Image Processing Method and System Based on High-Definition Display Chip

Abstract

The present invention provides an image processing method and system based on a high-definition display chip, which relates to the technical field of image processing. The present invention significantly improves the image quality based on the high-definition display chip. The core lies in the combination of multi-scale analysis technology and intelligent dynamic adjustment mechanism, which can not only precisely enhance image details, improve the authenticity and accuracy of color reproduction, but also effectively suppress noise and avoid oversaturation phenomenon, thereby providing a more natural and harmonious visual effect. The method of determining the fusion weight by multi-scale analysis and combining image complexity ensures that the extracted reflection component can more accurately capture the detail information in the image, which helps to highlight the fine texture and edges, laying a good foundation for subsequent image quality enhancement, thus enabling the improvement of the overall clarity and fineness of the image, and finally realizing the effective enhancement of the image and improving the overall quality of the display.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image processing method and system based on a high-definition display chip. Background Art

[0002] With the rapid development and progress of the times, and the popularization of high-definition display devices, the requirements for image display quality are getting higher and higher. People's requirements for high-definition video images are also increasing. However, due to the limitations of the times, some existing video images, due to the limitations of their production years, cannot meet the requirements of existing high-definition display devices in terms of image quality. When playing, the image quality is not high, affecting the visual experience. Traditional display device manufacturers will perform image enhancement processing on the image during playback through hardware. These image enhancement methods mainly include adjusting image parameters such as chrominance, contrast, and brightness of the image, and enhancing the display effect of the image through techniques such as edge sharpening. Although these methods can improve the image quality to a certain extent, they often have limitations. They do not pay attention to the influence of the ambient light intensity and ambient color temperature in the environment where the image is produced, nor do they deeply improve the image quality in the depth features of the image, resulting in some problems, such as the image may become oversaturated, noise may be amplified, or artifacts may occur.

[0003] Therefore, it is necessary to provide an image processing method and system based on a high-definition display chip to solve the above technical problems. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides an image processing method and system based on a high-definition display chip, achieving the beneficial effect of enhancing the display of images based on the high-definition display chip.

[0005] The present invention provides an image processing method based on a high-definition display chip. The image processing method is executed by the high-definition display chip and includes the following steps:

[0006] S1: Convert the image to be processed into a Lab image, and obtain the backlight reference value in the shooting environment of the image to be processed, where the backlight reference value is obtained based on the ambient light intensity and ambient color temperature;

[0007] S2: Separate the L-channel image, a-channel image, and b-channel image from the Lab image, and obtain the edge map and reflection component map of the L-channel image;

[0008] S3: Obtain an edge protection weight map based on the edge map, and combine the backlight reference value to enhance the contrast of the reflection component map to obtain a high-contrast reflection map;

[0009] S4: Calibrate the a-channel image and the b-channel image respectively based on the backlight reference value to obtain the a-channel calibrated image and the b-channel calibrated image;

[0010] S5: Replace the L-channel image, the a-channel image, and the b-channel image in the Lab image with the high-contrast reflection map, the a-channel calibrated image, and the b-channel calibrated image respectively, and perform inverse color space conversion on the calibrated Lab image to complete image enhancement.

[0011] Preferably, the calculation formula for the backlight reference value is:

[0012]

[0013] where, is the backlight reference value, is the proportionality coefficient, is the ambient light intensity, is the reference illumination intensity, is the reference color temperature, is the ambient color temperature.

[0014] Preferably, in step S2, obtaining the reflection component map includes the following steps:

[0015] Extract the reflection components of the L-channel image at multiple different scales respectively, calculate the complexity of the reflection components at multiple different scales, and obtain the local complexity maps corresponding to the reflection components at multiple different scales;

[0016] Perform histogram statistics on the local complexity map at each scale to obtain the complexity histograms corresponding to the reflection components at multiple different scales;

[0017] Based on the complexity histogram at each scale, obtain the cumulative distribution functions corresponding to the reflection components at multiple different scales;

[0018] Calculate the fusion weights corresponding to the reflection components at multiple different scales based on the cumulative distribution function at each scale;

[0019] Weightedly fuse the reflection components at multiple scales based on the fusion weights to obtain the reflection component map.

[0020] Preferably, the calculation formula for the cumulative distribution function at each scale is:

[0021]

[0022] where, is the cumulative distribution function corresponding to the reflection component at scale , is the scale the reflection component complexity value at scale The number of pixels at is the total number of complexity values.

