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

Abstract

The embodiment of the invention provides an image processing method and device, electronic equipment, a chip and a medium, and relates to the technical field of display algorithms, and the method comprises the steps: carrying out the fragmentation mapping of a to-be-processed image based on the current electric quantity or the current ambient light intensity in response to the condition that the current electric quantity or the current ambient light intensity meets a preset condition, and obtaining a target image. According to the method provided by the invention, the electric quantity of the current equipment and the light intensity of the environment where the equipment is located are judged through the preposed judgment condition, and when the electric quantity or the environment light intensity meets the preset condition, a fragment mapping processing mode based on the electric quantity or the environment light intensity is adopted, so that the effect of displaying a picture is improved on the premise of ensuring low power consumption, and the user experience is improved. The power consumption is further reduced, the equipment endurance time is prolonged, the daily average use duration is prolonged, and the user experience is improved.

Description

Technical Field

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

[0002] When smart display devices are used in environments with high light intensity, the display parameters of the image will be adjusted due to light reflection. For example, the brightness of dark areas will be increased to enhance the details in dark areas. Summary of the Invention

[0003] This disclosure provides an image processing method and apparatus, electronic device, chip and medium, and proposes a solution to improve image display effect.

[0004] A first aspect of this disclosure provides an image processing method, which includes: in response to the current power level or the current ambient light intensity meeting a preset condition, performing segmented mapping processing on the image to be processed based on the current power level or the current ambient light intensity to obtain a target image.

[0005] In some embodiments of this disclosure, the preset conditions include: the current battery level is greater than a preset battery level; and the current ambient light intensity is greater than a preset ambient light intensity.

[0006] In some embodiments of this disclosure, the image to be processed is sliced ​​and mapped based on the current battery level or the current ambient light intensity, including: determining the target slice gradient based on the current battery level or the current ambient light intensity; slicing the image to be processed into multiple slice images based on the target slice gradient; and adjusting the image parameters of the first slice image among the multiple slice images according to image feature information.

[0007] In some embodiments of this disclosure, determining the target segment gradient based on the current battery level or the current ambient light intensity includes: determining the gradient difference based on the current battery level or the current ambient light intensity; and determining the target segment gradient based on the gradient difference and the initial segment gradient.

[0008] In some embodiments of this disclosure, image parameter adjustment is performed on a first segment image among multiple segmented images based on image feature information, including: determining a segmentation sampling frequency based on the first image feature information of the first segment image; performing brightness sampling processing on the first segment image based on the segmentation sampling frequency to obtain first segment brightness statistics; and determining a first target image corresponding to the first segment image through a first processing based on the first segment brightness statistics.

[0009] In some embodiments of this disclosure, a first target image corresponding to a first segment image is determined through a first process based on first segment brightness statistics, including: performing equalization processing on the first segment brightness statistics to obtain first contrast mapping data; performing tone mapping synthesis processing on the first contrast mapping data based on a preset mode to obtain first brightness adjustment data; performing smoothing processing on the first brightness adjustment data to obtain first brightness mapping data corresponding to the first segment image; and performing mapping processing on the first segment image based on the first brightness mapping data to obtain the first target image.

[0010] In some embodiments of this disclosure, the method further includes: in response to the current battery level or the current ambient light intensity not meeting preset conditions, performing global mapping processing on the image to be processed based on the current battery level or the current ambient light intensity to obtain a target image.

[0011] In some embodiments of this disclosure, global mapping processing is performed on the image to be processed based on the current battery level or the current ambient light intensity, including: determining the global sampling frequency based on the current battery level or the current ambient light intensity to obtain global brightness statistics; and performing a first processing on the image to be processed based on the global brightness statistics.

[0012] In the above embodiments, the current power level of the device and the light intensity of the environment in which the device is located are judged by the pre-judgment conditions. When the power level or the ambient light intensity meets the preset conditions, the segmented mapping processing method based on the power level or the ambient light intensity is adopted to improve the display effect while ensuring low power consumption, further reduce power consumption, improve device battery life, increase daily usage time, and improve user experience.

[0013] A second aspect of this disclosure provides an image processing apparatus, comprising: a processing module, configured to, in response to a preset condition being met by the current power level or the current ambient light intensity, perform segmented mapping processing on an image to be processed based on the current power level or the current ambient light intensity to obtain a target image.

[0014] In some embodiments of this disclosure, the preset conditions include: the current battery level is greater than a preset battery level; and the current ambient light intensity is greater than a preset ambient light intensity.

[0015] In some embodiments of this disclosure, the processing module is used to determine the target segmentation gradient based on the current power level or the current ambient light intensity; to segment the image to be processed based on the target segmentation gradient to obtain multiple segmented images; and to adjust the image parameters of the first segmented image among the multiple segmented images according to image feature information.

[0016] In some embodiments of this disclosure, the processing module is used to determine the gradient difference based on the current power level or the current ambient light intensity; and to determine the target slice gradient based on the gradient difference and the initial slice gradient.

[0017] In some embodiments of this disclosure, the processing module is used to determine the segmentation sampling frequency based on the first image feature information of the first segmented image; perform brightness sampling processing on the first segmented image based on the segmentation sampling frequency to obtain the first segmented brightness statistics; and determine the first target image corresponding to the first segmented image through the first processing based on the first segmented brightness statistics.

