctuation range of the gain adjustment range of each channel of the color-corrected image; and normalizing the allowable fluctuation range of each channel of the color-corrected image to generate the target adjustment range.

[0151] According to one embodiment of this disclosure, determining the target light source ratio based on the RGB values ​​of the target color-adaptive light source includes: determining the target light source ratio based on the D56 white point and the RGB values ​​of the target color-adaptive light source.

[0152] According to one embodiment of this disclosure, the color space conversion parameters include a first conversion parameter from the CIE XYZ color space to the RGB color space, a second conversion parameter from the LMS color space to the XYZ color space, a third conversion parameter from the XYZ color space to the LMS color space, and a fourth conversion parameter from the RGB color space to the XYZ color space. Determining color adaptation parameters based on the target light source ratio and the color space conversion parameters includes: determining the color adaptation parameters based on the first conversion parameter, the second conversion parameter, the third conversion parameter, the fourth conversion parameter, and the target light source ratio.

[0153] According to one embodiment of this disclosure, after performing a color adaptation transformation on a color-corrected image based on a standard white point gain and a preferred white point gain, the method further includes: obtaining a standard color gamut requirement; determining a target gamma curve based on the standard color gamut requirement; and performing gamma correction on the transformed image after the adaptation transformation based on the color correction parameters based on the target gamma curve to generate a target image.

[0154] According to one embodiment of this disclosure, obtaining standard color gamut requirements includes: obtaining a user's requirement instruction; and determining the standard color gamut requirements based on the requirement instruction.

[0155] This disclosure employs standard white point gain combined with light source parameters for white balance and color correction, ensuring the physical accuracy of image color reproduction. It introduces preferred white point gain and standard white point gain for color adaptation transformation, achieving stylized rendering that conforms to human visual preferences. This effectively solves the interference of preferred white point gain on color correction in current technologies, and improves the color fidelity, stability, and stylistic flexibility of the imaging system.

[0156] To implement the above embodiments, this disclosure also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the image processing method provided in this disclosure, and / or implements the steps of the image processing method provided in this disclosure.

[0157] Figure 7 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. For example, the electronic device 700 may be a vehicle, mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0158] Reference Figure 7 The electronic device 700 may include one or more of the following components: processing component 702, memory 704, power component 707, multimedia component 708, audio component 710, input / output (I / O) interface 712, sensor component 714, and communication component 717.

[0159] Processing component 702 typically controls the overall operation of electronic device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 702 may include one or more processors 720 to execute instructions to perform all or part of the steps of the aforementioned image processing method, and / or to perform all or part of the steps of the aforementioned image processing method. Furthermore, processing component 702 may include one or more modules to facilitate interaction between processing component 702 and other components. For example, processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.

[0160] Memory 704 is configured to store various types of data to support the operation of electronic device 700. Examples of this data include instructions for any application or method operating on electronic device 700, contact data, phonebook data, messages, pictures, videos, etc. Memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0161] Power component 707 provides power to various components of electronic device 700. Power component 707 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 700.

[0162] Multimedia component 708 includes a screen that provides an output interface between electronic device 700 and user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a Touch Panel, the screen may be implemented as a touchscreen to receive input signals from the user. The Touch Panel includes one or more touch sensors to sense touches, swipes, and gestures on the Touch Panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 708 includes a front-facing camera and / or a rear-facing camera. When electronic device 700 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0163] Audio component 710 is configured to output and / or input audio signals. For example, audio component 710 includes a microphone (MIC) configured to receive external audio signals when electronic device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 704 or transmitted via communication component 717. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.

[0164] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0165] Sensor assembly 714 includes one or more sensors for providing state assessments of various aspects of electronic device 700. For example, sensor assembly 714 may detect the on / off state of electronic device 700, the relative positioning of components such as the display and keypad of electronic device 700, changes in position of electronic device 700 or a component of electronic device 700, the presence or absence of user contact with electronic device 700, orientation or acceleration / deceleration of electronic device 700, and temperature changes of electronic device 700. Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 714 may also include an optical sensor, such as a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, sensor assembly 714 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0166] Communication component 717 is configured to facilitate wired or wireless communication between electronic device 700 and other devices. Electronic device 700 can access wireless networks based on communication standards, such as WiFi, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 717 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 717 also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra-Wideband (UWB), Bluetooth, and other technologies.

