Metadata detection method and device

Abstract

The invention discloses a metadata detection method and device, and relates to the technical field of multimedia. And the computing device obtains metadata of the image data comprising one or more frames of images, and processes the metadata of the image data according to the detection strategy to obtain a detection result. Wherein the detection strategy is used for indicating that the similarity of the metadata of two adjacent frames of images in the multiple frames of images is determined, and / or the geometric characteristics of the brightness mapping curve of the image data are detected, the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of image in the image data.

Description

Technical Field

[0001] The present application relates to the field of multimedia technology, and in particular to a metadata detection method and device. Background Art

[0002] Dynamic range mapping in the video field refers to the process of adapting the original video data obtained by the front end to the display device. The aforementioned process relies on the metadata in the video data, which includes the parameters for adapting the image included in the original video data to the display device. In order to ensure that the aforementioned adaptation process is correct, basic checks are often performed by detecting whether the type of metadata meets the requirements and whether the metadata meets the corresponding specifications. However, the aforementioned basic checks can only evaluate the reliability of the metadata from the format of the metadata, and when the image is mapped to the display device for display based on the metadata, the picture quality of the image in the display device cannot be guaranteed. Summary of the invention

[0003] The present application provides a metadata detection method and device to solve the problem that basic verification can only evaluate the reliability of metadata from the format of the metadata, but when the image is mapped to a display device for display based on the metadata, the picture quality of the image in the display device cannot be guaranteed.

[0004] In a first aspect, the present application provides a metadata detection method. The metadata detection method can be applied to a computer system or a computing device applied to the computer system to implement the metadata detection method. The computing device is such as a server or a terminal (encoding end). The metadata detection method includes: the computing device obtains metadata of image data including one or more frames of images, and processes the metadata of the image data according to a detection strategy to obtain a detection result. Among them, the detection strategy is used to indicate: determine the similarity of metadata of two adjacent frames of images in multiple frames of images, and / or, detect the geometric characteristics of the brightness mapping curve of the image data, and the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame image in the image data.

[0005] Compared with only checking the type and specification of metadata, in this application, the computing device detects the similarity of metadata between two adjacent frames of image data, and can determine that when the metadata difference between the two adjacent frames of image data is too large, the screen flickers when displaying the image data. That is, this application also considers the display effect factors caused by metadata, improves the depth of metadata detection, and ensures the picture quality of image data when displayed. In addition, the computing device obtains a brightness mapping curve based on metadata, and detects the geometric characteristics of the brightness mapping curve, realizing multi-dimensional detection of metadata, further improving the depth of metadata detection, and ensuring the picture quality of image data when displayed.

[0006] Exemplarily, the brightness mapping curve of the first image represents: the corresponding relationship between the original brightness value of the pixel point in the first image and the display brightness value of the pixel point when the first image is displayed.

[0007] In one possible implementation, the computing device acquires metadata of the image data, including: the computing device receives a trigger operation of a user on a control component on a user interface, and then acquires metadata of the image data in response to the trigger operation of the user on the control component.

[0008] In the present application, the computing device interacts with the user through a user interface to confirm whether the user needs to perform quality detection of the metadata of the image data. While achieving visualization, it determines whether to perform quality detection of the metadata based on user needs, thereby improving processing freedom.

[0009] In one possible implementation, when the detection strategy is used to indicate: determining the similarity of metadata between two adjacent frames of images in a plurality of frames of images, the computing device processes the metadata of the image data according to the detection strategy, and the content of the detection result obtained may be the following two examples.

[0010] Example 1: A computing device determines the similarity of at least one metadata of two adjacent frames of images. If the similarity of at least one metadata of two adjacent frames of images is greater than a first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is normal. If the similarity of at least one metadata of two adjacent frames of images is less than the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is abnormal. If the similarity of at least one metadata of two adjacent frames of images is equal to the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is abnormal or normal.

[0011] Example 2: The computing device determines the brightness mapping curves of the two adjacent frames of images based on the metadata of the two adjacent frames of images, and then determines the similarity of the brightness mapping curves of the two adjacent frames of images. If the similarity of the brightness mapping curves of the two adjacent frames of images is less than the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is abnormal. If the similarity of the brightness mapping curves of the two adjacent frames of images is greater than the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is normal. If the similarity of the brightness mapping curves of the two adjacent frames of images is equal to the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is normal or abnormal.

[0012] In the present application, since the brightness mapping curve indicates the correspondence between the original brightness value of the pixel in the first image and the displayed brightness value of the pixel when the first image is displayed, it shows the complete mapping relationship when the image is mapped to the display device. Therefore, the computing device detects the quality of the metadata of the image data according to the complete mapping relationship corresponding to two adjacent frames of images, and can detect whether the screen flickers when the image data is displayed, avoiding the verification of the type or specification of the metadata only, improving the depth of metadata quality detection, that is, the reliability of the metadata, and ensuring the quality of the image data when it is displayed.

[0013] In a possible implementation, when the detection strategy is used to indicate: detecting the geometric characteristics of the brightness mapping curve of the image data, the computing device processes the metadata of the image data according to the detection strategy, and the content of the detection result obtained can be the following three examples.

[0014] Example 1: The brightness mapping curve corresponding to the second image includes multiple curve segments, and the second image is any frame image in the image data. The computing device determines multiple display brightness values ​​corresponding to each original brightness value of the multiple curve segments corresponding to the second image in multiple original brightness values, and calculates the difference between the multiple display brightness values. If the difference values ​​are all less than or equal to the second threshold value, the detection result is used to indicate that the metadata of the second image is normal. If there is at least one difference value greater than the second threshold value, the detection result is used to indicate that the metadata of the second image is abnormal.

[0015] In the present application, the computing device detects the continuity of the brightness mapping curve to avoid discontinuity of the brightness mapping curve corresponding to the metadata, thereby preventing the problem of screen faults when displaying image data, thereby ensuring the quality of the screen when displaying image data.