[0023] Preferably, by selecting the complexity values at multiple preset percentile positions on the cumulative distribution function as the key complexity thresholds.

[0024] Preferably, the calculation formula of the fusion weight is:

[0025]

[0026] where is the scale The fusion weight of the reflection component at is the scale The standard deviation of the Gaussian filter at is the number of selected key complexity thresholds, when is the lowest boundary of the complexity of the cumulative distribution function, when is the highest boundary of the complexity of the cumulative distribution function, is the scale The key complexity threshold at is the value of the cumulative distribution function, is the attenuation speed control parameter.

[0027] Preferably, the acquisition formula of the edge protection weight map is:

[0028]

[0029] is the edge protection weight map, is the edge intensity value of the pixel point in the L-channel image, is the weight attenuation speed control parameter.

[0030] Preferably, the acquisition of the high-contrast reflection map includes the following steps:

[0031] Perform preliminary enhancement processing on the reflection component map to obtain a preliminary enhanced contrast reflection map. The formula for the preliminary enhancement processing is:

[0032]

[0033] where is the preliminary enhanced contrast reflection map, is the reflection component map, is the proportional coefficient for controlling the enhancement degree, is the pixel point The local average brightness value of is the global average luminance value of the reflection component map;

[0034] Combining the edge protection weight map and the backlight reference value to perform final enhancement processing on the preliminary enhanced contrast reflection map to obtain a high-contrast reflection map. The formula for the final enhancement processing is:

[0035]

[0036] where, is the high-contrast reflection map, is the reflection component map, is the preliminary enhanced contrast reflection map, is the edge protection weight map, is the backlight reference value.

[0037] Preferably, the correction formulas for the a-channel correction image and the b-channel correction image are:

[0038]

[0039]

[0040] where, is the a-channel correction image, is the b-channel correction image, is the a-channel image, is the b-channel image, and are the reference color values of the a-channel image and the b-channel image respectively, is the backlight reference value.

[0041] The present invention also provides an image processing system based on a high-definition display chip. The image processing system is deployed in the high-definition display chip and is applied to an image processing method based on a high-definition display chip, including:

[0042] A color space conversion module, configured to convert the image to be processed into a Lab image and obtain the backlight reference value in the shooting environment of the image to be processed, where the backlight reference value is obtained based on the ambient light intensity and the ambient color temperature;

[0043] An edge detection module, configured to separate the L-channel image, the a-channel image, and the b-channel image from the Lab image and obtain the edge map and the reflection component map of the L-channel image;

[0044] A contrast enhancement module, configured to obtain an edge protection weight map based on the edge map and, in combination with the backlight reference value, perform contrast enhancement on the reflection component map to obtain a high-contrast reflection map;

[0045] A chromaticity correction module, which is used to correct the a-channel image and the b-channel image respectively based on the backlight reference value to obtain the a-channel corrected image and the b-channel corrected image;

[0046] An image reconstruction module, which is used to replace the L-channel image, the a-channel image, and the b-channel image in the Lab image with the high-contrast reflection map, the a-channel corrected image, and the b-channel corrected image respectively, and perform inverse color space conversion on the corrected Lab image to complete image enhancement.

[0047] Compared with the related technologies, an image processing method and system based on a high-definition display chip provided by the present invention have the following beneficial effects:

[0048] Based on the high-definition display chip, the present invention significantly improves the image quality. The core lies in the combination of advanced multi-scale analysis technology and intelligent dynamic adjustment mechanism, which can not only accurately enhance image details, improve the authenticity and accuracy of color reproduction, but also effectively suppress noise and avoid over-saturation phenomena, thereby providing a more natural and harmonious visual effect. The method of determining the fusion weight by multi-scale analysis and combining image complexity ensures that the extracted reflection component can more accurately capture the detail information in the image, which helps to highlight fine textures and edges and lay a foundation for subsequent image quality enhancement, thus improving the overall clarity and fineness of the image. In addition, the present invention specifically optimizes the algorithm structure to support advanced display functions such as high dynamic range and wide color gamut, ensuring that it can automatically adapt and provide the optimal image display quality in any display environment. More importantly, this solution is designed to be highly compatible with existing high-definition display chip technologies. Adopting the idea of module integration, it is easy to integrate and has good scalability, laying a solid foundation for future technology upgrades. Finally, it effectively enhances the image, improves the overall quality of the display, and meets the user's demand for extreme visual enjoyment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic flowchart of an image processing method based on a high-definition display chip of the present invention;

[0050] Figure 2 It is a schematic module structure diagram of an image processing system based on a high-definition display chip of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only for explaining the present invention, rather than limiting the present invention. Additionally, it should be noted that for ease of description, only parts related to the present invention rather than all structures are shown in the drawings. Furthermore, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.

