Image display quality detection method, device and equipment and computer storage medium

Abstract

The embodiment of the present application relates to the technical field of computer data processing, and discloses an image display quality detection method, device and equipment and a computer storage medium, the method comprising: acquiring video stream data sent by a receiving end in real time; identifying foreground and background for each of the to-be-detected video frames respectively to obtain target character pixel point color of at least one target character in each of the to-be-detected video frames and target background pixel point color corresponding to the target character; determining a detection result of the target character according to information of character color error pixel points corresponding to the target character, background color error pixel points and information of brightness error pixel points; counting the detection result corresponding to each of the to-be-detected video frames to obtain the number of error frames in the video stream data; and determining whether the video stream data is abnormal according to the number of error frames. Through the above manner, the accuracy of automobile instrument display is realized.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, specifically to an image display quality detection method, apparatus, device, and computer storage medium. Background Technology

[0002] The development of vehicle instrument panels and navigation systems has entered the era of intelligence, with more and more manufacturers using cockpit head units to drive cockpit displays. The cockpit head unit transmits display signals from the transmitter to the receiver, which then transmits the display signals to the cockpit displays for viewing.

[0003] In the process of implementing the prior art, the inventors discovered that during the operation of the cockpit host, connection failures may occur between the transmitter and receiver, and data transmission errors may occur between the receiver and the cockpit display. Furthermore, the video data transmission link may be interfered with in the large system and complex electromagnetic environment of a car. After the display signal is interfered with, it may cause problems such as black screen, distorted screen, or screen freezing on the cockpit display, resulting in potential safety hazards for vehicle driving.

[0004] Therefore, a solution is needed that can accurately check the quality of the display signals received by the cockpit displays. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention provide an image display quality detection method, apparatus, device, and computer storage medium to solve the problem that the quality of the display signal received by the cockpit display cannot be guaranteed in the prior art.

[0006] According to one aspect of the present invention, an image display quality method is provided, the method comprising:

[0007] The system acquires video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end via the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected.

[0008] The real-time transparency information corresponding to the video stream data is read from the display processing module preset in the smart cockpit;

[0009] Based on the real-time transparency information, foreground and background recognition are performed on each of the video frames to be detected to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character.

[0010] Determine the standard character pixel color and standard background pixel color of the standard character corresponding to the target character;

[0011] The color of the target character pixel is compared with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character;

[0012] The target background pixel color is compared with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character.

[0013] The detection result of the target character is determined based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels;

[0014] The number of erroneous frames in the video stream data is obtained by statistically analyzing the detection results corresponding to each of the video frames to be detected.

[0015] The presence of anomalies in the video stream data is determined based on the number of error frames.

[0016] In an alternative approach, the method further includes:

[0017] Based on the preset mapping relationship between color and brightness, the colors of the target background pixels and the standard background pixels are converted respectively to obtain the target brightness of each pixel corresponding to the target character and the standard brightness of each pixel corresponding to the standard character;

[0018] The first difference between the target brightness and the standard brightness of each pixel and a preset first threshold are compared to obtain the brightness error pixel corresponding to the target character.

[0019] In an alternative approach, the method further includes:

[0020] Calculate the second difference between the color of each pixel in the target character and the color of the pixel at the same position in the standard character;

[0021] Pixels whose second difference is greater than the second threshold are identified as character color error pixels.

[0022] In an alternative approach, the method further includes:

[0023] Calculate the third difference between the target color of each pixel in the background of the target character and the standard color of the pixel at the same position in the background of the standard character;

[0024] Pixels whose third difference is greater than the third threshold are identified as background color error pixels.

[0025] In an alternative approach, the method further includes:

[0026] Calculate the proportions of the number of character color error pixels, background color error pixels, and brightness error pixels to the total number of pixels in the target character;

[0027] The target character whose proportion of at least one of the character color error pixels, background color error pixels, and brightness error pixels is greater than a preset proportion threshold is determined as a display error character.

[0028] In an alternative approach, the method further includes:

[0029] The video frame to be detected containing the displayed error character is identified as an error frame;

[0030] Determining whether the video stream data is abnormal based on the number of error frames includes:

[0031] When the number of erroneous frames is determined to be greater than a preset frame count threshold, the video stream data is determined to be abnormal.

[0032] In an alternative approach, the method further includes:

[0033] When it is determined that there is an anomaly in the video stream data, the standard character is read from the display processing module;

[0034] The standard characters are used to cover up erroneous frames in the video stream data.

[0035] According to another aspect of the present invention, an image display quality detection apparatus is provided, comprising:

[0036] The acquisition module is used to acquire video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end through the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected;

[0037] The reading module is used to read the real-time transparency information corresponding to the video stream data from the preset display processing module in the smart cockpit;

[0038] The recognition module is used to perform foreground and background recognition on each of the video frames to be detected according to the real-time transparency information, and to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character.