[0016] Example 2: The brightness mapping curve corresponding to the third image includes multiple curves, and the third image is any frame image in the image data. The computing device determines the monotonicity of the multiple curves corresponding to the third image. If the monotonicity corresponding to the multiple curves is not uniform, the detection result is used to indicate that the metadata of the third image is abnormal. If the monotonicity corresponding to the multiple curves is uniform, the detection result is used to indicate that the metadata of the third image is normal.

[0017] In the present application, the computing device detects the monotonicity of the brightness mapping curve to avoid the problem of brightness flipping, that is, when the original brightness value is too large, the corresponding display brightness value is too small, or when the original brightness value is too small, the corresponding display brightness value is too large, thereby ensuring the picture quality when displaying image data.

[0018] Example 3: The computing device determines the target original brightness value corresponding to the target display brightness value in the brightness mapping curve corresponding to the fourth image, and calculates the ratio of the number of pixels in the fourth image that is greater than or less than the target original brightness value to the total number of pixels in the fourth image. The fourth image is any frame image in the image data. If the ratio is greater than the third threshold, the detection result is used to indicate that the metadata of the fourth image is abnormal. If the ratio is less than the third threshold, the detection result is used to indicate that the metadata of the fourth image is normal. If the ratio is equal to the third threshold, the detection result is used to indicate that the metadata of the fourth image is normal or abnormal.

[0019] In the present application, the computing device detects the rationality of the brightness mapping curve (brightness rationality), that is, detects whether there is an original brightness value in the brightness mapping curve that does not correspond to the displayed brightness value, thereby avoiding the problem of missing brightness of some pixels (i.e., not displayed) when displaying image data, and ensures the quality of the picture when displaying image data.

[0020] In a possible implementation, the above-mentioned multi-frame images are images between two scene switching frames, and the scene switching frame is a subsequent frame image in the adjacent multi-frame images whose metadata similarity is less than or equal to the fourth threshold. That is, when the computing device performs the above-mentioned quality detection, it only detects the metadata of the multi-frame images between the two scene switching frames, which reduces the amount of data calculation and improves data efficiency.

[0021] In a possible implementation, the metadata detection method further includes: if the one or more frames of images with abnormal metadata indicated by the detection result are scene switching frames, updating the one or more frames of images with abnormal metadata indicated by the detection result to have normal metadata. The scene switching frame is a subsequent frame of the adjacent multiple frames of images whose metadata similarity is less than or equal to a fourth threshold.

[0022] In this application, since scene switching may occur in image data, sudden changes in brightness and other parameters during scene switching are considered normal, that is, when a frame of image is a scene switching frame, the abnormal metadata of the scene switching frame is allowed, that is, normal. The computing device sets the detection result corresponding to the scene switching frame to normal, which complies with the scene switching law.

[0023] With respect to the above-mentioned scene switching frame, two examples of determining the scene switching frame are provided below.

[0024] Example 1: A computing device obtains a scene switching identifier included in metadata of a fifth image, where the scene switching identifier is used to indicate that the fifth image is a scene switching frame, and the fifth image is any frame image in the image data.

[0025] Example 2: The computing device determines the similarity of the attributes of the pixels in the sixth image and the seventh image, and when the similarity of the attributes of the pixels in the sixth image and the seventh image is less than or equal to the fifth threshold, calculates the similarity between the metadata of the sixth image and the metadata of the seventh image, and further determines that the sixth image is a scene switching frame. The sixth image and the seventh image are in the same sliding window, the seventh image is one or more frames of images adjacent to the sixth image, and the sixth image and the seventh image are any frame of images in the image data. The similarity between the metadata of the sixth image and the metadata of the seventh image is less than or equal to the sixth threshold, and the above attributes include: one or more of brightness value or brightness-chrominance YUV.

[0026] In the present application, the computing device determines the scene switching frame from multiple frames of images based on the similarity of attributes and the similarity of metadata, and then when determining abnormal metadata, the scene switching frame can be referred to. That is, when the monotonicity of the brightness mapping curve corresponding to the metadata of the scene switching frame is not uniform, the continuity is inconsistent, etc., the metadata of the scene switching frame is also normal, thereby improving the accuracy of determining abnormal metadata.

[0027] In a possible implementation, the metadata detection method further includes: the computing device outputting the detection result.

[0028] For example, the computing device outputs the detection result to the display device.

[0029] In a possible implementation, the metadata detection method further includes: when the detection result indicates that the metadata of one or more frames of images are abnormal, the computing device updates the metadata of the one or more frames of images with abnormal metadata.

[0030] In the present application, the computing device ensures the quality of the image data displayed by the display device by updating the metadata of one or more frames of images with abnormal metadata.

[0031] In a second aspect, the present application provides a metadata detection method. The metadata detection method can be applied to a computer system or a computing device applied to the computer system to implement the metadata detection method. The computing device is such as a server or a terminal (decoding end). The metadata detection method includes: the computing device obtains metadata of image data including one or more frames of images, and processes the metadata of the image data according to a detection strategy to obtain a detection result. Among them, the detection strategy is used to indicate: determine the similarity of metadata of two adjacent frames of images in multiple frames of images, and / or, detect the geometric characteristics of the brightness mapping curve of the image data, and the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame image in the image data.

[0032] Exemplarily, the brightness mapping curve of the first image represents: the corresponding relationship between the original brightness value of the pixel point in the first image and the display brightness value of the pixel point when the first image is displayed.

[0033] In a possible implementation, the metadata detection method further includes: if the detection result indicates that the similarity of metadata between two adjacent frames of images in multiple frames is less than or equal to a first threshold, and / or the geometric characteristic detection of the brightness mapping curve of at least one frame of image in the image data is abnormal, an alarm is issued according to the detection result.

[0034] In a possible implementation, the metadata detection method further includes: when the detection result of the computing device indicates that the metadata of one or more frames of images is abnormal, the one or more frames of images are mapped according to preset metadata, and the one or more frames of images include images with abnormal metadata.

[0035] In the present application, when the metadata of one or more frames of an image are abnormal, the computing device uses preset metadata to map the image data to the display device to ensure the quality of the image data when the display device displays it.

[0036] For other possible implementations of the second aspect, reference may be made to any possible implementation of the first aspect described above, and detailed description will not be given here.