[0052] It should also be noted that for ease of description, only parts related to the present invention rather than all content are shown in the drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as being processed sequentially, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.

[0053] Embodiment 1

[0054] An image processing method based on a high-definition display chip, the image processing method is executed by the high-definition display chip. In the specific implementation process, as Figure 1 shown, it shows a schematic flowchart of an image processing method based on a high-definition display chip, including the following steps:

[0055] Step S1: Convert the image to be processed into a Lab image, and obtain the backlight reference value in the shooting environment of the image to be processed, where the backlight reference value is obtained based on the ambient light intensity and the ambient color temperature.

[0056] Specifically, the calculation formula for the backlight reference value is:

[0057]

[0058] Where is the backlight reference value, is the proportionality coefficient, is the ambient light intensity, is the reference light intensity, is the reference color temperature, is the ambient color temperature.

[0059] In the specific implementation process, the image processing method is executed by a high-definition display chip. In practice, a high-definition display chip typically includes, but is not limited to, a video decoder responsible for receiving and decoding input video signals, an image enhancement engine for adjusting image contrast and brightness, as well as noise reduction and enhancing image edge information, a color management unit for adjusting hue, saturation, and achieving a wider color gamut coverage to ensure the true reproduction of colors, and a scaling engine for adjusting the input video resolution to match the physical resolution of the display to achieve smooth image scaling without distortion. In the present invention, the high-definition display chip processes the image. First, the image is converted from the original color space to the Lab color space for better separation and processing of color and brightness. The Lab color space consists of three components, namely the L-channel component, the a-channel component, and the b-channel component. Then, based on the original information of the image, the ambient light intensity and ambient color temperature when the image to be processed was taken are obtained. Based on the ambient light intensity and ambient color temperature, a backlight reference value is obtained through the backlight reference value calculation formula for subsequent adjustment of image brightness and other parameters, where, is the reference illumination intensity, which is usually set according to the average illumination intensity of a typical working environment, is the reference color temperature, which is usually set to 6500K, close to the color temperature of natural daylight.

[0060] Step S2: Separate the L-channel image, a-channel image, and b-channel image from the Lab image and obtain the edge map and reflection component map of the L-channel image.

[0061] In the specific implementation process, to enhance the detail information in the image, edge features are extracted from the L-channel image. Through edge detection algorithms including but not limited to Canny edge detection and Sobel operator detection, an edge map is obtained. Through the multi-scale Retinex algorithm, the image to be processed is filtered and smoothed at multiple scales to extract the reflection components at multiple scales, and a reflection component map is obtained through weighted fusion.

[0062] Specifically, in step S2, obtaining the reflection component map includes the following steps:

[0063] Extract the reflection components of the L-channel image at multiple different scales respectively, calculate the complexity of the reflection components at multiple different scales, and obtain the local complexity maps corresponding to the reflection components at multiple different scales;

[0064] Perform histogram statistics on the local complexity maps at each scale to obtain the complexity histograms corresponding to the reflection components at multiple different scales;

[0065] Based on the complexity histograms at each scale, obtain the cumulative distribution functions corresponding to the reflection components at multiple different scales;

[0066] Calculate the fusion weights corresponding to the reflection components at multiple different scales based on the cumulative distribution function at each scale;

[0067] Based on the fusion weights, weightedly fuse the reflection components at multiple scales to obtain a reflection component map.

[0068] Specifically, the calculation formula for the cumulative distribution function at each scale is:

[0069]

[0070] Among them, is the cumulative distribution function corresponding to the reflection component at scale , is the number of pixels when the complexity value of the reflection component at scale is , is the total number of complexity values.