[0018] In some embodiments of this disclosure, the processing module is used to perform equalization processing on the brightness statistics of the first segment to obtain first contrast mapping data; perform tone mapping synthesis processing on the first contrast mapping data based on a preset mode to obtain first brightness adjustment data; perform smoothing processing on the first brightness adjustment data to obtain first brightness mapping data corresponding to the first segment image; and perform mapping processing on the first segment image based on the first brightness mapping data to obtain a first target image.

[0019] In some embodiments of this disclosure, the processing module is used to perform global mapping processing on the image to be processed based on the current power level or the current ambient light intensity in response to the current power level or the current ambient light intensity not meeting the preset conditions, so as to obtain the target image.

[0020] In some embodiments of this disclosure, the processing module is used to determine the global sampling frequency based on the current power level or the current ambient light intensity, and obtain global brightness statistics; based on the global brightness statistics, the image to be processed is subjected to a first processing.

[0021] In summary, the image processing device proposed in this disclosure determines the current power level of the device and the light intensity of the environment in which the device is located by using pre-judgment conditions. When the power level or ambient light intensity meets the preset conditions, a segmented mapping processing method based on the power level or ambient light intensity is adopted to improve the display effect while ensuring low power consumption, further reduce power consumption, increase device battery life, increase daily usage time, and improve user experience.

[0022] A third aspect of this disclosure provides an electronic device comprising: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, performs the method described in any one of the first aspects of this disclosure, or includes an image processing apparatus as described in any one of the second aspects of this disclosure.

[0023] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any of the methods described in the first aspect of this disclosure.

[0024] A fifth aspect of this disclosure provides a chip including at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method described in any one of the first aspects of this disclosure through logic circuits or executing code instructions.

[0025] In summary, the method proposed in this disclosure can determine the current power level of the device and the light intensity of the environment in which the device is located by using pre-judgment conditions. When the power level or ambient light intensity meets the preset conditions, a segmented mapping processing method based on the power level or ambient light intensity is adopted to improve the display effect while ensuring low power consumption, further reduce power consumption, improve device battery life, increase daily usage time, and improve user experience.

[0026] 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

[0027] 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.

[0028] Figure 1 This is a schematic diagram of gamma correction;

[0029] Figure 2 Image showing the effect of brightening dark areas of a sunlight screen;

[0030] Figure 3 This is a schematic flowchart of the image processing method proposed in the embodiments of this disclosure;

[0031] Figure 4 This is a schematic diagram of the process for determining the target slice gradient according to an embodiment of this disclosure;

[0032] Figure 5 This is a schematic flowchart of the first process proposed in an embodiment of the present disclosure;

[0033] Figure 6 This is a schematic flowchart of the image processing method proposed in the embodiments of this disclosure;

[0034] Figure 7A This is a flowchart of Example 1;

[0035] Figure 7B This is a flowchart of Example 2;

[0036] Figure 8 This is a schematic diagram of the structure of an image processing apparatus 800 according to an embodiment of the present disclosure;

[0037] Figure 9 This is a schematic diagram illustrating an electronic device for implementing the above-described image processing method according to an exemplary embodiment;

[0038] Figure 10 This is a schematic diagram of the structure of a chip for implementing the above-described image processing method, according to an exemplary embodiment. Detailed Implementation

[0039] 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.

[0040] When smart display devices are used in ambient light, light reflection can make the screen of smartphones and other smart display devices unclear, especially in dark areas. For example... Figure 1 The diagram illustrating gamma correction shows how changing the gamma relationship can yield both the original image and the image under sunlight. Normally, the gamma input and output have a linear relationship, but altering this mapping, by brightening the grayscale in dark areas, can improve image visibility. Figure 2 The image shown illustrates the effect of a sunlight screen brightening dark areas.

[0041] In related technologies, there are two main problems with adjusting screen display: one is that the optimization effect is good but the power consumption increase is large, and the power consumption increase is small but the optimization effect is poor. It is impossible to simultaneously balance power consumption and display effect.

[0042] Therefore, in order to solve the above-mentioned technical problems, this disclosure proposes an image processing method to resolve the contradiction between display effect and power consumption.

[0043] Furthermore, the image processing method proposed in this disclosure can be applied to terminal devices in any scenario, especially to terminal devices under sunlight. The terminal device has a display interface, and the method of this disclosure can improve the image quality of the display device under sunlight and reduce power consumption.

[0044] The image processing method proposed in this application will be described in detail below with reference to the accompanying drawings.

[0045] Figure 3 This is a flowchart of an image processing method proposed in an embodiment of the present disclosure, as follows: Figure 3 As shown, the method includes the following steps:

[0046] Step 301: In response to the current power level or current ambient light intensity meeting the preset conditions, the image to be processed is segmented and mapped based on the current power level or current ambient light intensity to obtain the target image.

[0047] In some embodiments, the image to be processed is an image of the terminal device's currently displayed interface.

[0048] In some embodiments, the current battery level is the battery level of the terminal device, and the current ambient light intensity is the ambient light intensity detected by the sensor of the terminal device.

[0049] In some embodiments, the preset conditions include: the current battery level is greater than the preset battery level; and the current ambient light intensity is greater than the preset ambient light intensity.

[0050] In some embodiments, the preset power consumption and preset ambient light intensity are pre-set thresholds, which can be customized according to the scenario, requirements, or experimental calibration. It is understood that the preset power consumption and preset ambient light intensity are used to determine whether the current terminal device supports segmented mapping processing. That is, when any parameter value exceeds the preset threshold, segmented mapping processing is adopted, which can ensure the display quality effect after image processing and make the power consumption low.