[0039] The first determining module is used to determine the standard character pixel color and the standard background pixel color of the standard character corresponding to the target character;

[0040] The first comparison module is used to compare the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character;

[0041] The second comparison module is used to compare the color of the target background pixel with the color of the standard background pixel to obtain information on the background color error pixels and the brightness error pixels corresponding to the target character.

[0042] The second determining module is used to determine the detection result of the target character based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels;

[0043] The statistics module is used to count the detection results corresponding to each of the video frames to be detected, and to obtain the number of error frames in the video stream data;

[0044] The third determining module is used to determine whether there is an anomaly in the video stream data based on the number of error frames.

[0045] According to another aspect of the present invention, an image display quality testing device is provided, comprising:

[0046] The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus.

[0047] The memory is used to store at least one executable instruction that causes the processor to perform operations as described in any of the preceding embodiments of the image display quality detection method.

[0048] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing an image display quality detection device to perform the operation of any of the preceding embodiments of the image display quality detection method.

[0049] This invention achieves real-time acquisition of video stream data sent by a receiving end; the video stream data is sent to a transmitting end via the cockpit host of the intelligent cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; real-time transparency information corresponding to the video stream data is read from a preset display processing module in the intelligent cockpit; foreground and background recognition are performed on each of the video frames to be detected based on the real-time transparency information to obtain the target character pixel color and the target background pixel color corresponding to at least one target character in each of the video frames to be detected; the standard character pixel color and standard background pixel color of the standard character corresponding to the target character are determined. The detection process involves: 1) comparing the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character; 2) comparing the color of the target background pixel with the color of the standard background pixel to obtain information about the background color error pixels and the brightness error pixels corresponding to the target character; 3) determining the detection result of the target character based on the information about the character color error pixels, the background color error pixels, and the brightness error pixels; 4) statistically analyzing the detection results for each of the video frames to be detected to obtain the number of error frames in the video stream data; and 5) determining whether the video stream data is abnormal based on the number of error frames. This invention differs from existing smart cockpits where the display quality of automotive instrument panels cannot be guaranteed. Instead, it extracts the target character and its background pixel-level color information from the video frame to be detected. Based on the pixel-level color information of a preset standard character in the smart cockpit, it calculates the difference between the target character and the standard character from three dimensions: character color, background color, and background brightness. The character color and background color characterize the color display error of the video frame to be detected, while the background brightness characterizes the visibility of the video frame. By calculating the differences in these three dimensions at the pixel, character, and frame levels, the accuracy of video stream detection is improved.

[0050] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0051] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0052] Figure 1A flowchart illustrating the image display quality detection method provided in an embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram of the image display quality detection device provided in an embodiment of the present invention is shown;

[0054] Figure 3 A schematic diagram of the image display quality testing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0055] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0056] Explanation of related terms:

[0057] A smart cockpit upgrades the interior space of a car to make the driving and riding experience more intelligent. Currently, multi-screen interaction (one chip for multiple screens is the market mainstream), intelligent voice control, vehicle connectivity, and OTA (Over-The-Air) updates are basic features of mainstream smart cockpits. This also requires powerful AI computing power to process voice and graphic interactions, and even the interaction between the vehicle's functions and the driver (marking user preferences such as temperature, ventilation, seat adjustment, and multimedia music). As the functions of smart cockpits continue to increase and improve, VR+AR solutions may well enter the smart cockpit field. The smart cockpit SOC market features rapid chip iteration, short lifecycles, and low shipment volumes, making it difficult to amortize R&D costs, resulting in a high-barrier-to-entry chip industry.

[0058] Figure 1 A flowchart of an image display quality detection method provided in an embodiment of the present invention is shown. This method is executed by a computer processing device. The computer processing device may include a mobile phone, a laptop computer, etc. Figure 1 As shown, the method includes the following steps:

[0059] Step 10: Real-time acquisition of video stream data sent by the receiving end; the video stream data is sent to the transmitting end by the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected.

[0060] The video stream data can be real-time monitoring data of the vehicle in the smart cockpit, such as vehicle speed, fuel level, gear position, coolant level, temperature, and humidity. By displaying the video stream data on the smart dashboard of the smart cockpit, the vehicle's real-time operating status is shown to the user. However, considering that the video stream data may be affected by electromagnetic loop noise during transmission from acquisition and generation on the cockpit's main unit to the transmitter and receiver, it is necessary to test the display quality of the video stream data to ensure the accuracy of the data displayed on the smart dashboard and avoid problems such as black screens, distorted images, or character distortion that could affect the user's safe driving.