[0037] In a third aspect, the present application provides a metadata detection device, which is applied to a computer system or a computing device that supports the computer system to implement a metadata detection method, and the metadata detection device includes various modules for executing the metadata detection method in the first aspect or any optional implementation of the first aspect. For example, the metadata detection device includes: a first acquisition module and a first processing module. Among them,

[0038] The first acquisition module is used to acquire metadata of image data, wherein the image data includes one or more frames of images.

[0039] The first processing module is used to process the metadata of the image data according to the detection strategy to obtain a detection result. The detection strategy is used to indicate: determining the similarity of the metadata of two adjacent frames of images in the multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data. The brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of the image data.

[0040] For more detailed implementation content of the metadata detection device, please refer to the description of any implementation method in the first aspect above, as well as the content of the following specific implementation methods, which will not be repeated here.

[0041] In a fourth aspect, the present application provides a metadata detection device, which is applied to a computer system or a computing device that supports the computer system to implement a metadata detection method, and the metadata detection device includes various modules for executing the metadata detection method in the second aspect or any optional implementation of the second aspect. For example, the metadata detection device includes: a second acquisition module and a second processing module. Among them,

[0042] The second acquisition module is used to acquire metadata of image data, wherein the image data includes one or more frames of images.

[0043] The second processing module is used to process the metadata of the image data according to the detection strategy to obtain a detection result. The detection strategy is used to indicate: determining the similarity of the metadata of two adjacent frames of images in the multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data, the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of the image data.

[0044] For more detailed implementation content of the metadata detection device, please refer to the description of any implementation method in the second aspect above, as well as the content of the following specific implementation methods, which will not be repeated here.

[0045] In a fifth aspect, the present application provides a chip comprising: a processor and a power supply circuit; the power supply circuit is used to power the processor, and the processor is used to execute the method in the above-mentioned first aspect or any possible implementation of the first aspect; and / or, the processor is used to execute the method in the above-mentioned second aspect or any possible implementation of the second aspect.

[0046] In a sixth aspect, the present application provides a decoder, comprising a memory and a processor, wherein the memory is used to store computer instructions; when the processor executes the computer instructions, the method in the first aspect or any possible implementation of the first aspect is implemented; and / or, when the processor executes the computer instructions, the method in the second aspect or any possible implementation of the second aspect is implemented. The decoder may be an encoder or a decoder.

[0047] In a seventh aspect, the present application provides a computer-readable storage medium storing a computer program or instruction. When the computer program or instruction is executed by a processing device, the method in the above-mentioned first aspect or any possible implementation of the first aspect is implemented; and / or, when the computer program or instruction is executed by a processing device, the method in the above-mentioned second aspect or any possible implementation of the second aspect is implemented.

[0048] In an eighth aspect, the present application provides a computer program product, which includes a computer program or instructions, which, when executed by a processing device, implements the method in the above-mentioned first aspect or any possible implementation of the first aspect; and / or, when the computer program or instructions are executed by a processing device, implements the method in the above-mentioned second aspect or any possible implementation of the second aspect.

[0049] The beneficial effects of the second to eighth aspects above can refer to the first aspect or any possible implementation of the first aspect, and will not be described in detail here. Based on the implementations provided in the above aspects, this application can also be further combined to provide more implementations. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 An application scenario diagram of a computer system provided for this application;

[0051] Figure 2 Schematic diagram of the metadata detection method provided in this application Figure 1 ;

[0052] Figure 3 A schematic diagram of the graphical user interface provided for this application;

[0053] Figure 4 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 1 ;

[0054] Figure 5 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 2 ;

[0055] Figure 6 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 3 ;

[0056] Figure 7 A schematic diagram of a flow chart of a method for determining a scene switching frame provided in the present application;

[0057] Figure 8 Schematic diagram of the metadata detection method provided in this application Figure 2 ;

[0058] Fig. 9 A schematic diagram of the structure of a metadata detection device provided in this application Figure 1 ;

[0059] Fig.10 A schematic diagram of the structure of a metadata detection device provided in this application Figure 2 ;

[0060] Fig.11A schematic diagram of the structure of a metadata detection device provided in this application Figure 3 ;

[0061] Fig.12 A schematic diagram of the structure of a metadata detection device provided in this application Figure 4 ;

[0062] Fig.13 A schematic diagram of the structure of a computing device provided in this application. DETAILED DESCRIPTION

[0063] To facilitate understanding, the technical terms involved in this application are first introduced.

[0064] High dynamic range (HDR) refers to images with a dynamic range between 0.001 nit and 10,000 nit, where nit is the unit of illumination.

[0065] Standard dynamic range (SDR) refers to images with a dynamic range of 1nit to 100nit.

[0066] Metadata, in this application, refers to the key feature information of each frame image in the video, such as the average brightness, maximum brightness or minimum brightness of the scene.

[0067] Static metadata refers to metadata that remains unchanged throughout the entire video or scene.

[0068] Dynamic metadata refers to metadata that changes dynamically based on the scene or each frame.

[0069] Tone mapping (TM) refers to the tone mapping technology between dynamic ranges, that is, the adjustment method between different dynamic ranges.

[0070] The following is an example of the process of adapting the original video obtained by the video production end to the display device. The original HDR video collected or produced at the video production end has a maximum brightness of 4000nit, while the SDR display capability of the display device is only 100nit. Therefore, the 4000nit original video needs to be mapped to 100nit to be displayed on the display device.

[0071] The above mapping method may include static mapping or dynamic mapping. Static mapping refers to the overall mapping based on the same video or image content by a single static metadata. Dynamic mapping is to use different data to map each scene or frame based on the characteristics of each frame image in the video, that is, to use dynamic metadata for mapping.

[0072] The advantage of the static mapping is that it carries less information and has a simple processing flow. The priority of the dynamic mapping is that in extremely dark or extremely bright scenes, the details of the scene in the video are still preserved and the display effect is better.

[0073] The above mapping process relies on metadata containing key information or features in the video. The mapping curve obtained based on the metadata is used to adapt the original video to the display device. The mapping curve is used to represent the correspondence between the original brightness of a pixel in an image in the video and the displayed brightness of the pixel when the image is displayed.