[0051] For example, the preset battery level is 20%, and the preset ambient light intensity is 2000 LUX.

[0052] In some embodiments, if the current battery level meets a preset condition, the image to be processed is segmented and mapped based on the current battery level; if the current ambient light intensity meets a preset condition, the image to be processed is segmented and mapped based on the current ambient light intensity; if both the current battery level and the current ambient light intensity meet preset conditions, the image to be processed can be segmented and mapped based on either the current battery level or the current ambient light intensity.

[0053] Specifically, when both the current battery level and the current ambient light intensity meet the preset conditions, the difference between the current battery level and the preset battery level, and the difference between the current ambient light intensity and the preset ambient light intensity can be compared. After normalizing the two differences, the parameter with the smaller difference is used to perform slice mapping processing on the image to be processed, so as to reduce the power consumption required for image processing while ensuring the image processing quality.

[0054] For example, to enable the ACAD module and debug it, Tone Mapping (TM) must be enabled, meaning that EO TF (electro-optical transfer function) and OETF (o-electric transfer function) in TM must be enabled. Add a conditional statement: determine the current conditions of the phone to decide whether to use GTM or LTM for image analysis; if the battery level is greater than a threshold condition X (e.g., X = 20%), LTM will be executed.

[0055] For example, to enable the ACAD module and debug it, Tone Mapping (TM) must be enabled, meaning that EO TF (electro-optical transfer function) and OETF (o-electric transfer function) must be enabled in TM. Add a conditional statement: determine the current conditions of the phone to decide whether to use GTM or LTM for image analysis; if the ambient light intensity is greater than the threshold condition Y (e.g., Y = 20000 LUX), LTM will be executed.

[0056] In some embodiments, the image to be processed is sliced ​​and mapped based on the current battery level or the current ambient light intensity, including: determining the target slice gradient based on the current battery level or the current ambient light intensity; slicing the image to be processed into multiple slice images based on the target slice gradient; and adjusting the image parameters of the first slice image among the multiple slice images according to image feature information.

[0057] In some embodiments, the target segment gradient is determined based on the current battery level or the current ambient light intensity. This can be done by determining the target segment gradient based on the current battery level when the current battery level meets a preset condition, or by determining the target segment gradient based on the current ambient light intensity when the current ambient light intensity meets a preset condition.

[0058] In some embodiments, determining the target segment gradient can be based on a pre-set gradient table, in which the segment gradient corresponding to the current power level or the current ambient light intensity is selected.

[0059] In some embodiments, the target segment gradient can be determined based on the difference between the current battery level and a preset battery level, or based on the difference between the current ambient light intensity and a preset ambient light intensity, according to a pre-defined correspondence between the difference and the segment gradient.

[0060] In some embodiments, the target slicing gradient can be determined based on a formula for calculating the gradient between electrical charge or ambient light intensity and slicing gradient. This formula can be determined through experiments or tests.

[0061] In some embodiments, the target segment gradient can be determined according to a preset rule. For example, if the current battery level exceeds twice the preset battery level, a larger segment gradient is used, such as 20×20. If the current battery level exceeds the preset battery level by less than one time, a smaller segment gradient is used, such as 5×5. The same applies to the current ambient light intensity, which will not be elaborated here.

[0062] In some embodiments, the image to be processed is sliced ​​based on the target slice gradient to obtain multiple sliced ​​images. This can be achieved by determining the target slice gradient in the manner described above, and then slicing the image to be processed according to the number of horizontal and vertical blocks of the target slice gradient to obtain multiple sliced ​​images.

[0063] For example, select the horizontal and vertical local block counts --> Blk HoriNum / Blk Vert Num to perform partitioning analysis on the image. For instance, the horizontal block Blk HoriNum = 6, and the vertical block Blk Vert Num = 8.

[0064] In some embodiments, image feature information may include features such as rich high-frequency details or low noise, high noise or smooth regions or scenes dominated by low frequencies, and the need to avoid block artifacts, etc.

[0065] In some embodiments, adjusting the image parameters of a first segmented image among multiple segmented images based on image feature information includes: determining a segmentation sampling frequency based on the first image feature information of the first segmented image; performing brightness sampling processing on the first segmented image based on the segmentation sampling frequency to obtain first segmented brightness statistics; and determining a first target image corresponding to the first segmented image through a first processing based on the first segmented brightness statistics.

[0066] In some embodiments, determining the segment sampling frequency based on the first image feature information of the first segment image may involve determining the corresponding sampling method according to the first image feature information, and then determining the segment sampling frequency.

[0067] Specifically, in images with rich high-frequency details and low noise, subsampling can be used, while in high-noise or smooth regions or low-frequency dominant scenes, average sampling can be used, and then the specific sampling frequency can be determined according to the current needs.

[0068] In some embodiments, determining the segmented sampling frequency can be done after determining the segmented sampling method, based on the level of detail required for image processing. For example, for high detail requirements, one of two pixels can be selected in the sub-sampling method; for lower detail requirements, one of six or more pixels can be selected in the sub-sampling method. Alternatively, in the average sampling method, for high detail requirements, two pixels can be selected to take the average value; for lower detail requirements, six or more pixels can be selected to take the average value.