[0061] The video stream data can be in the format of LVDS (Low Voltage Differential Signaling). The video stream data includes multiple video frames to be detected, and each video frame to be detected includes at least one character to be displayed. It can be understood that one character to be displayed corresponds to at least one pixel.

[0062] Step 20: Read the real-time transparency information corresponding to the video stream data from the preset display processing module in the smart cockpit.

[0063] The display processing module can be a module with OSD (On Screen Display) functionality. This module stores and updates preset display menu information on the smart dashboard in real time, including real-time transparency. Essentially, when characters are displayed on the smart dashboard, both the characters and the background have transparency. The final dashboard data display effect is achieved by overlaying the character color, character transparency, background transparency, and background color.

[0064] Step 30: Based on the real-time transparency information, perform foreground and background recognition on each of the video frames to be detected to obtain the target character pixel color and the target background pixel color corresponding to at least one target character in each of the video frames to be detected.

[0065] Specifically, the real-time transparency information is analyzed to obtain the transparency information (i.e., alpha channel value) at each pixel position in the video frame to be detected. Based on alpha matting technology, foreground and background recognition is performed on each of the video frames to be detected to obtain the target character pixel color and the corresponding target background pixel color for at least one target character in each video frame. The specific process is as follows: RGBA and ARGB are both RGB (Alpha, Red, Green, Blue) color modes with an added alpha (transparency) channel, commonly found in 32-bit bitmap (i.e., four-channel image) storage structures. PNG images are a typical four-channel image. For images, transparency means the image itself is not visible. When two image files are combined, if the transparent file is placed on top of the opaque file, the lower file is completely unobstructed and visible. When two images are combined, the image with the alpha channel is generally called the source image (foreground), and the image without the alpha channel is the background image. Therefore: The pixel display color of the combined image = source image pixel color x alpha + background image pixel color x (1-alpha)

[0066] When using alpha masking technology for image matting, it is generally assumed that the color of each pixel in a natural image is a linear combination of the foreground and background colors at that point. The linearity coefficient represents the proportion of the foreground color in the color of that pixel, and its value varies between [0, 1]. The specific relationship is shown in the following formula:

[0067] I=αF+(1-α)B(1.1)

[0068] Where I represents the color value of the point, F and B represent the foreground and background color values ​​of the point, respectively, and α is the coefficient of the linear combination, representing the transparency of the point (i.e., the alpha channel value).

[0069] For the alpha matting problem, the α value calculated by the above formula for any point on the image is not unique. Therefore, it is very important to find the most reasonable solution from countless solutions.

[0070] Therefore, the most reasonable α value can generally be determined using the color-clustering image matting algorithm. In the color-clustering algorithm, for each pixel in the image, the K-Nearest Neighbor (KNN) algorithm is used to find the most similar related pixel. A "colorball model" is used to build a model, and then the solution for α is obtained by combining it with the above formula, thereby identifying at least one target character in the video frame to be detected and the color information of the background corresponding to that target character. Step 40: Determine the standard character pixel color and the standard background pixel color of the standard character corresponding to the target character.

[0071] Considering that the characters displayed on car dashboards generally have a fixed format, such as numbers 0-9 and letters az in specific display colors, the character colors and background colors of pre-stored standard characters can be used as the benchmark for evaluating the display quality of the target characters. Specifically, preset standard character data can be obtained from the aforementioned display processing module, and this data can be parsed to obtain the standard character pixel colors and standard background pixel colors.

[0072] Step 50: Compare the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character.

[0073] Specifically, by measuring the difference between the color of each pixel in the target character and the color of the pixel in the same position in the standard character, information such as the number and position of the character color error pixels in the target character can be obtained.

[0074] Specifically, in order to improve the flexibility and accuracy of image display quality detection, pixel points with character color errors in the target character can be filtered out based on a preset threshold.

[0075] Step 50 also includes:

[0076] Step 501: Calculate the second difference between the color of each pixel in the target character and the color of the pixel at the same position in the standard character.

[0077] In this context, considering that the color of a pixel in video stream data is generally represented by the channel values ​​of the RGB three channels, the second difference can also be the channel values ​​of the color of each pixel in the target character and the pixel at the same position in the standard character in each of the RGB channels.

[0078] Optionally, in order to improve the efficiency of image display quality detection, the second difference can also be obtained by weighted summation of the differences in the channel values ​​of the colors of each pixel in the target character and the pixels at the same position in the standard character in each RGB channel. The weighted summation can be the average of the differences in the channel values ​​of each RGB channel.

[0079] Step 502: Identify the pixels whose second difference is greater than the second threshold as the character color error pixels.

[0080] Specifically, if the second difference of a pixel in any of the RGB three channels is greater than the second threshold, then the pixel is determined to be a character color error pixel.