[0074] To ensure that the display device can display the original video content well, the metadata of the original video is often detected. Two possible detection methods are provided below.

[0075] Detection method 1: after encoding the video to obtain a bitstream, detect whether the metadata of the video is correctly encapsulated in the bitstream.

[0076] Since the encapsulation position of metadata in the bitstream (media file) has corresponding specifications (such as supplemental enhancement information (SEI)), this solution can parse the bitstream and confirm whether the corresponding metadata can be found according to the identifier of the metadata.

[0077] The above solution can only confirm whether the metadata is correctly encapsulated, but does not detect the quality of the metadata itself.

[0078] Detection method 2: After encoding the video to obtain the bitstream, the type of metadata in the bitstream can be detected to determine whether the type of metadata meets the preset requirements. Also, whether the parameters corresponding to the metadata meet the set specification definition and threshold range. For example, Boolean metadata / fixed-point metadata, etc. can be judged.

[0079] When judging Boolean metadata, it can be determined whether the Boolean metadata has only two values: true / false. When judging fixed-point metadata, it can be determined whether the fixed-point metadata overflows the bit value range. The value range of Nbit non-negative fixed-point metadata is 0 to 2. N -1.

[0080] The above detection method 2 only performs basic verification of the metadata, that is, it only determines whether different types of metadata meet the specification requirements, and does not evaluate the quality of the metadata, resulting in an inability to determine the quality of the original video when it is mapped to the display device according to the metadata, resulting in unstable display quality.

[0081] In summary, the above detection method can only evaluate the reliability of metadata from the format of metadata. When the original video data (one or more frames of images) is mapped to a display device for display based on the metadata, the picture quality of the image in the display device cannot be guaranteed.

[0082] Based on this, the present application provides a metadata detection method, which can be applied to a computing device, which can be an encoding end or a decoding end. The metadata detection method includes: the computing device obtains metadata of image data including one or more frames of images, and processes the metadata of the image data according to a detection strategy to obtain a detection result. The detection strategy is used to indicate: determining the similarity of metadata of two adjacent frames of images in multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data, and the brightness mapping curve of the first image is obtained based on the metadata of the first image. The first image is any frame in the image data.

[0083] Compared with only checking the type and specification of metadata, in this application, the computing device detects the similarity of metadata between two adjacent frames of image data, and can determine that when the metadata difference between the two adjacent frames of image data is too large, the screen flickers when displaying the image data. That is, this application also considers the display effect factors caused by metadata, improves the depth of metadata detection, and ensures the picture quality of image data when displayed. In addition, the computing device obtains a brightness mapping curve based on metadata, and detects the geometric characteristics of the brightness mapping curve, realizing multi-dimensional detection of metadata, further improving the depth of metadata detection, and ensuring the picture quality of image data when displayed.

[0084] Exemplarily, the brightness mapping curve of the first image represents: the corresponding relationship between the original brightness value of the pixel point in the first image and the display brightness value of the pixel point when the first image is displayed.

[0085] When the computing device is an encoding end, the computing device may also output a detection result. When the computing device is a decoding end, the computing device may issue an alarm according to the detection result.

[0086] The metadata detection method provided in this application can be applied to Figure 1 The computer system shown in FIG. Figure 1 As shown, Figure 1 An application scenario diagram of a computer system provided for this application.

[0087] The computer system includes an encoding end 100 and a decoding end 200. The encoding end 100 generates an encoded video (or a code stream). Therefore, the encoding end 100 can be called a video encoding device. The decoding end 200 can decode the code stream (such as image data including one or more frames of images) generated by the encoding end 100. Therefore, the decoding end 200 can be called a video decoding device. Various embodiments of the encoding end 100, the decoding end 200, or both may include one or more processors and a memory coupled to the processor or the one or more processors. The memory may include, but is not limited to, a random access memory (RAM), a flash memory, a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, or any other medium that can be used to store the desired program code in the form of instructions or data structures that can be accessed by a computer.

[0088] exist Figure 1 In the example of , the encoding end 100 includes a source video 110, a video encoder 120, and an output interface 130. In some examples, the output interface 130 may include a modulator / demodulator (modem) and / or a transmitter. The source video 110 may be obtained by a video capture device (e.g., a camera), a video archive containing previously captured video, a video feed interface for receiving video from a video content provider, and / or a computer graphics system for generating video, such as an image rendering engine, or a combination of the above video sources.

[0089] Exemplarily, the encoding end 100 may be deployed with (davinciresolve) tool, color grading system (baselight) tool.

[0090] The video encoder 120 can encode the video (multiple source images) from the source video 110. In some examples, the encoding end 100 transmits the code stream directly to the decoding end 200 via the link 300 through the output interface 130. In other examples, the code stream can also be stored in the storage device 400 for later access by the decoding end 200 for decoding and / or playback. The code stream contains metadata of the image data.

[0091] The metadata of a frame of image may include: the maximum brightness value, the minimum brightness value and the average brightness value corresponding to the frame of image, etc. The content included in the above metadata is only an example. In other embodiments of the present application, the metadata of a frame of image may also include: parameters of several specified functions (such as exponential function, logarithmic function, quadratic function, cubic spline function, etc.), and the specified function and the parameters of the specified function are used to map the video to a display device (such as display device 210) for display.

[0092] exist Figure 1 In the example of , the decoding end 200 includes an input interface 230, a video decoder 220 and a display device 210. In some examples, the input interface 230 includes a receiver and / or a modem. The input interface 230 can receive the encoded video via the link 300 and / or from the storage device 400. The code stream received by the video decoder 220 is decoded to obtain a video (a plurality of decoded images). The display device 210 can be integrated with the decoding end 200 or can be outside the decoding end 200. In general, the display device 210 displays the decoded video. The display device 210 may include a variety of display devices, for example, a liquid crystal display (LCD), a plasma display, an organic light-emitting diode (OLED) display or other types of display devices.

[0093] Exemplarily, after the encoding end 100 acquires the video, metadata corresponding to each frame image in the video may be generated, and then when the video encoder 120 in the encoding end 100 encodes the video, the metadata is further encoded to obtain a bitstream.