[0167] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the steps of the above-described image processing method, and / or to perform the steps of the above-described image processing method.

[0168] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, which can be executed by a processor 720 of an electronic device 700 to complete the aforementioned image processing method, and / or to complete the aforementioned image processing method. For example, the non-transitory computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0169] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the image processing method provided by this disclosure, and / or implement the steps of the image processing method provided by this disclosure.

[0170] Alternatively, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0171] To implement the above embodiments, this disclosure also proposes a chip including an interface circuit and a processing circuit coupled to each other. The interface circuit is used to input or output signals, and the processing circuit is configured to implement the steps of the image processing method provided in this disclosure, and / or implement the steps of the image processing method provided in this disclosure.

[0172] Figure 8 This is a schematic diagram illustrating the structure of a chip according to an exemplary embodiment. See also... Figure 8The diagram shown is a schematic representation of the structure of chip 800, but is not limited to this.

[0173] Chip 800 includes processing circuit 801, which is configured to perform the steps of any of the above image processing methods, and / or perform the steps of any of the above image processing methods.

[0174] In some embodiments, the chip 800 further includes one or more interface circuits 802. Optionally, the interface circuit 802 is connected to the memory 803, and the interface circuit 802 can be used to receive signals from the memory 803 or other devices, and the interface circuit 802 can be used to send signals to the memory 803 or other devices. For example, the interface circuit 802 can read instructions stored in the memory 803 and send the instructions to the processing circuit 801.

[0175] In some embodiments, the interface circuit 802 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 801 performs other steps.

[0176] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0177] In some embodiments, chip 800 further includes one or more memories 803 for storing instructions. Optionally, all or part of the memories 803 may be located outside of chip 800.

[0178] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the image processing method provided in this disclosure, and / or implements the steps of the image processing method provided in this disclosure.

[0179] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0180] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An image processing method, characterized by, The method comprises the following steps: acquiring an original image to be processed, and acquiring a standard white point gain, a preferred white point gain and a light source parameter; performing white balance correction on the original image by using the standard white point gain, and performing color correction on a white balance image obtained by the white balance processing according to the light source parameter to obtain a color correction image; performing color adaptation transformation processing on the color correction image according to the standard white point gain and the preferred white point gain to obtain a target image.

2. The method of claim 1, wherein, The color correction on the white balance image obtained by the white balance processing according to the light source parameter to obtain a color correction image comprises: determining a color correction parameter according to the light source parameter; adjusting original RGB parameters of the white balance image according to the color correction parameter to determine target RGB parameters; performing color correction on the white balance image obtained by the white balance processing according to the target RGB parameters to obtain the color correction image.

3. The method of claim 2, wherein, The determination of the color correction parameter according to the light source parameter comprises: acquiring a light source-correction parameter mapping relationship; matching the light source parameter in the light source-correction parameter mapping relationship to determine the color correction parameter that is matched successfully.

4. The method of claim 2, wherein, The color adaptation transformation processing on the color correction image according to the standard white point gain and the preferred white point gain to obtain a target image comprises: acquiring a preset color space conversion parameter; determining a color adaptation parameter based on the color correction parameter, the color space conversion parameter, the standard white point gain and the preferred white point gain; performing color adaptation transformation processing on the color correction image based on the color adaptation parameter to generate the target image.

5. The method of claim 4, wherein, The determination of the color adaptation parameter based on the color correction parameter, the color space conversion parameter, the standard white point gain and the preferred white point gain comprises: determining a target color adaptation light source RGB value according to the color correction parameter determined according to the light source parameter, and according to the standard white point gain and the preferred white point gain; determining a target light source proportion according to the target color adaptation light source RGB value; determining the color adaptation parameter according to the target light source proportion and the color space conversion parameter.