[0081] Optionally, when the second difference is obtained by weighted summation of the differences corresponding to the three RGB channels, pixels whose second difference is greater than the second threshold are identified as character color error pixels. By setting the second threshold, the accuracy of character color error detection can be adjusted at the pixel level, improving the flexibility of image display quality detection.

[0082] Step 60: Compare the target background pixel color with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character.

[0083] In this invention, considering that the background display of the target character may also be abnormal, thus affecting the user's perception of the character, such as a large error in the background color causing changes in the display effect of the target character on the background (e.g., when the background color changes from white to blue, a yellow character displayed on a blue background will appear green instead of the normal white), and further considering the mapping relationship between color and brightness, and that the brightness of the background affects the visibility of the target character (e.g., when the background brightness deviation is too large, the visibility of the target character will obviously have a large error, causing the user to be unable to properly identify the target character), this embodiment of the invention, on the one hand, similar to the determination of character color error pixels in the aforementioned steps, determines the background color error pixels corresponding to the target character based on the difference between the target background pixel color and the standard background pixel color. On the other hand, the target background pixel color and the standard background pixel color are converted according to a preset conversion relationship between color and brightness, the brightness of the converted target background pixel is calculated, and the difference between the brightness of the target background pixel and the brightness of the target background pixel is calculated. Pixels with brightness errors are then selected based on the brightness difference.

[0084] Specifically, step 60 also includes:

[0085] Step 601: Convert the color of the target background pixel and the color of the standard background pixel according to the preset mapping relationship between color and brightness to obtain the target brightness of each pixel corresponding to the target character and the standard brightness of each pixel corresponding to the standard character.

[0086] The mapping relationship between color and brightness can be a conversion relationship between RGB channel values ​​and YUV encoded values.

[0087] For each pixel in the background area corresponding to the target character, the color value of the pixel is converted according to the above mapping relationship to obtain the target brightness of the pixel. Similarly, the color value of the pixel of the standard character in the same position is converted according to the above mapping relationship to obtain the standard brightness of the pixel.

[0088] Step 602: Compare the first difference between the target brightness and the standard brightness of each pixel with a preset first threshold to obtain the brightness error pixel corresponding to the target character.

[0089] Pixels with a first difference greater than a first threshold can be identified as brightness error pixels. By setting the first threshold, the accuracy of character background brightness error detection can be adjusted at the pixel level, thereby improving the flexibility of image display quality detection.

[0090] Correspondingly, in order to determine the deviation of the background brightness of the target character in the video frame to be detected, in this embodiment of the invention, step 60 further includes:

[0091] Step 603: Calculate the third difference between the target color of each pixel in the background of the target character and the standard color of the pixel at the same position in the background of the standard character.

[0092] The background area of ​​the target character includes the smallest rectangular area that can cover the outline of the target character. For each pixel within this rectangular area, a third difference between the target color and the standard color is calculated to indicate the deviation between the color of the pixel and the standard color.

[0093] Step 604: Pixels with a third difference greater than a third threshold are identified as background color error pixels.

[0094] The third threshold can be independent of the first and second thresholds. By setting the third threshold, the error detection accuracy of character background color can be adjusted at the pixel level, thereby improving the flexibility of image display quality detection.

[0095] Step 70: Determine the detection result of the target character based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels.

[0096] Specifically, considering that a target character comprises multiple pixels, its overall display is affected by the proportion of pixels with display errors. These errors can be at least one of the following: incorrect character color, incorrect background color, or incorrect background brightness. Understandably, when only a small number of pixels in a target character have display errors, the overall display can generally still be correctly identified. For example, if only one or two pixels in the target character "8" have color differences, the user can still recognize "8". Conversely, if a large proportion of pixels in a target character have display errors, it may be difficult for the user to identify the correct character information. For instance, if the left side of the target character "8" has color differences resulting in low visibility, the user might identify "8" as "3" or "5", or fail to recognize it correctly at all.

[0097] Therefore, in this embodiment of the invention, the detection result of the target character is determined by the proportion of the number of character color error pixels, background color error pixels, and brightness error pixels to the total number of pixels in the target character. The detection result of the target character is used to characterize whether the target character is a display error character. A display error character is a character whose display effect is so bad that the user is unlikely to be able to correctly identify the information corresponding to the character, such as a character with missing or disordered characters.

[0098] Specifically, step 70 also includes:

[0099] Step 701: Calculate the proportion of the number of character color error pixels, background color error pixels, and brightness error pixels to the total number of pixels of the target character.

[0100] Specifically, the proportions of the number of character color error pixels, background color error pixels, and brightness error pixels to the total number of pixels in the target character are calculated.

[0101] Step 702: Determine the target character whose proportion of the character color error pixels, background color error pixels, and brightness error pixels is greater than a preset proportion threshold as a display error character.