[0069] In some embodiments, based on the segmented sampling frequency, brightness sampling processing is performed on the first segmented image to obtain the first segmented brightness statistics. This can be based on the segmented sampling frequency to sample the brightness of the pixels in the first segmented image to obtain the first segmented brightness statistics corresponding to the first segmented image. The first segmented brightness statistics can be a brightness histogram corresponding to the first segmented image or a brightness bar chart. Specifically, the form of the brightness statistics is not limited in this disclosure, and it can reflect the brightness of each pixel.

[0070] For example, select the statistical method of the local histogram, Hist Mode (pixel sampling methods are divided into 1. Subsample: one of 4 or more pixels; 2. Average: the average of 4 or more pixels).

[0071] In some embodiments, based on the first segment brightness statistics, the first target image corresponding to the first segment image is determined through the first processing. This can be based on the first segment brightness statistics and operations such as equalization and contrast stretching are performed to change the brightness values ​​of the pixels in the first segment image in order to obtain the first target image.

[0072] In some embodiments, the first processing may be based on the brightness statistics of the first slice image to adjust the brightness parameters in order to obtain the first target image. The specific method of the first processing may follow the subsequent processing procedures in GTM or LTM in related technologies.

[0073] In the above embodiments, the current power level of the device and the light intensity of the environment in which the device is located are judged by the pre-judgment conditions. When the power level or the ambient light intensity meets the preset conditions, a segmented mapping processing method based on the power level or the ambient light intensity is adopted to improve the display effect while ensuring low power consumption. This can further reduce power consumption, increase device battery life, increase daily usage time, and improve the user experience.

[0074] Figure 4 This is a schematic diagram of the process for determining the target piecewise gradient according to an embodiment of this disclosure. Based on Figure 3 The embodiment shown, Figure 4 right Figure 3 Step 301 is further defined, such as Figure 4 As shown, the method includes the following steps:

[0075] Step 401: Determine the gradient difference based on the current battery level or the current ambient light intensity.

[0076] In some embodiments, if the current battery level meets preset conditions, the gradient difference corresponding to the current battery level can be determined based on the current battery level and according to a preset mapping relationship.

[0077] For example, if the current battery level is 50% and the baseline battery level is 70%, the gradient difference corresponding to the baseline battery level is 0. For every 10% decrease, the corresponding gradient difference increases by 1, and the gradient difference corresponding to 50% is 2.

[0078] In some embodiments, if the current ambient light intensity meets preset conditions, the gradient difference corresponding to the current ambient light intensity can be determined based on the current ambient light intensity and according to a preset mapping relationship.

[0079] For example, if the current ambient light intensity is 30,000 LUX and the reference ambient light intensity is 50,000 LUX, the corresponding gradient difference is 0. If the gradient difference increases by 1 for every 10,000 LUX decrease in the current ambient light intensity, then the gradient difference corresponding to the current ambient light intensity is 2.

[0080] Step 402: Determine the target piece gradient based on the gradient difference and the initial piece gradient.

[0081] In some embodiments, the initial slice gradient is the slice gradient corresponding to the reference ambient light intensity. Based on the gradient difference, the target slice gradient is determined by the gradient difference between the initial slice gradient decrease and increase.

[0082] For example, if the current battery level is 50%, the corresponding gradient difference is 2, the initial slice gradient is 20×20, and for each additional gradient difference, the slice gradient decreases by 4 in each direction, then the target slice gradient is 12×12.

[0083] For example, if the current ambient light intensity is 30000 LUX, the gradient difference corresponding to the current ambient light intensity is 2, the initial piecewise gradient is 20×20, and for each additional gradient difference, the piecewise gradient decreases by 5 in each direction, then the target piecewise gradient is 10×10.

[0084] In some embodiments, the rules for determining the gradient difference, as well as the rules for determining the initial segmented gradient and the target segmented gradient, can be customized according to the scenario or requirements. This disclosure does not limit this. For example, the gradient difference can be preset to correspond to different values ​​of the current power level and the current ambient light intensity, as well as the segmented gradient corresponding to the gradient difference. Thus, when the current ambient light intensity or the current power level is determined, the target segmented gradient can be obtained directly. Alternatively, an initial segmented gradient can be set, and the value of the corresponding segmented gradient decrease or increase in each direction for each gradient difference added or decreased.

[0085] In the above embodiments, the higher the current battery level or the current ambient light intensity, the larger the corresponding target segment gradient, and vice versa. It can be understood that if the current battery level is higher, it means that the terminal device's battery level can support a segmentation mapping process with more segments, and vice versa. Therefore, it is necessary to reduce the segmentation gradient to reduce the number of segments that the terminal device performs segmentation mapping processing, thereby reducing the time and energy required for the segmentation mapping process.

[0086] In the above embodiments, by setting different segmentation gradients, the segmentation gradient currently used can be dynamically determined based on the current battery level or the current ambient light intensity to segment the image to be processed. The appropriate segmentation gradient can be determined by comprehensively considering the current device status or the current environment to adapt to the current battery level or environment of the terminal device, thereby improving processing efficiency while ensuring image processing effect.

[0087] Figure 5 This is a schematic flowchart of the first process proposed in an embodiment of this disclosure. Based on Figures 3-4 The embodiment shown, Figure 5 right Figure 3 Step 301 in the text is further defined, such as Figure 5 As shown, it includes the following steps.

[0088] Step 501: Perform equalization processing on the brightness statistics of the first segment to obtain the first contrast mapping data.

[0089] In some embodiments, the equalization process may involve adjusting the brightness values ​​of the first segment brightness statistics to equalize the overall brightness of the first segment image and obtain the first contrast mapping data.