[0094] Furthermore, after the decoding end 200 obtains the bitstream, it processes the metadata corresponding to the image data carried in the bitstream according to the detection strategy to obtain a detection result.

[0095] On the one hand, the present application provides a metadata detection method, which can be applied to Figure 1 The computer system shown. Figure 2 Schematic diagram of the metadata detection method provided in this application Figure 1 The metadata detection method may be executed by a first computing device 310, and the first computing device 310 may be Figure 1 The encoding end 100 in the first computing device 310 is deployed with a metadata editing tool (such as or baselight, etc.). Figure 2 As shown, the metadata detection method may include the following steps S210 and S220.

[0096] S210 . The first computing device 310 obtains metadata of the image data.

[0097] The image data includes one or more frames of images. When the image data includes multiple frames of images, the image data can be called a video.

[0098] In a possible implementation, the first computing device 310 acquires metadata of image data, including: the first computing device 310 generates metadata of each frame image in the video according to the acquired video.

[0099] For example, the first computing device 310 may obtain the video from the memory, or the video may be obtained by a video capture device (such as a camera) built into or connected to the first computing device 310. The aforementioned method of obtaining the video is only an example of the present application. In other embodiments of the present application, the video rendered by the image rendering engine may also be obtained.

[0100] In a possible example, the first computing device 310 generates metadata of each frame image in the video according to the acquired video, including: the first computing device 310 may generate metadata of each frame image in the video according to the deployed The tool generates metadata for the video, and then obtains metadata corresponding to each frame image in the video.

[0101] Exemplarily, the first computing device 310 imports the video into tools and in Define the type of metadata to be generated in the tool, and then output the metadata of the video. Or, The tool outputs the edited video along with the video's metadata.

[0102] In another possible example, the first computing device 310 generates metadata for each frame image in the video based on the acquired video, including: the first computing device 310 can generate metadata for the video based on the baselight tool deployed thereon, and then obtain metadata for each frame image in the video.

[0103] Regarding the content of metadata of the image data obtained by the first computing device 310, a possible embodiment is provided below. Figure 3 As shown, Figure 3 This is a schematic diagram of the graphical user interface provided by this application. Figure 2 S210 in the embodiment may include the following steps S310 and S320.

[0104] S310: The first computing device 310 receives a trigger operation of a user on a control component on a user interface.

[0105] The user interface includes controls for metadata detection.

[0106] like Figure 3As shown, the metadata detection control component in the user interface can be in various forms, such as a square or a switch shape, etc., which is not limited in this application. The control component is used to indicate whether to generate metadata for each frame image in the video.

[0107] In a possible example, the first computing device 310 may display a user interface on the front end, where the front end may refer to a display connected to the first computing device 310, or a display screen provided by the first computing device 310, etc., which is not limited in this application.

[0108] In one possible implementation, the first computing device 310 receives a user's trigger operation on a control component, including: the first computing device 310 obtains the user's trigger operation on the control component on a user interface through various input devices (keyboard, mouse, touch screen, etc.).

[0109] Regarding the specific implementation of the trigger operation, three possible examples are provided below.

[0110] Example 1: The trigger operation may be a user's confirmation of the control component of metadata detection through a keyboard, such as the trigger operation being a confirmation (enter) key triggered by the user.

[0111] Example 2: The trigger operation may be a user clicking a control component of metadata detection using a mouse.

[0112] Example 3: The trigger operation may be a user clicking or sliding a control component for metadata detection on a touch screen.

[0113] The above examples are only optional implementations provided in this embodiment and should not be understood as limiting the present application. In other embodiments of the present application, the trigger operation may also be airborne operation or voice control.

[0114] S320: The first computing device 310 obtains metadata of the image data in response to a triggering operation of the control component by the user.

[0115] The first computing device 310 determines metadata of each frame image in the generated video in response to the user's triggering operation on the control component through various input devices, and performs Figure 2 The metadata detection method shown.

[0116] For the content of metadata generated by the first computing device 310 for each frame image in the video, reference may be made to the description of S210 above, which will not be elaborated here.

[0117] In the present application, the first computing device 310 interacts with the user through a user interface to confirm whether the user needs to perform metadata quality detection. While achieving visualization, it determines whether to perform metadata quality detection based on user needs, thereby improving processing freedom.

[0118] Please continue to see Figure 2 The metadata detection method provided in this embodiment also includes step S220.

[0119] S220. The first computing device 310 processes the metadata of the image data according to the detection strategy to obtain a detection result.

[0120] In a possible embodiment, the above detection strategy is used to indicate that: the first computing device 310 determines the similarity of metadata between two adjacent frames of images in the multiple frames of images.

[0121] When the metadata similarity between two adjacent frames of images is greater than a first threshold (the first threshold may also be referred to as threshold 1, which may be 0.85), the first computing device 310 determines that the detection results of the two adjacent frames of images are normal. When the metadata similarity between two adjacent frames of images is less than threshold 1, the first computing device 310 determines that the detection results of the two adjacent frames of images are abnormal. When the metadata similarity between two adjacent frames of images is equal to threshold 1, the first computing device 310 may determine whether the detection results of the two adjacent frames of images are normal or abnormal according to user needs, which is not limited here.

[0122] The similarity of metadata of two adjacent frames of images may be the similarity of at least one metadata of the two adjacent frames of images, or the similarity of brightness mapping curves corresponding to the two adjacent frames of images. The brightness mapping curve of the first image is obtained according to the metadata of the first image, and the brightness mapping curve is used to indicate the correspondence between the original brightness value of the pixel point in the first image and the displayed brightness value of the pixel point when the first image is displayed. The device displaying the first image is a display device, and the display device may be Figure 1 The first image is any frame image in the image data.

[0123] Exemplarily, the original brightness value represents the original brightness of the pixel points of each frame image in the video when the first computing device 310 acquires the video. The display brightness value represents the brightness of the pixel points displayed by the display device when the video is mapped to the display device for display.

[0124] In a possible implementation, the first computing device 310 calculates the similarity of at least one metadata of two adjacent image frames.