[0071] In the specific implementation process, the reflection component map is obtained by extracting the reflection components at multiple scales through the multi-scale Retinex algorithm and performing weighted fusion. First, use the multi-scale Retinex algorithm to process the L-channel image at multiple different scales to extract the reflection components at multiple scales. The selection of each scale should be determined according to actual needs. For the reflection components at each scale, calculate their local complexity, and use edge detection or gradient magnitude to evaluate the complexity of the reflection components at each scale to obtain the local complexity maps corresponding to the reflection components at multiple different scales. Next, perform histogram statistics on the local complexity maps at each scale to obtain the complexity histograms. The complexity histograms reflect the distribution of the complexity of the reflection components at each scale. Based on each complexity histogram, calculate the corresponding cumulative distribution function for subsequent weight calculation. The cumulative distribution function represents the proportion of pixels with a complexity less than or equal to a certain value. Determine the fusion weights of the reflection components at each scale based on the cumulative distribution function.

[0072] Specifically, select the complexity values at multiple preset percentile positions on the cumulative distribution function as the key complexity thresholds.

[0073] Specifically, the calculation formula for the fusion weight is:

[0074]

[0075] Among them, is the fusion weight of the reflection component at scale , is the standard deviation of the Gaussian filter at scale , is the number of selected key complexity thresholds, When is the lowest boundary of the complexity of the cumulative distribution function, When is the highest boundary of the complexity of the cumulative distribution function, is the scale The key complexity threshold under is the value of the cumulative distribution function at, is the attenuation rate control parameter.

[0076] In the specific implementation process, before determining the fusion weight of the reflection component through the cumulative distribution function, it is necessary to determine the key complexity thresholds, which can be determined according to the percentile points on the cumulative distribution function curve, such as 25%, 50%, 75% for example. These thresholds divide different complexity regions of the image. After determining the key complexity thresholds, the fusion weights of the reflection components at the corresponding scales are calculated respectively through the fusion weight calculation formula. Among them, reflects that the smaller the scale, that is, the more details, the greater its importance in the high complexity region, adjusts the weights in different complexity ranges, and the parameter controls the attenuation speed. A larger value means more inclined to give higher weights in a smaller complexity range. Exemplarily, three scales are selected through the multi-scale Retinex algorithm to obtain the reflection components at three different scales, corresponding to the standard deviations of the Gaussian filters being 、 、 , and the complexity values corresponding to the 25%, 50%, and 75% positions of the cumulative distribution function are selected as the three key complexity thresholds, denoted as 、 、 , then in the fusion weight calculation formula , the function value of the cumulative distribution function at one of the scales calculated according to the cumulative distribution function formula is 、 、 、 、 , here That is , and are the range boundaries of the cumulative distribution function respectively. Finally, the fusion weight of the reflection component at this scale is obtained through the fusion weight calculation formula. The fusion weights of the reflection components at other scales can be calculated in the same way. Finally, the obtained fusion weights are normalized and used for the fusion of the reflection components to obtain the final reflection component map.

[0077] Step S3: Obtain an edge protection weight map based on the edge map, and combine it with the backlight reference value to enhance the contrast of the reflection component map to obtain a high-contrast reflection map.

[0078] Specifically, the formula for obtaining the edge protection weight map is:

[0079]

[0080] is the edge protection weight map, is the edge intensity value of the pixel point in the L-channel image, is the weight attenuation speed control parameter.

[0081] In the specific implementation process, a smoothing filter is used to smooth the edge map to reduce the influence of noise and generate a continuous edge protection weight map. According to the formula for obtaining the edge protection weight map, the edge intensity is mapped to a value between 0 and 1, where a value close to 1 represents strong edge protection and a value close to 0 represents weak protection. The contrast of the reflection component map is adjusted in combination with the backlight reference value. A larger value will make the change of the weight map smoother, while a smaller value will cause the weight map to drop rapidly near the edge, providing a stronger edge protection effect. In general contrast enhancement tasks, a typical starting point may be 10 or adjusted appropriately according to the scale of the image. This value can be used as a basis for an initial attempt, and then fine-tuned according to the results or the optimal value can be found through experiments.

[0082] Specifically, the steps for obtaining the high-contrast reflection map include the following steps:

[0083] Perform preliminary enhancement processing on the reflection component map to obtain a preliminarily enhanced contrast reflection map. The formula for the preliminary enhancement processing is:

[0084]

[0085] where, is the preliminarily enhanced contrast reflection map, is the reflection component map, is the proportionality coefficient for controlling the enhancement degree, is the local average brightness value of the pixel point and is the global average brightness value of the reflection component map;

[0086] Perform final enhancement processing on the preliminarily enhanced contrast reflection map in combination with the edge protection weight map and the backlight reference value to obtain a high-contrast reflection map. The formula for the final enhancement processing is:

[0087]

[0088] Among them, is a high-contrast reflection map, is a reflection component map, is a preliminary enhanced contrast reflection map, is an edge protection weight map, is a backlight reference value.