[0090] In some embodiments, the equalization process may involve cropping the brightness statistics of the first segment and distributing the brightness values ​​exceeding a preset value evenly to other pixels, so that the brightness of each pixel in the first segment image is equalized.

[0091] In some embodiments, the equalization process can be Hist Limit mode (truncated equalization), Hist mode (direct equalization), etc. Hist Limit mode can truncate the luminance statistics, limiting the extreme pixel proportions of highlights and shadows, and then equalize the truncated luminance statistics, i.e., redistributing pixel brightness; Hist mode can directly perform global equalization on the luminance statistics, stretching the brightness distribution to the entire usable range.

[0092] For example, select the contrast curve mode: histlimit for equalization; Hist mode for direct equalization.

[0093] In some embodiments, automatic selection can be made according to the scene and needs. For example, for high dynamic range images and backlight scenes, the truncation equalization mode can be used, while for low contrast images, such as foggy or hazy scenes, the direct equalization mode can be used.

[0094] Step 502: Based on the preset mode, the first contrast mapping data is subjected to tone mapping synthesis processing to obtain the first brightness adjustment data.

[0095] In some embodiments, the preset mode may be an automatic selection of the corresponding mode for different scenes from the preset tone mapping synthesis processing modes, so as to perform tone mapping synthesis processing on the first contrast mapping data according to the preset mode to obtain brightness adjustment data reflecting the brightness.

[0096] In some embodiments, the preset mode may be manual mode, calculation mode, inter-frame adaptive mode, etc.

[0097] In some embodiments, tone mapping compositing can be performed based on first contrast mapping data and all tones can be combined into a single mapping curve to obtain first brightness adjustment data.

[0098] In some embodiments, the first contrast mapping data may be in the form of a histogram, and the first brightness adjustment data may be a mapping curve.

[0099] In some embodiments, the first brightness adjustment data is used to adjust the brightness in the brightness channel.

[0100] For example, select the histogram weight curve mode (0: manual mode; 1: calculation mode; 2: inter-frame adaptive mode).

[0101] Step 503: Smooth the first brightness adjustment data to obtain the first brightness mapping data corresponding to the first segment image.

[0102] In some embodiments, smoothing the first brightness adjustment data may be a time-domain smoothing process.

[0103] In some embodiments, temporal smoothing processing may involve inter-frame mixing and update control of the first brightness adjustment data to obtain the first brightness mapping data.

[0104] In some embodiments, temporal smoothing can be achieved by setting parameters related to tone mapping enhancement, inter-frame smooth transition, and the number of frames for gradual stop, in order to obtain first luminance mapping data through smoothing.

[0105] In some embodiments, tone mapping enhancement may involve setting a target value (DstAlpha) and switching the current Alpha value (controlling curve intensity) to the target value.

[0106] In some embodiments, smooth inter-frame transition can be achieved by mixing the tone mapping curves between adjacent frames according to weights, where the weights can be mixing coefficients that can be customized according to the scene or requirements.

[0107] In some embodiments, the gradient stop frame number can be set at which frame the image stops changing, i.e. how many frames it takes to reach the target value, thereby avoiding sudden changes in image brightness.

[0108] For example, select the Tone Mapping enhancement method: Manual (Alpha value changes directly to the set target DstAlpha value); Auto (Alpha value automatically and smoothly changes to the set target DstAlpha value). Set the blending coefficient between the current frame's Tone Mapping curve and the previous frame: Curve Alpha, for a smooth transition. Set the frame number where the image stops changing during the Alpha Step gradient.

[0109] Step 504: Based on the first brightness mapping data, perform mapping processing on the first segmented image to obtain the first target image.

[0110] In some embodiments, the first segmented image is mapped based on the first brightness mapping data, thereby mapping the brightness of the first segmented image according to the brightness value of the first brightness mapping data, thereby obtaining the first target image.

[0111] In the above embodiments, through the first processing, the brightness mapping data of the first segment image can be obtained through equalization processing, tone mapping synthesis processing, and smoothing processing. The first segment image is then mapped to obtain the first target image with adjusted brightness. The equalization processing can achieve equalization of brightness and contrast, the smoothing processing can achieve a smooth transition between two adjacent frames, and by setting the number of frames at which the transition stops, abrupt changes in screen brightness are avoided. The obtained first brightness mapping data can change the display of the first segment image, making the display effect of the first target image better, extending the battery life of the terminal device, and improving the overall user experience.

[0112] Figure 6 This is a schematic flowchart of the image processing method proposed in an embodiment of this disclosure. Based on Figures 3-5 The illustrated embodiments, such as Figure 6 As shown, it also includes the following steps.

[0113] Step 601: In response to the fact that the current battery level and current ambient light intensity do not meet the preset conditions, perform global mapping processing on the image to be processed based on the current battery level or current ambient light intensity to obtain the target image.

[0114] In some embodiments, the global mapping process includes: determining a global sampling frequency based on the current battery level or the current ambient light intensity to obtain global brightness statistics; and performing a first processing on the image to be processed based on the global brightness statistics.

[0115] In some embodiments, global mapping processing performs overall brightness mapping on the image to be processed to obtain the target image. It is understood that if the current battery level and ambient light intensity do not meet preset conditions, it means that the current terminal device and its environment cannot support the time or energy required for partitioned mapping processing. In such cases, global mapping processing is adopted, which can complete the image processing in a shorter time and reduce power consumption.