6. The method of claim 5, wherein, The determination of the target color adaptation light source RGB value according to the color correction parameter determined according to the light source parameter, and according to the standard white point gain and the preferred white point gain comprises: determining a gain adjustment amplitude of each channel of the color correction image according to the standard white point gain and the preferred white point gain; determining a target adjustment amplitude according to the gain adjustment amplitude of each channel of the color correction image; acquiring the target color adaptation light source RGB value according to the color correction parameter and the target adjustment amplitude.

7. The method of claim 6, wherein, The determination of the target adjustment amplitude according to the gain adjustment amplitude of each channel of the color correction image comprises: acquiring an allowable fluctuation range of the gain adjustment amplitude of each channel of the color correction image; performing normalization processing on the allowable fluctuation range of each channel of the color correction image to generate the target adjustment amplitude.

8. The method of claim 5, wherein, The determination of the target light source proportion according to the target color adaptation light source RGB value comprises: Determine the target light source proportion based on the D56 white point and the target color adapted light source RGB value.

9. The method of claim 5, wherein, The color space conversion parameters include first conversion parameters from CIE XYZ color space to RGB color space, second conversion parameters from LMS color space to XYZ color space, third conversion parameters from XYZ color space to LMS color space, and fourth conversion parameters from RGB color space to XYZ color space, and the determining the color adaptation parameters according to the target light source proportion and the color space conversion parameters includes: Determining the color adaptation parameters according to the first conversion parameters, the second conversion parameters, the third conversion parameters, the fourth conversion parameters and the target light source proportion.

10. The method of claim 2, wherein, After the color correction image is subjected to the color adaptation transformation processing according to the standard white point gain and the preferred white point gain, the method further includes: Obtaining a standard color gamut requirement; Determining color correction parameters and a target gamma curve according to the standard color gamut requirement; Performing gamma correction on the converted image subjected to the color adaptation transformation based on the color correction parameters according to the target gamma curve to generate the target image.

11. The method of claim 10, wherein, The obtaining of the standard color gamut requirement includes: Obtaining a requirement instruction of a user; Determining the obtaining of the standard color gamut requirement based on the requirement instruction.

12. An electronic device, comprising: It includes: An image processing module and a white balance module; The white balance module is configured to output a standard white point gain, a preferred white point gain and light source parameters to the image processing module; The image processing module is configured to obtain the standard white point gain, the preferred white point gain and the light source parameters output by the white balance module and execute the method according to any one of claims 1-11.

13. The electronic device of claim 12, wherein, The white balance module is further configured to: Obtain image sensor data, calculate a standard white point gain and a light source type based on the image sensor data; Calculate a correlated color temperature CCT based on the standard white point gain; Calculate a preferred white point gain based on the standard white point gain, the correlated color temperature CCT and the image sensor data, and take the correlated color temperature CCT and the light source type as the light source parameters.

14. The electronic device of claim 13, wherein, The image sensor data further includes an original image.

15. An image processing apparatus characterized by comprising: It includes: An obtaining module configured to obtain an original image to be processed and obtain a standard white point gain, a preferred white point gain and light source parameters; A correction module configured to perform white balance correction on the original image by using the standard white point gain and perform color correction on a white balance image obtained by the white balance processing according to the light source parameters to obtain a color correction image; A transformation module configured to perform color adaptation transformation processing on the color correction image according to the standard white point gain and the preferred white point gain to obtain a target image.

16. An electronic device, comprising: It includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the steps of the method according to any one of claims 1-11.

17. A computer-readable storage medium having stored thereon computer program instructions, wherein, The program instructions are executed by the processor to implement the steps of the method according to any one of claims 1-11.

18. A chip, characterized by The chip comprises an interface circuit and a processing circuit coupled to each other, the interface circuit is used for inputting or outputting signals, and the processing circuit is configured to implement the steps of the method in any one of claims 1-11.

19. A computer program product, characterised in that, The computer program is executed by a processor to implement the steps of the method in any one of claims 1-11.

Images