[0102] Considering that an excessively large proportion of pixels with incorrect character color, background color, or brightness will affect the semantic recognizability of the target character, target characters with at least one of these three incorrect pixel proportions exceeding a preset threshold are identified as display error characters. The preset threshold can be set to 20%.

[0103] Understandably, when the precision requirements for image display quality are low, the average percentage of character color error pixels, background color error pixels, and brightness error pixels can be determined, and target characters whose average percentage is greater than a preset percentage threshold are identified as display error characters.

[0104] Step 80: Statistically analyze the detection results corresponding to each of the video frames to be detected to obtain the number of erroneous frames in the video stream data.

[0105] Specifically, for each video frame to be detected, the detection results of each target character in the video frame are used to determine whether the video frame is an error frame. The detection results of the target characters include whether there are any pixels with display errors in the target character, that is, whether the target character is a display error character. When the number of display error characters in the video frame to be detected is greater than a preset error character count threshold, the video frame to be detected is determined to be an error frame.

[0106] Optionally, in scenarios where the data display accuracy of the car instrument panel is higher, step 80 further includes: step 801: identifying the video frame to be detected containing the displayed error character as an error frame.

[0107] Considering that even a single erroneous character displayed on a car's instrument panel can significantly impact driving safety—for example, displaying a speed of 90 instead of 80—this embodiment of the invention directly identifies video frames containing such erroneous characters as error frames, thereby ensuring a higher level of driving safety for the user.

[0108] Understandably, in other scenarios where the requirements for image display quality are relatively less stringent, a video frame to be detected that displays more than a preset threshold number of error characters can be identified as an error frame.

[0109] Step 90: Determine whether there is an anomaly in the video stream data based on the number of error frames.

[0110] The system can compare the number of error frames with a preset fourth threshold. When the number of error frames exceeds the fourth threshold, it is determined that there is an anomaly in the video stream data.

[0111] Step 901: When it is determined that the number of erroneous frames is greater than a preset frame number threshold, it is determined that the video stream data is abnormal.

[0112] The frame count threshold is the aforementioned fourth threshold. The frame count threshold can be set according to the required image display accuracy. This fourth threshold is independent of the first, second, and third thresholds. By setting the fourth threshold, the accuracy of error detection for character background color can be adjusted at the video frame level, thereby improving the flexibility of image display quality detection.

[0113] Considering that problems with video stream data can seriously affect user driving safety, it is necessary to promptly correct errors displayed on the car dashboard. Therefore, in another embodiment of the present invention, after step 90, the following is also included:

[0114] Step 910: When it is determined that there is an anomaly in the video stream data, the standard character is read from the display processing module.

[0115] In addition to storing real-time transparency files, the display processing module also stores preset standard characters. Therefore, when it is determined that there is an anomaly in the video stream data, the standard characters are read from the display processing module so that the video stream data can be overwritten subsequently.

[0116] Step 911: Overwrite the erroneous frames in the video stream data using the standard characters.

[0117] On-screen display technology is used to overlay standard characters onto the corresponding character positions in erroneous frames of video stream data. In this embodiment of the invention, on the one hand, when extracting pixel information of characters, real-time transparency information is read from the display processing module; on the other hand, when overlaying characters with display errors, standard characters can also be read from the display processing module. This enables the display processing module to be used in multiple ways, thereby improving the processing efficiency of image display quality detection, avoiding redundancy and complexity, and reducing the cost of modifying existing car dashboards.

[0118] The image display quality detection method provided in this embodiment of the invention acquires video stream data sent by a receiving end in real time; the video stream data is sent to a transmitting end via the cockpit host of the intelligent cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; real-time transparency information corresponding to the video stream data is read from a preset display processing module in the intelligent cockpit; foreground and background recognition are performed on each of the video frames to be detected according to the real-time transparency information to obtain the target character pixel color and the target background pixel color corresponding to at least one target character in each of the video frames to be detected; the standard character pixel color of the standard character corresponding to the target character is determined and... The standard background pixel color is used; the target character pixel color is compared with the standard character pixel color to obtain information on the character color error pixels corresponding to the target character; the target background pixel color is compared with the standard background pixel color to obtain information on the background color error pixels and brightness error pixels corresponding to the target character; the detection result of the target character is determined based on the information on the character color error pixels, background color error pixels, and brightness error pixels; the detection results corresponding to each of the video frames to be detected are statistically analyzed to obtain the number of error frames in the video stream data; the presence of anomalies in the video stream data is determined based on the number of error frames. This invention distinguishes itself from existing smart cockpits where the display quality of automotive instrument panels cannot be guaranteed. The image display quality detection device provided in this embodiment extracts the target character and its background pixel-level color information from the video frame to be detected. Based on the pixel-level color information of the target character and the standard character preset in the smart cockpit, it calculates the difference between the target character and the standard character from three dimensions: character color, background color, and background brightness. The character color and background color are used to characterize the color display error of the video frame to be detected, while the background brightness is used to characterize the visibility of the video frame to be detected. By calculating the differences in the above three dimensions at the pixel level, character level, and frame level, the accuracy of video stream detection is improved.