[0116] In some embodiments, the global sampling frequency is determined based on the current battery level or the current ambient light intensity, which may be according to... Figure 3 The method for determining the sampling frequency of the first image segment in step 301 will not be described here.

[0117] For example, if the battery level is less than or equal to threshold condition X (e.g., X = 20%), a global mapping analysis will be performed; if the ambient light level is less than or equal to threshold condition Y (e.g., Y = 20000 LUX), a global mapping analysis will be performed.

[0118] In some embodiments, the first process includes: equalizing global brightness statistics to obtain second contrast mapping data; performing tone mapping synthesis on the second contrast mapping data based on a preset mode to obtain second brightness adjustment data; smoothing the second brightness adjustment data to obtain second brightness mapping data corresponding to the image to be processed; and performing mapping processing on the image to be processed based on the second brightness mapping data to obtain a target image.

[0119] In some embodiments, a detailed implementation of the first process may be found in [reference needed]. Figure 5 The specific implementation methods shown are not described in detail here.

[0120] In summary, the image processing method proposed in this disclosure judges the current power level of the device and the light intensity of the environment in which the device is located by pre-judging conditions. When the power level or ambient light intensity meets the preset conditions, a segmented mapping processing method based on the power level or ambient light intensity is adopted to improve the display effect while ensuring low power consumption.

[0121] The following are specific implementation methods for image processing:

[0122] Figure 7A This is a flowchart of Example 1. It includes the following steps:

[0123] 1. Enable the ACAD module --> Enable. To debug this module, TM (Tone Mapping) needs to be enabled, that is, EOTF (electro-optical transfer function) and OETF (electro-optical transfer function) in TM need to be enabled.

[0124] 2. Add judgment conditions:

[0125] Determine the current conditions of the phone to decide on the image analysis method: whether to use GTM (Global Tone Mapping) or LTM (Local Tone Mapping).

[0126] 2.1 If the battery charge (remaining charge) is greater than the threshold condition X (e.g., X = 20%), step 2.2, Partition Mapping Analysis (LTM), will be executed.

[0127] 2.2 Select the number of local blocks in the horizontal and vertical directions --> Blk Horizontal Num / Blk Vertical Num to perform partitioning analysis on the image. For example, the horizontal block Blk Horizontal Num = 6, and the vertical block Blk Vertical Num = 8.

[0128] 2.3 If the battery charge is less than or equal to the threshold condition X (e.g., X = 20%), step 3, Global Mapping Analysis (LTM), will be performed.

[0129] 3. Select the statistical method for the Local histogram --> Histogram Mode (pixel sampling methods are divided into 1. Subs ample: one of 4 or more pixels; 2. Average: the average of 4 or more pixels).

[0130] 4. Select the contrast curve mode --> Cc Mode (histlimit: histlimit performs equalization; His: Hist mode performs equalization directly).

[0131] 5. Select the tone mapping enhancement method --> Tm Mode (Manual: Alpha value changes directly to the set target DstAlpha value; Auto: Alpha value automatically and smoothly changes to the set target DstAlpha value).

[0132] 6. Set the Tone Mapping curve of the current frame and the Blending coefficient of the previous frame --> Curve Alpha for a smooth transition.

[0133] 7. When setting Alpha Step gradients, specify which frame the image stops changing in --> Skip Frames to avoid sudden changes in image brightness.

[0134] 8. Select the histogram weight curve mode. Dynamically adjust the weight curve of local tone mapping (i.e., the distribution of enhancement intensity in different brightness areas), adapt to scene requirements through different modes, and ultimately optimize contrast distribution and detail performance.

[0135] The execution order of step 8 can be between steps 4 and 5.

[0136] Figure 7B This is a flowchart of Example 2. It includes the following steps:

[0137] 1. Enable the ACAD module --> Enable. To debug this module, TM (Tone Mapping) needs to be enabled, that is, EOTF (electro-optical transfer function) and OETF (electro-optical transfer function) in TM need to be enabled.

[0138] 2. Add judgment conditions: Determine the current conditions of the mobile phone to decide the image analysis method, whether to use GTM or LTM.

[0139] 2.1 If the ambient light intensity is greater than the threshold condition Y (e.g., Y = 20000 LUX), step 2.2, Partition Mapping Analysis (LTM), will be executed;

[0140] 2.2 Select the number of local blocks in the horizontal and vertical directions --> Blk Horizontal Num / Blk Vertical Num to perform partitioning analysis on the image. For example, the horizontal block Blk Horizontal Num = 6, and the vertical block Blk Vertical Num = 8.

[0141] 2.3 If the ambient light intensity is less than or equal to the threshold condition Y (e.g., Y = 20000 LUX), then perform step 3, Global Mapping Analysis (GTM).

[0142] 3. Select the local histogram statistical method --> Histogram Mode (pixel sampling methods are divided into 1. Subs ample: one of 4 or more pixels; 2. Average: the average of 4 or more pixels).

[0143] 4. Select the contrast curve mode --> Cc Mode (histlimit: histlimit performs equalization; His: Hist mode performs equalization directly).

[0144] 5. Select the tone mapping enhancement method --> Tm Mode (Manual: Alpha value changes directly to the set target DstAlpha value; Auto: Alpha value automatically and smoothly changes to the set target DstAlpha value).

[0145] 6. Set the Tone Mapping curve of the current frame and the Blending coefficient of the previous frame --> Curve Alpha for a smooth transition.