[0125] The first computing device 310 may calculate the similarity by calculating the distance between at least one metadata in two adjacent image frames. For example, the first computing device 310 uses Euclidean distance, Manhattan distance, Chebyshev distance, etc. to calculate the distance between at least one metadata in two adjacent image frames.

[0126] If the distance between at least one metadata in two adjacent frames of images is greater than the preset distance, the detection results of the two adjacent frames of images are determined to be abnormal; if the distance between at least one metadata in two adjacent frames of images is less than the preset distance, the detection results of the two adjacent frames of images are determined to be normal. If the distance between at least one metadata in two adjacent frames of images is equal to the preset distance, the detection results of the two adjacent frames of images are determined to be normal or abnormal according to user needs.

[0127] Exemplarily, the at least one metadata includes: a maximum brightness value and a minimum brightness value, the maximum brightness value and the minimum brightness value of the first frame image in the two adjacent frames of images are 4000 and 500 respectively, and the maximum brightness value and the minimum brightness value of the second frame image in the two adjacent frames of images are 4200 and 520 respectively. The first computing device 310 determines the similarity of the metadata of the two adjacent frames of images according to the maximum brightness value and the minimum brightness value of the two adjacent frames of images, that is, determines the distance between (4000, 500) and (4200, 520).

[0128] In another possible implementation manner, the first computing device 310 calculates the similarity of brightness mapping curves determined according to metadata of two adjacent image frames.

[0129] The first calculation device 310 may use Fréchet distance, dynamic time warping (DTW), etc. to determine the similarity of brightness mapping curves corresponding to two adjacent frames of images.

[0130] The Fréchet distance is defined as the maximum value of the following process: select a point on one curve and a point on the other curve, and then connect the two points to form a connecting line segment. The position and length of the connecting line segment are restricted by the order of the points on the two curves. The Fréchet distance is the maximum value that makes the connecting line segment of this process the shortest. The smaller the Fréchet distance, the more similar the two curves are.

[0131] The basic idea of ​​DTW is to align two time series (curves) to minimize the distance between them while satisfying a strict monotonicity and smoothness restriction. Specifically, DTW calculates the distance matrix between the two sequences and uses dynamic programming to find the optimal path, that is, to provide a measure of the similarity between the two sequences.

[0132] In one possible scenario, when the similarity of the brightness mapping curves of two adjacent frames of images is less than a threshold value 1, the first computing device 310 confirms that the metadata of one of the two adjacent frames of images is abnormal. In other words, the detection result indicates that the metadata of one of the two adjacent frames of images is abnormal. When the similarity of the brightness mapping curves of the two adjacent frames of images is greater than a threshold value 1, the first computing device 310 confirms that the metadata of the two adjacent frames of images is normal. In other words, the detection result indicates that the metadata of the two adjacent frames of images is normal.

[0133] When the similarity of the brightness mapping curves of two adjacent image frames is equal to the threshold 1, the first computing device 310 confirms that the metadata of the two adjacent image frames are normal or abnormal, which is not limited in this application.

[0134] In a possible example, one of the two adjacent image frames is used to indicate: the latter image of the two adjacent image frames.

[0135] In the present application, since the brightness mapping curve indicates the correspondence between the original brightness value of the pixel in the first image and the displayed brightness value of the pixel when the first image is displayed, it shows the complete mapping relationship when the image is mapped to the display device. Therefore, the first computing device 310 detects the quality of the metadata according to the complete mapping relationship corresponding to the two adjacent frames of images, and can detect whether the display device will have a screen flickering problem when displaying the two adjacent frames of images, avoiding the verification of the type or specification of the metadata only, improving the depth of metadata quality detection, that is, the reliability of the metadata, and ensuring the quality of the video when the display device displays the video.

[0136] In a possible scenario, three examples of obtaining corresponding brightness mapping curves according to metadata of each frame image are shown below.

[0137] Example 1: The metadata only includes several brightness features of each frame image, such as the maximum brightness value, the minimum brightness value, and the average brightness, etc. The first computing device 310 uses the brightness features of each frame image as the horizontal coordinate, and the vertical coordinate corresponds to the brightness information of the display device (such as the maximum brightness value, the minimum brightness value, and the average brightness value), and uses a preset function to smoothly connect the aforementioned multiple points to obtain a brightness mapping curve.

[0138] The preset curve may be a Catmull-Rom spline curve, a Bezier curve, or the like.

[0139] Among them, the Catmull-Rom spline is a curve used for interpolation control points. It can produce smooth and natural curves, so it is widely used in computer graphics, animation and other related fields.

[0140] A Bezier curve is a mathematical curve that can describe a smooth curve. A Bezier curve describes the shape of a curve by defining a starting point (such as the minimum brightness value mentioned above), an end point (such as the maximum brightness value mentioned above), and a control point (such as the average brightness value mentioned above). The starting point and the end point are the endpoints of the curve, while the control point determines the curvature and shape of the curve.

[0141] Example 2: In addition to the brightness feature in Example 1, the metadata also includes several specified function parameters (such as exponential function, logarithmic function, quadratic function, cubic spline function, etc.), and the parameters of the specified function are used to adjust the brightness, chromaticity, etc. of the video. The first computing device 310 generates a brightness mapping curve according to the above function parameters and the corresponding function.

[0142] Exemplarily, the metadata indicates an exponential function and parameters corresponding to the exponential function, and the first computing device 310 can accurately determine the brightness mapping curve according to the exponential function and the parameters corresponding to the exponential function.

[0143] Example 3 is a combination of the above-mentioned Example 1 and Example 2, which will not be described in detail here.

[0144] The above contents are merely optional examples provided in this embodiment and should not be construed as limitations on the present application.

[0145] In a possible embodiment, the above detection strategy is further used to instruct: the first computing device 310 detects geometric characteristics of a brightness mapping curve of the image data.

[0146] In this embodiment, the first computing device 310 mainly detects at least one of continuity, monotonicity, and rationality of the geometric characteristics of the mapping curve.

[0147] The continuity refers to whether the multiple segments of the brightness mapping curve are continuous. That is, whether the difference between two adjacent segments of the curve at the same horizontal coordinate is less than the second threshold. The first computing device 310 detects the continuity of the brightness mapping curve, which can be referred to as follows Figure 4 The contents shown will not be repeated here.