[0089] In the specific implementation process, the acquisition of the high-contrast reflection map includes two detailed steps. First, the reflection component map is preliminarily enhanced through a preliminary enhancement processing formula to obtain a preliminary enhanced contrast reflection map. First, calculate the local average brightness value. For each pixel point of the reflection component map, calculate the local average brightness value within a certain range around it, which is achieved by using a filter through a convolution operation. Then, calculate the global average brightness value of the entire reflection component map, which is to provide a reference point to maintain the overall brightness consistency during the enhancement process. Finally, apply the preliminary enhancement processing formula to the difference between each pixel point and its local and global average brightness values to perform brightness adjustment, thereby achieving preliminary contrast enhancement. The final enhancement processing is to perform final enhancement processing on the preliminary enhanced contrast reflection map by combining the backlight reference value and the edge protection weight map to obtain the high-contrast reflection map. First, consider the backlight reference value in the shooting environment. The backlight reference value is calculated based on the ambient light intensity and ambient color temperature, and it reflects the ideal backlight compensation level. By applying this value to the final enhancement processing step of the reflection component map, it can better adapt to different lighting conditions and ensure the quality of the output image. At the same time, combine the previously generated edge protection weight map to make the changes in the edge region smoother. In this way, the overall contrast of the image can be improved without destroying the edge structure. Finally, relevant post-processing is also required for the high-contrast reflection map. Exemplarily, perform normalization processing on the high-contrast reflection map to ensure that the pixel values fall within a suitable range.

[0090] Step S4: Correct the a-channel image and the b-channel image respectively based on the backlight reference value to obtain the a-channel corrected image and the b-channel corrected image.

[0091] Specifically, the correction formulas for the a-channel corrected image and the b-channel corrected image are:

[0092]

[0093]

[0094] Among them, is the a-channel corrected image, is the b-channel corrected image, is the a-channel image, is the b-channel image, and are the reference color values of the a-channel image and the b-channel image respectively, is the backlight reference value.

[0095] In the specific implementation process, color correction also needs to be performed on the a-channel image and the b-channel image. Based on the backlight reference value, the a-channel image and the b-channel image are corrected. First, the reference color values of the a-channel image and the b-channel image need to be determined. Exemplarily, and By calculating the color distribution histograms of the a-channel image and the b-channel image respectively, and selecting the value of the ninetieth percentile in the color distribution histogram as the values of and Next, the provided correction formula is used to perform color correction on the a-channel image and the b-channel image respectively. After the correction is completed, in order to ensure that the corrected image is within a suitable color range, normalization processing is required, and finally the a-channel corrected image and the b-channel corrected image are obtained.

[0096] Step S5: Replace the L-channel image, the a-channel image, and the b-channel image in the Lab image with the high-contrast reflection map, the a-channel corrected image, and the b-channel corrected image respectively, and perform an inverse color space conversion on the corrected Lab image to complete image enhancement.

[0097] In the specific implementation process, the high-contrast reflection map obtained in the previous step is used to replace the L-channel image in the Lab image, and the a-channel corrected image and the b-channel corrected image obtained in the previous step are used to replace the a-channel image and the b-channel image in the Lab image respectively, obtaining a Lab image completely composed of enhanced and corrected new channels. Using a color space conversion algorithm or function, the corrected Lab image is inversely converted back to the image in the original color space, obtaining the final enhanced image, and realizing the enhancement processing of the image quality by the high-definition display chip.