[0146] 7. When setting Alpha Step gradients, specify which frame the image stops changing in --> Skip Frames to avoid sudden changes in image brightness.

[0147] 8. Select the histogram weight curve mode to dynamically adjust the weight curve of local tone mapping (i.e., the distribution of enhancement intensity in different brightness areas). Adapt to scene requirements through different modes to ultimately optimize contrast distribution and detail performance.

[0148] The execution order of step 8 is between steps 4 and 5.

[0149] In summary, the beneficial effects of this solution are as follows:

[0150] By adding conditions such as remaining battery power and ambient light intensity, it can determine whether to use global mapping analysis or partition mapping analysis. This satisfies the display effect experience while extending the battery life of smart devices, increasing DOU (Daily Operating Usage), and further improving the overall user experience of smart devices.

[0151] Figure 8 This is a schematic diagram of the structure of an image processing apparatus 800 according to an embodiment of the present disclosure. Figure 8 As shown, the device includes:

[0152] The processing module 810 is used to perform segmented mapping processing on the image to be processed based on the current power level or the current ambient light intensity when the preset conditions are met, so as to obtain the target image.

[0153] In some embodiments, the preset conditions include: the current battery level is greater than the preset battery level; and the current ambient light intensity is greater than the preset ambient light intensity.

[0154] In some embodiments, the processing module is further configured to determine the target segmentation gradient based on the current power level or the current ambient light intensity; segment the image to be processed based on the target segmentation gradient to obtain multiple segmented images; and adjust the image parameters of the first segmented image among the multiple segmented images according to image feature information.

[0155] In some embodiments, the processing module is further configured to determine the gradient difference based on the current power level or the current ambient light intensity; and to determine the target slice gradient based on the gradient difference and the initial slice gradient.

[0156] In some embodiments, the processing module is further configured to determine a segmentation sampling frequency based on the first image feature information of the first segmented image; perform brightness sampling processing on the first segmented image based on the segmentation sampling frequency to obtain first segmented brightness statistics; and determine the first target image corresponding to the first segmented image through the first processing based on the first segmented brightness statistics.

[0157] In some embodiments, the processing module is further configured to perform equalization processing on the brightness statistics of the first segment to obtain first contrast mapping data; perform tone mapping synthesis processing on the first contrast mapping data based on a preset mode to obtain first brightness adjustment data; perform smoothing processing on the first brightness adjustment data to obtain first brightness mapping data corresponding to the first segment image; and perform mapping processing on the first segment image based on the first brightness mapping data to obtain a first target image.

[0158] In some embodiments, the processing module is further configured to, in response to the current power level or current ambient light intensity not meeting the preset conditions, perform global mapping processing on the image to be processed based on the current power level or current ambient light intensity to obtain the target image.

[0159] In some embodiments, the processing module is further configured to determine the global sampling frequency based on the current power level or the current ambient light intensity, and obtain global brightness statistics; and perform a first processing on the image to be processed based on the global brightness statistics.

[0160] In summary, the image processing device proposed in this disclosure determines the current power level of the device and the light intensity of the environment in which the device is located by using pre-judgment conditions. When the power level or ambient light intensity meets the preset conditions, a segmented mapping processing method based on the power level or ambient light intensity is adopted to improve the display effect while ensuring low power consumption, further reduce power consumption, increase device battery life, increase average daily usage time, and improve user experience.

[0161] Regarding the image processing apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0162] Figure 9 This is a schematic diagram of the structure of an electronic device 900 for implementing the above-described image processing method, according to an exemplary embodiment.

[0163] Reference Figure 9 The electronic device 900 may include one or more of the following components: a processing component 902, a memory 904, a power supply component 906, an input / output (I / O) interface 908, a sensor component 910, and a communication component 912.

[0164] Processing component 902 typically controls the overall operation of electronic device 900, such as operations associated with display, telephone calls, data communication, battery management, and recording. Processing component 902 may include one or more processors 920 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include an equalization module to facilitate interaction between power supply component 906 and processing component 902.

[0165] Memory 904 is configured to store various types of data to support the operation of electronic device 900. Examples of this data include instructions for any application or method operating on electronic device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 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.

[0166] Power supply component 906 provides power to various components of electronic device 900. Power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 900.

[0167] I / O interface 908 provides an interface between processing component 902 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.

[0168] Sensor assembly 910 includes one or more sensors for providing state assessment of various aspects of electronic device 900. For example, sensor assembly 910 can detect the on / off state of electronic device 900, the relative positioning of components such as the display and keypad of electronic device 900, changes in position of electronic device 900 or a component of electronic device 900, the presence or absence of user contact with electronic device 900, orientation or acceleration / deceleration of electronic device 900, and temperature changes of electronic device 900. Sensor assembly 910 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 910 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.

[0169] In some embodiments, the sensor assembly 910 may further include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0170] Communication component 912 is configured to facilitate wired or wireless communication between electronic device 900 and other devices. Electronic device 900 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 5G NR (NewRadio), or combinations thereof. In one exemplary embodiment, communication component 912 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 912 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0171] In an exemplary embodiment, the electronic device 900 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 methods described above.

[0172] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of an electronic device 900 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0173] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the image processing method provided in this disclosure.

[0174] Embodiments of this disclosure also provide a computer program product, including a computer program that is executed by a processor using the image processing method described in the above embodiments of this disclosure.