[0119] Figure 2 A schematic diagram of the image display quality detection device provided in an embodiment of the present invention is shown. Figure 2 As shown, the device 1010 includes: an acquisition module 1001, a reading module 1002, an identification module 1003, a first determination module 1004, a first comparison module 1005, a second comparison module 1006, a second determination module 1007, a statistics module 1008, and a third determination module 1009.

[0120] The acquisition module 1001 is used to acquire video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end through the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected.

[0121] The reading module 1002 is used to read the real-time transparency information corresponding to the video stream data from the preset display processing module in the smart cockpit;

[0122] The recognition module 1003 is used to perform foreground and background recognition on each of the video frames to be detected according to the real-time transparency information, and to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character.

[0123] The first determining module 1004 is used to determine the standard character pixel color and the standard background pixel color of the standard character corresponding to the target character;

[0124] The first comparison module 1005 is used to compare the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixel corresponding to the target character;

[0125] The second comparison module 1006 is used to compare the color of the target background pixel with the color of the standard background pixel to obtain information on the background color error pixels and the brightness error pixels corresponding to the target character.

[0126] The second determining module 1007 is used to determine the detection result of the target character based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels;

[0127] The statistics module 1008 is used to perform statistics on the detection results corresponding to each of the video frames to be detected, and to obtain the number of error frames in the video stream data;

[0128] The third determining module 1009 is used to determine whether there is an anomaly in the video stream data based on the number of error frames.

[0129] The operation process of the image display quality detection device provided in this embodiment of the invention is largely the same as that of the aforementioned method embodiment, and will not be repeated here.

[0130] The image display quality detection device provided in this embodiment of the invention acquires video stream data sent by a receiving end in real time; the video stream data is sent to a transmitting end via the cockpit host of the intelligent cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; real-time transparency information corresponding to the video stream data is read from a preset display processing module in the intelligent cockpit; foreground and background recognition are performed on each of the video frames to be detected according to the real-time transparency information to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character; the standard character pixel color of the standard character corresponding to the target character is determined and... The standard background pixel color is used; the target character pixel color is compared with the standard character pixel color to obtain information on the character color error pixels corresponding to the target character; the target background pixel color is compared with the standard background pixel color to obtain information on the background color error pixels and brightness error pixels corresponding to the target character; the detection result of the target character is determined based on the information on the character color error pixels, background color error pixels, and brightness error pixels; the detection results corresponding to each of the video frames to be detected are statistically analyzed to obtain the number of error frames in the video stream data; the presence of anomalies in the video stream data is determined based on the number of error frames. This invention distinguishes itself from existing smart cockpits where the display quality of automotive instrument panels cannot be guaranteed. The image display quality detection device provided in this embodiment extracts the target character and its background pixel-level color information from the video frame to be detected. Based on the pixel-level color information of the target character and the standard character preset in the smart cockpit, it calculates the difference between the target character and the standard character from three dimensions: character color, background color, and background brightness. The character color and background color are used to characterize the color display error of the video frame to be detected, while the background brightness is used to characterize the visibility of the video frame to be detected. By calculating the differences in the above three dimensions at the pixel level, character level, and frame level, the accuracy of video stream detection is improved.

[0131] Figure 3 The diagram shows a schematic of the image display quality testing device provided in an embodiment of the present invention. The specific implementation of the image display quality testing device is not limited by the specific embodiments of the present invention.

[0132] like Figure 3 As shown, the image display quality inspection device may include: a processor 1102, a communications interface 1104, a memory 1106, and a communications bus 1108.

[0133] The processor 1102, communication interface 1104, and memory 1106 communicate with each other via communication bus 1108. Communication interface 1104 is used to communicate with other network elements such as clients or other servers. The processor 1102 executes program 1110, specifically performing the relevant steps described in the embodiment of the image display quality detection method.

[0134] Specifically, program 1110 may include program code, which includes computer-executable instructions.

[0135] Processor 1102 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The image display quality inspection device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0136] Memory 1106 is used to store program 1110. Memory 1106 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0137] Specifically, program 1110 can be called by processor 1102 to cause the image display quality detection device to perform the following operations:

[0138] The system acquires video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end via the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected.

[0139] The real-time transparency information corresponding to the video stream data is read from the display processing module preset in the smart cockpit;

[0140] Based on the real-time transparency information, foreground and background recognition are performed on each of the video frames to be detected to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character.