[0098] The working principle of an image processing method based on a high-definition display chip provided by the present invention is as follows:

[0099] First, convert the image to be processed into the Lab color space and calculate the backlight reference value in the shooting environment. Then, extract the edge map and the reflection component map from the L channel. The reflection component map is obtained by calculating the complexity of the reflection components at multiple scales to obtain the local complexity map and the corresponding cumulative distribution function, and then calculating the multi-scale fusion weights for weighted fusion of the reflection components to obtain the final reflection component map. Subsequently, calculate the edge protection weight map based on the edge map, and enhance the reflection component map in combination with the backlight reference value to obtain the enhanced reflection component map. Then, use the backlight reference value to correct the a-channel image and the b-channel image respectively to ensure color accuracy, and obtain the a-channel corrected image and the b-channel corrected image. Finally, replace the L-channel image, the a-channel image, and the b-channel image in the Lab image with the high-contrast reflection map after contrast enhancement, the a-channel corrected image, and the b-channel corrected image respectively, and then inverse-transform the corrected Lab image back to the image in the original color space, so as to obtain a high-quality enhanced image. This method of the present invention realizes precise control of the image brightness, contrast, and color by comprehensively applying multi-scale analysis, edge protection, reflection component optimization, and color correction technologies, significantly improves the visual effect and display quality of the final image, and thus realizes the processing of images by the high-definition display chip.

[0100] Embodiment 2

[0101] An image processing system based on a high-definition display chip, the image processing system is deployed in the high-definition display chip. In the specific implementation process, as Figure 2 shown, it shows a schematic diagram of the module structure of an image processing system based on a high-definition display chip. The image processing system includes:

[0102] A color space conversion module 100, which is used to convert the image to be processed into a Lab image and obtain the backlight reference value in the shooting environment of the image to be processed. Among them, the backlight reference value is obtained based on the ambient light intensity and the ambient color temperature;

[0103] An edge detection module 200, which is used to separate the L-channel image, the a-channel image, and the b-channel image from the Lab image and obtain the edge map and the reflection component map of the L-channel image;

[0104] A contrast enhancement module 300, which is used to obtain an edge protection weight map based on the edge map and, in combination with the backlight reference value, enhance the contrast of the reflection component map to obtain a high-contrast reflection map;

[0105] A chromaticity correction module 400, which is used to correct the a-channel image and the b-channel image respectively based on the backlight reference value to obtain the a-channel corrected image and the b-channel corrected image;

[0106] The image reconstruction module 500 is used to replace the L-channel image, a-channel image, and b-channel image in the Lab image with a high-contrast reflection map, an a-channel corrected image, and a b-channel corrected image respectively, and perform an inverse color space conversion on the corrected Lab image to complete image enhancement.

[0107] The working principle of an image processing system based on a high-definition display chip provided by the present invention is as follows:

[0108] First, the color space conversion module 100 converts the image to be processed into a Lab color space image to obtain a Lab image, and calculates the backlight reference value in the shooting environment, which is calculated based on the ambient light intensity and ambient color temperature; then, the edge detection module 200 separates the L-channel image, a-channel image, and b-channel image from the Lab image, and particularly extracts the edge map and reflection component map of the L-channel image; then, the contrast enhancement module 300 generates an edge protection weight map according to the edge map, and enhances the contrast of the reflection component map using the backlight reference value to obtain an enhanced high-contrast reflection map; then, the chromaticity correction module 400 corrects the a-channel image and b-channel image respectively using the backlight reference value to obtain an a-channel corrected image and a b-channel corrected image, enhancing the color performance; finally, the image reconstruction module 500 reconstructs the Lab image by replacing the L-channel image, a-channel image, and b-channel image in the Lab image, and then inversely converts the corrected Lab image back to the image in the original color space, thereby obtaining the final enhanced processed image.

[0109] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0110] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disc memories, tape memories, or any other medium that can be used to carry or store data and is computer-readable.

[0111] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the element.