[0148] In a possible example, the abscissa of the brightness mapping curve is used to indicate an original brightness value, and the ordinate of the brightness mapping curve is used to indicate a displayed brightness value.

[0149] Monotonicity refers to whether the multiple curves included in the brightness mapping curve are all increasing or decreasing. The content of the first computing device 310 detecting the monotonicity of the brightness mapping curve can be referred to as follows: Figure 5 The contents shown will not be repeated here.

[0150] Reasonableness refers to whether there is a brightness value greater than or less than the target original brightness value corresponding to the target display brightness value in the brightness mapping curve. The first computing device 310 detects the rationality of the brightness mapping curve, which can be referred to as follows: Figure 6 The contents shown will not be repeated here.

[0151] In the present application, the first computing device 310 detects at least one of the continuity, monotonicity, and rationality of the brightness mapping curves of multiple frames of images, thereby realizing multi-dimensional detection of metadata, improving the depth of metadata quality detection, and thereby ensuring the picture quality of the image when it is displayed.

[0152] In a possible embodiment, the first computing device 310 may output the detection result to the front end, and the front end may display the detection result. The front end here may refer to a display connected to the first computing device 310, or a display screen provided by the first computing device 310, etc., which is not limited in this application.

[0153] The detection result may include the metadata of each frame image in the video, such as the metadata of the first frame image is normal or the metadata of the second frame image is abnormal.

[0154] In a possible example, the detection results may also include: specific conditions of the metadata of each frame image, such as at least one abnormality in the continuity, monotonicity, and rationality of the corresponding brightness mapping curve of the first frame image, or screen flickering in the second frame image, that is, the similarity between the metadata of the first frame image and the second frame image is less than or equal to a threshold value of 1.

[0155] In another possible example, the above detection result may further include: marking one or more frames of images with abnormal metadata, that is, the marked images are images with abnormal metadata.

[0156] In a possible embodiment, the first computing device 310 may determine, based on the content of the detection result, to update the metadata of one or more frames of images with abnormal metadata.

[0157] In a possible implementation, the first computing device 310 determines one or more frames of images with abnormal metadata in the detection result, and then regenerates metadata according to the one or more frames of images with abnormal metadata.

[0158] Exemplarily, the first computing device 310 instructs the metadata editing tool to regenerate metadata according to one or more frames of images with abnormal metadata.

[0159] In another optional implementation, the first computing device 310 determines that the detection result indicates that there are one or more frames of images with abnormal metadata, and then instructs the metadata editing tool to regenerate metadata corresponding to each frame of the video based on the video.

[0160] The above method for regenerating metadata may refer to the content of obtaining metadata in S210, which will not be described in detail here.

[0161] In the present application, the first computing device 310 detects the quality of metadata from multiple dimensions by determining the similarity of metadata of two adjacent frames of images in multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data, thereby improving the depth of metadata quality detection, that is, the reliability of metadata, and ensuring the quality of the image displayed by the display device. In addition, the abnormal metadata is regenerated according to the detection result, further ensuring the quality of the picture when the display device displays the image.

[0162] In order to detect the continuity of the mapping curve of each frame image, the following provides a possible implementation method, such as Figure 4 As shown, Figure 4 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 1 The brightness mapping curve corresponding to the second image includes multiple curves. The second image is any frame image in the image data. The second threshold value can be called threshold value 2. Figure 2 S220 in the embodiment may include the following steps S410 to S440.

[0163] S410: The first computing device 310 determines a plurality of display brightness values ​​corresponding to each of a plurality of original brightness values ​​of a plurality of curve segments corresponding to the second image.

[0164] like Figure 4 As shown, the brightness mapping curve of the second image has three segments, and the first computing device 310 determines that there are overlapping horizontal coordinates between the horizontal coordinate sets corresponding to two adjacent segments of the curve, and the overlapping horizontal coordinates are the above-mentioned original brightness values, such as the original brightness value a and the original brightness value b. The first computing device 310 determines the display brightness value c' on curve 1 and the display brightness value c'' on curve 2 corresponding to the original brightness value a.

[0165] Likewise, the first computing device 310 determines a display brightness value d' corresponding to curve 2 and a display brightness value d" on curve 3 at the original brightness value b.

[0166] S420: The first calculation device 310 calculates the difference between the multiple display brightness values.

[0167] Taking one of the multiple original brightness values ​​as an example, the brightness mapping curve corresponds to multiple display brightness values ​​at the original brightness value, and the first calculation device 310 determines the difference between the multiple display brightness values.

[0168] Exemplarily, the first computing device 310 determines a difference 1 between the display brightness value c` and the display brightness value c``, and a difference 2 between the display brightness value d` and the display brightness value d``.

[0169] S430: If the difference values ​​are all less than or equal to the threshold 2, the detection result is used to indicate that the metadata of the second image is normal.

[0170] The first computing device 310 compares the difference values ​​respectively corresponding to the above-mentioned multiple original brightness values ​​with threshold 2, and when the difference values ​​are all less than or equal to threshold 2, determines that the detection result indicates that the metadata of the second image is normal.

[0171] Exemplarily, the difference 1 and the threshold 2 (such as 30 nit) are judged, and the difference 2 and the threshold 2. If the difference 1 and the difference 2 are both less than or equal to the threshold 2, the detection result is used to indicate that the metadata of the second image is normal.

[0172] S440: If there is at least one difference value greater than the threshold 2, the detection result is used to indicate that the metadata of the second image is abnormal.

[0173] The first computing device 310 compares the difference values ​​corresponding to the above-mentioned multiple original brightness values ​​with threshold 2, and when one of the difference values ​​is greater than threshold 2, determines that the detection result indicates that the metadata of the second image is abnormal.

[0174] Exemplarily, if at least one difference between the difference 1 and the threshold 2, or the difference 2 and the threshold 2, is greater than the threshold 2, then it is determined that the detection result indicates that the metadata of the second image is abnormal.