[0141] Determine the standard character pixel color and standard background pixel color of the standard character corresponding to the target character;

[0142] The color of the target character pixel is compared with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character;

[0143] The target background pixel color is compared with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character.

[0144] The detection result of the target character is determined based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels;

[0145] The number of erroneous frames in the video stream data is obtained by statistically analyzing the detection results corresponding to each of the video frames to be detected.

[0146] The presence of anomalies in the video stream data is determined based on the number of error frames.

[0147] The operation process of the image display quality detection device provided in this embodiment of the invention is largely the same as that of the aforementioned method embodiment, and will not be repeated here.

[0148] The image display quality detection device provided in this embodiment of the invention acquires video stream data sent by a receiving end in real time; the video stream data is sent to a transmitting end via the cockpit host of the intelligent cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; real-time transparency information corresponding to the video stream data is read from a preset display processing module in the intelligent cockpit; foreground and background recognition are performed on each of the video frames to be detected according to the real-time transparency information to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character; the standard character pixel color of the standard character corresponding to the target character is determined and... The standard background pixel color is used; the target character pixel color is compared with the standard character pixel color to obtain information on the character color error pixels corresponding to the target character; the target background pixel color is compared with the standard background pixel color to obtain information on the background color error pixels and brightness error pixels corresponding to the target character; the detection result of the target character is determined based on the information on the character color error pixels, background color error pixels, and brightness error pixels; the detection results corresponding to each of the video frames to be detected are statistically analyzed to obtain the number of error frames in the video stream data; the presence of anomalies in the video stream data is determined based on the number of error frames. This invention distinguishes itself from existing smart cockpits where the display quality of automotive instrument panels cannot be guaranteed. The image display quality detection device provided in this embodiment extracts the target character and its background pixel-level color information from the video frame to be detected. Based on the pixel-level color information of the target character and the standard character preset in the smart cockpit, it calculates the difference between the target character and the standard character from three dimensions: character color, background color, and background brightness. The character color and background color are used to characterize the color display error of the video frame to be detected, while the background brightness is used to characterize the visibility of the video frame to be detected. By calculating the differences in the above three dimensions at the pixel level, character level, and frame level, the accuracy of video stream detection is improved.

[0149] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on an image display quality detection device, causes the image display quality detection device to perform the image display quality detection method described in any of the above method embodiments.

[0150] Specifically, the executable instructions can be used to cause the image display quality inspection device to perform the following operations:

[0151] The system acquires video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end via the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected.

[0152] The real-time transparency information corresponding to the video stream data is read from the display processing module preset in the smart cockpit;

[0153] Based on the real-time transparency information, foreground and background recognition are performed on each of the video frames to be detected to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character.

[0154] Determine the standard character pixel color and standard background pixel color of the standard character corresponding to the target character;

[0155] The color of the target character pixel is compared with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character;

[0156] The target background pixel color is compared with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character.

[0157] The detection result of the target character is determined based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels;

[0158] The number of erroneous frames in the video stream data is obtained by statistically analyzing the detection results corresponding to each of the video frames to be detected.

[0159] The presence of anomalies in the video stream data is determined based on the number of error frames.

[0160] The operation process of the executable instructions stored in the computer-readable storage medium provided in this embodiment of the invention is largely the same as the operation process of the aforementioned method embodiment, and will not be repeated here.

[0161] The executable instructions stored in the computer-readable storage medium provided in this embodiment of the invention acquire video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end by the cockpit host of the intelligent cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; real-time transparency information corresponding to the video stream data is read from the display processing module preset in the intelligent cockpit; foreground and background recognition are performed on each of the video frames to be detected according to the real-time transparency information to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character; the standard character pixel of the standard character corresponding to the target character is determined. The target character's pixel color is compared with the standard background pixel color to obtain information about the character color error pixels corresponding to the target character. The target background pixel color is compared with the standard background pixel color to obtain information about the background color error pixels and brightness error pixels corresponding to the target character. The detection result of the target character is determined based on the information about the character color error pixels, background color error pixels, and brightness error pixels. The detection results corresponding to each of the video frames to be detected are statistically analyzed to obtain the number of error frames in the video stream data. The presence of anomalies in the video stream data is determined based on the number of error frames. This invention distinguishes itself from existing smart cockpits where the display quality of automotive instrument panels cannot be guaranteed. The image display quality detection device provided in this embodiment extracts the target character and its background pixel-level color information from the video frame to be detected. Based on the pixel-level color information of the target character and the standard character preset in the smart cockpit, it calculates the difference between the target character and the standard character from three dimensions: character color, background color, and background brightness. The character color and background color are used to characterize the color display error of the video frame to be detected, while the background brightness is used to characterize the visibility of the video frame to be detected. By calculating the differences in the above three dimensions at the pixel level, character level, and frame level, the accuracy of video stream detection is improved.