Claims

1. An image processing method based on a high-definition display chip, wherein the image processing method is executed by a high-definition display chip, characterized in that: The following steps are involved: S1: converting the image to be processed into a Lab image, and obtaining a backlight reference value under the shooting environment of the image to be processed, wherein the backlight reference value is obtained based on the ambient light intensity and the ambient color temperature; S2: Separate the L channel image, the a channel image and the b channel image from the Lab image and obtain the edge map and the reflection component map of the L channel image; S3: obtaining an edge protection weight map based on the edge map, and performing contrast enhancement on the reflection component map in combination with the backlight reference value to obtain a high-contrast reflection map; S4: Correcting the a-channel image and the b-channel image respectively based on the backlight reference value to obtain an a-channel corrected image and a b-channel corrected image; S5: replacing the L channel image, the a channel image and the b channel image in the Lab image with the high contrast reflection image, the a channel correction image and the b channel correction image respectively, and performing color space inverse conversion on the corrected Lab image to complete image enhancement; The formula for obtaining the edge protection weight map is: is the edge protection weight map, is the pixel point in the L channel image The edge strength value of is the weight decay speed control parameter; The acquisition of the high contrast reflection image comprises the following steps: The reflection component image is subjected to preliminary enhancement processing to obtain a preliminary enhanced contrast reflection image. The formula for the preliminary enhancement processing is: in, To initially enhance the contrast reflectance map, is the reflection component map, To control the proportional coefficient of the enhancement degree, Pixel The local average brightness value, is the global average brightness value of the reflection component map; The preliminary enhanced contrast reflection image is subjected to final enhancement processing in combination with the edge protection weight map and the backlight reference value to obtain a high contrast reflection image. The formula for the final enhancement processing is: in, For high contrast reflectance images, is the reflection component map, To initially enhance the contrast reflectance map, is the edge protection weight map, This is the backlight reference value.

2. The image processing method based on a high-definition display chip according to claim 1, characterized in that: The calculation formula of the backlight reference value is: in, is the backlight reference value, is the proportionality coefficient, is the ambient light intensity, is the reference light intensity, is the reference color temperature, is the ambient color temperature.

3. The image processing method based on a high-definition display chip according to claim 2, characterized in that: In step S2, obtaining the reflection component map includes the following steps: Reflection components of the L channel image at multiple scales are extracted respectively, and complexity calculations are performed on the reflection components at multiple scales to obtain local complexity maps corresponding to the reflection components at multiple scales; Perform histogram statistics on the local complexity graph at each scale to obtain complexity histograms corresponding to reflection components at multiple different scales; Based on the complexity histogram at each scale, the cumulative distribution functions corresponding to the reflection components at multiple different scales are obtained; Calculate the fusion weights corresponding to the reflection components at multiple different scales based on the cumulative distribution function at each scale; The reflection components at multiple scales are weighted fused based on the fusion weights to obtain a reflection component map.

4. The image processing method based on a high-definition display chip according to claim 3, characterized in that: The calculation formula of the cumulative distribution function at each scale is: in, For scale The cumulative distribution function of the reflected component under For scale The complexity of the reflection component under The number of pixels when is the total number of complexity values.

5. The image processing method based on a high-definition display chip according to claim 4, characterized in that: The complexity values ​​at multiple preset percentile positions on the cumulative distribution function are selected as the key complexity threshold.

6. The image processing method based on a high-definition display chip according to claim 5, characterized in that: The calculation formula of the fusion weight is: in, For scale The fusion weight of the reflection component under, For scale The standard deviation of the Gaussian filter under is the number of selected key complexity thresholds, hour is the minimum complexity bound of the cumulative distribution function, hour is the maximum complexity boundary of the cumulative distribution function, It is a scale The critical complexity threshold under The cumulative distribution function value at is the decay speed control parameter.

7. The image processing method based on a high-definition display chip according to claim 6, characterized in that: The correction formulas of the a-channel corrected image and the b-channel corrected image are: in, Correct the image for channel a, is the b channel corrected image, is the a channel image, is the b channel image, and are the reference color values ​​of the a channel image and the b channel image respectively. This is the backlight reference value.

8. An image processing system based on a high-definition display chip, wherein the image processing system is deployed in the high-definition display chip, characterized in that: An image processing method based on a high-definition display chip as described in any one of claims 1 to 7, comprising: A color space conversion module is used to convert the image to be processed into a Lab image and obtain a backlight reference value under the shooting environment of the image to be processed, wherein the backlight reference value is obtained based on the ambient light intensity and the ambient color temperature; An edge detection module is used to separate an L channel image, an a channel image and a b channel image from the Lab image and obtain an edge map and a reflection component map of the L channel image; A contrast enhancement module is used to obtain an edge protection weight map based on the edge map, and to enhance the contrast of the reflection component map in combination with the backlight reference value to obtain a high-contrast reflection map; A chromaticity correction module, used to correct the a-channel image and the b-channel image respectively based on the backlight reference value to obtain an a-channel corrected image and a b-channel corrected image; The image reconstruction module is used to replace the L channel image, a channel image and b channel image in the Lab image with the high contrast reflection map, a channel correction image and b channel correction image respectively, and perform color space inverse conversion on the corrected Lab image to complete image enhancement.

Images