[0175] Figure 10 This is a schematic diagram illustrating the structure of a chip 1000 for implementing the above-described image processing method according to an exemplary embodiment. (Refer to...) Figure 10 The chip 1000 includes at least one communication interface 1001 and a processor 1002. The communication interface 1001 is used to receive signals input to the chip 1000 or signals output from the chip 1000. The processor 1002 communicates with the communication interface 1001 and implements the image processing method described in the above embodiments of this disclosure through logic circuits or execution code instructions.

[0176] 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.

[0177] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. 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 at least one embodiment or example.

[0178] 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 a particular logical function or process, and the scope of the preferred embodiments of the invention 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 will be understood by those skilled in the art to which embodiments of the invention pertain.

[0179] 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 system including a processing module, 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 at least one wiring (control method), 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 portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0180] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in 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, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0181] Those skilled in the art will understand that all or part of the steps of the methods described 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.

[0182] Furthermore, the functional units in the various embodiments of the present invention 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. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0183] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An image processing method, characterized in that, The method includes: In response to the current battery level or current ambient light intensity meeting a preset condition, the image to be processed is segmented and mapped based on the current battery level or current ambient light intensity to obtain the target image.

2. The method according to claim 1, characterized in that, The preset conditions include: The current battery level is greater than the preset battery level; The current ambient light intensity is greater than the preset ambient light intensity.

3. The method according to claim 1, characterized in that, The step of performing segmented mapping processing on the image to be processed based on the current battery level or the current ambient light intensity includes: Determine the target segmentation gradient based on the current battery level or the current ambient light intensity; Based on the target segmentation gradient, the image to be processed is segmented to obtain multiple segmented images; Based on image feature information, the image parameters of the first segment image among the multiple segmented images are adjusted.

4. The method according to claim 3, characterized in that, Determining the target slicing gradient based on the current battery level or the current ambient light intensity includes: Determine the gradient difference based on the current battery level or the current ambient light intensity; The target piecewise gradient is determined based on the gradient difference and the initial piecewise gradient.

5. The method according to claim 3, characterized in that, The step of adjusting the image parameters of the first segment image among the plurality of segmented images based on image feature information includes: Based on the first image feature information of the first segmented image, the segmented sampling frequency is determined; Based on the segmented sampling frequency, the first segmented image is subjected to brightness sampling processing to obtain the brightness statistics of the first segment; Based on the first slice brightness statistics, the first target image corresponding to the first slice image is determined through the first processing.

6. The method according to claim 5, characterized in that, The step of determining the first target image corresponding to the first segmented image through a first process based on the first segmented brightness statistics includes: The brightness statistics data of the first segment are subjected to equalization processing to obtain the first contrast mapping data; Based on a preset mode, the first contrast mapping data is subjected to tone mapping synthesis processing to obtain the first brightness adjustment data; The first brightness adjustment data is smoothed to obtain the first brightness mapping data corresponding to the first segmented image; Based on the first brightness mapping data, the first segmented image is mapped to obtain the first target image.

7. The method according to claim 1, characterized in that, The method further includes: In response to the current battery level and the current ambient light intensity not meeting the preset conditions, the image to be processed is subjected to global mapping processing based on the current battery level or the current ambient light intensity to obtain the target image.

8. The method according to claim 7, characterized in that, The global mapping process of the image to be processed based on the current battery level or the current ambient light intensity includes: Based on the current battery level or the current ambient light intensity, the global sampling frequency is determined to obtain global brightness statistics. Based on the global brightness statistics, the image to be processed undergoes a first processing step.

9. An image processing apparatus, characterized in that, The image processing device includes: The processing module is used to respond to the current power level or the current ambient light intensity meeting a preset condition, and to perform segmented mapping processing on the image to be processed based on the current power level or the current ambient light intensity to obtain the target image.

10. The image processing apparatus according to claim 9, characterized in that, The preset conditions include: The current battery level is greater than the preset battery level; The current ambient light intensity is greater than the preset ambient light intensity.

11. The image processing apparatus according to claim 9, characterized in that, The processing module is used for: Determine the target segmentation gradient based on the current battery level or the current ambient light intensity; Based on the target segmentation gradient, the image to be processed is segmented to obtain multiple segmented images; Based on image feature information, the image parameters of the first segment image among the multiple segmented images are adjusted.

12. The image processing apparatus according to claim 11, characterized in that, The processing module is used for: Determine the gradient difference based on the current battery level or the current ambient light intensity; The target piecewise gradient is determined based on the gradient difference and the initial piecewise gradient.

13. The image processing apparatus according to claim 11, characterized in that, The processing module is used for: Based on the first image feature information of the first segmented image, the segmented sampling frequency is determined; Based on the segmented sampling frequency, the first segmented image is subjected to brightness sampling processing to obtain the brightness statistics of the first segment; Based on the first slice brightness statistics, the first target image corresponding to the first slice image is determined through the first processing.

14. The image processing apparatus according to claim 9, characterized in that, The processing module is used for: In response to the current battery level and the current ambient light intensity not meeting the preset conditions, the image to be processed is subjected to global mapping processing based on the current battery level or the current ambient light intensity to obtain the target image.

15. An electronic device, characterized in that, include: A processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, performs the method of any one of claims 1-8, or includes an image processing apparatus as described in any one of claims 9-14.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-8.

17. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-8.

18. A chip, characterized in that, It includes at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method as described in any one of claims 1-8 through logic circuits or executing code instructions.

Images