[0162] This invention provides an image display quality detection device for performing the above-described image display quality detection method.

[0163] This invention provides a computer program that can be called by a processor to cause an image display quality detection device to execute the image display quality detection method in any of the above method embodiments.

[0164] This invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed on a computer, cause the computer to perform the image display quality detection method described in any of the above method embodiments.

[0165] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0166] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0167] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0168] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0169] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. An image display quality detection method, characterized in that, The method includes: The system acquires video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end via the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected. The real-time transparency information corresponding to the video stream data is read from the display processing module preset in the smart cockpit; Based on the real-time transparency information, foreground and background recognition are performed on each of the video frames to be detected to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character. Determine the standard character pixel color and standard background pixel color of the standard character corresponding to the target character; The color of the target character pixel is compared with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character; The target background pixel color is compared with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character. The detection result of the target character is determined based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels; The number of erroneous frames in the video stream data is obtained by statistically analyzing the detection results corresponding to each of the video frames to be detected. The presence of anomalies in the video stream data is determined based on the number of error frames.

2. The method according to claim 1, characterized in that, The step of comparing the target background pixel color with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character includes: Based on the preset mapping relationship between color and brightness, the colors of the target background pixels and the standard background pixels are converted respectively to obtain the target brightness of each pixel corresponding to the target character and the standard brightness of each pixel corresponding to the standard character; The first difference between the target brightness and the standard brightness of each pixel and a preset first threshold are compared to obtain the brightness error pixel corresponding to the target character.

3. The method according to claim 1, characterized in that, The step of comparing the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character includes: Calculate the second difference between the color of each pixel in the target character and the color of the pixel at the same position in the standard character; Pixels whose second difference is greater than the second threshold are identified as character color error pixels.

4. The method according to claim 1, characterized in that, The step of comparing the target background pixel color with the standard background pixel color to obtain information on background color error pixels and brightness error pixels corresponding to the target character includes: Calculate the third difference between the target color of each pixel in the background of the target character and the standard color of the pixel at the same position in the background of the standard character; Pixels whose third difference is greater than the third threshold are identified as background color error pixels.

5. The method according to claim 1, characterized in that, The step of determining the detection result of the target character based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels includes: Calculate the proportions of the number of character color error pixels, background color error pixels, and brightness error pixels to the total number of pixels in the target character; The target character whose proportion of at least one of the character color error pixels, background color error pixels, and brightness error pixels is greater than a preset proportion threshold is determined as a display error character.

6. The method according to claim 5, characterized in that, The step of statistically analyzing the detection results corresponding to each of the video frames to be detected to obtain the number of erroneous frames in the video stream data includes: The video frame to be detected containing the displayed error character is identified as an error frame; Determining whether the video stream data is abnormal based on the number of error frames includes: When the number of erroneous frames is determined to be greater than a preset frame count threshold, the video stream data is determined to be abnormal.

7. The method according to claim 1, characterized in that, After determining whether the video stream data is abnormal based on the number of error frames, the process includes: When it is determined that there is an anomaly in the video stream data, the standard character is read from the display processing module; The standard characters are used to cover up erroneous frames in the video stream data.

8. An image display quality detection device, characterized in that, The device includes: The acquisition module is used to acquire video stream data sent by the receiving end in real time; the video stream data is sent to the transmitting end through the cockpit host of the smart cockpit and then forwarded to the receiving end by the transmitting end; the video stream data includes multiple video frames to be detected; The reading module is used to read the real-time transparency information corresponding to the video stream data from the preset display processing module in the smart cockpit; The recognition module is used to perform foreground and background recognition on each of the video frames to be detected according to the real-time transparency information, and to obtain the target character pixel color of at least one target character in each of the video frames to be detected and the target background pixel color corresponding to the target character. The first determining module is used to determine the standard character pixel color and the standard background pixel color of the standard character corresponding to the target character; The first comparison module is used to compare the color of the target character pixel with the color of the standard character pixel to obtain information about the character color error pixels corresponding to the target character; The second comparison module is used to compare the color of the target background pixel with the color of the standard background pixel to obtain information on the background color error pixels and the brightness error pixels corresponding to the target character. The second determining module is used to determine the detection result of the target character based on the information of the character color error pixels, the background color error pixels, and the brightness error pixels; The statistics module is used to count the detection results corresponding to each of the video frames to be detected, and to obtain the number of error frames in the video stream data; The third determining module is used to determine whether there is an anomaly in the video stream data based on the number of error frames.

9. An image display quality testing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the image display quality detection method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the image display quality testing device, causes the image display quality testing device to perform the operation of the image display quality testing method as described in any one of claims 1-7.

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