[0175] In the present application, the first computing device 310 detects the continuity of the brightness mapping curve to avoid discontinuity of the brightness mapping curve corresponding to the metadata, thereby preventing the display device from displaying images with broken images, thereby ensuring the quality of the image when displaying the image.

[0176] In order to detect the monotonicity of the brightness mapping curve of each frame image, a possible implementation method is provided as follows: Figure 5 As shown, Figure 5 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 2 The brightness mapping curve corresponding to the third image includes multiple curves, and the third image is any frame image in the image data. Figure 2 S220 in the embodiment may include the following steps S510 to S530.

[0177] S510 : The first computing device 310 determines the monotonicity of multiple curve segments corresponding to the third image.

[0178] like Figure 5 As shown, the first computing device 310 may perform derivation on the three curve segments (curve 1, curve 2, curve 3) corresponding to the third image to obtain a derivation result. If the derivation result may be a specific value or function, the first computing device 310 determines whether the specific value or function is greater than 0 or less than 0. If the derivation result is greater than 0, the segment of the curve is monotonically increasing; if the derivation result is less than 0, the segment of the curve is monotonically decreasing.

[0179] S520: If the first computing device 310 determines that the monotonicity corresponding to the multiple curve segments is not uniform, the detection result is used to indicate that the metadata of the third image is abnormal.

[0180] exist Figure 5 When the three curves shown are not all monotonically decreasing (the derivative results are all less than 0) or monotonically increasing (the derivative results are all greater than 0), the first computing device 310 determines that the monotonicity of the brightness mapping curve corresponding to the third image is not uniform, and the detection result indicates that the metadata of the third image is abnormal.

[0181] S530: If the first computing device 310 determines that the monotonicity corresponding to the multiple curve segments is unified, the detection result is used to indicate that the metadata of the third image is normal.

[0182] exist Figure 5 When the three curves shown are all monotonically decreasing or monotonically increasing, the first computing device 310 determines that the brightness mapping curve corresponding to the third image is monotonically uniform, and the detection result indicates that the metadata of the third image is normal.

[0183] In the present application, the first computing device 310 detects the monotonicity of the brightness mapping curve to avoid the problem of brightness flipping, that is, when the original brightness value is too large, the corresponding display brightness value is too small, or when the original brightness value is too small, the corresponding display brightness value is too large, thereby ensuring the quality of the picture when displaying the image.

[0184] In order to detect the rationality of the brightness mapping curve of each frame image, a possible implementation method is provided as follows: Figure 6 As shown, Figure 6 Schematic diagram of the process of the geometric characteristic detection method provided in this application Figure 3 The fourth image is any frame in the image data, and the third threshold value can be referred to as threshold value 3. Figure 2 S220 in the embodiment may include the following steps S610 to S640.

[0185] S610. The first computing device 310 determines a target original brightness value corresponding to a target display brightness value in a brightness mapping curve corresponding to the fourth image.

[0186] Exemplarily, the target display brightness value may be a maximum display brightness value or a minimum display brightness value that can be displayed by the display device.

[0187] Taking the maximum display brightness value or the minimum display brightness value that the display device can display as the target display brightness value as an example, the first computing device 310 determines the first original brightness value (target original brightness value) corresponding to the maximum display brightness value in the brightness mapping curve corresponding to the fourth image.

[0188] S620: The first computing device 310 calculates the ratio of the number of pixel points in the fourth image that are greater than or less than the target original brightness value to the total number of pixel points in the fourth image.

[0189] If the target display brightness value is the maximum display brightness value, the first computing device 310 may determine a value a greater than the first original brightness value corresponding to the maximum display brightness value from the brightness mapping curve corresponding to the fourth image. The value a may include one or more values.

[0190] The first calculation device 310 determines the number 1 of pixels whose original brightness value is value a from the fourth image, and then calculates the ratio of the number 1 to the total number of pixels corresponding to the fourth image, that is, the proportion.

[0191] like Figure 6 As shown, the maximum display brightness value is 500nit, and the first computing device 310 determines that the first original brightness value corresponding to the maximum display brightness value in the brightness mapping curve is 5000nit, and then determines the brightness in the brightness mapping curve that is greater than the first original brightness value, such as 5500nit. The first computing device 310 determines that the number of pixels with a brightness value of 5500nit in the fourth image is 300, and the total number of pixels in the fourth image is 1920*1080=2073600, so the proportion is 300 / 2073600, which is approximately equal to 0.014%.

[0192] In a possible example, the first computing device 310 may directly determine the number of pixel points whose original brightness values ​​are greater than the first original brightness value from the fourth image to obtain the number 1.

[0193] Similarly, if the target display brightness value is the minimum display brightness value, the first computing device 310 may determine a value b that is smaller than the second original brightness value corresponding to the minimum display brightness value from the brightness mapping curve corresponding to the fourth image. The value b may include one or more.

[0194] The first calculation device 310 determines the number 2 of pixels whose original brightness value is value b from the fourth image, and then calculates the ratio of the number 2 to the total number of pixels corresponding to the fourth image, that is, the proportion.

[0195] like Figure 6 As shown, the above minimum display brightness value is 1nit, and the first computing device 310 determines that the second original brightness value corresponding to the minimum display brightness value in the brightness mapping curve is 0.1nit, and then determines the brightness in the brightness mapping curve that is smaller than the above second original brightness value, such as 0.05nit. The first computing device 310 determines that the number of pixels with a brightness value of 0.05nit in the fourth image is 300, and the total number of pixels in the fourth image is 1920*1080=2073600, so the proportion is 300 / 2073600, which is approximately equal to 0.014%.

[0196] In a possible example, when determining the number 2, the first computing device 310 may directly determine the number of pixel points having brightness values ​​less than the second original brightness value from the fourth image to obtain the number 2.

[0197] In a possible embodiment, the target original brightness value may also be a maximum display brightness value preset by a user, or a minimum display brightness value preset by a user.

[0198] S630: If the first computing device 310 determines that the proportion is greater than the threshold value 3, the detection result is used to indicate that the metadata of the fourth image is abnormal.

[0199] Exemplarily, the first computing device 310 compares the proportion calculated above with a threshold value 3 (eg, 0.02%). If the proportion is less than the third threshold value, the detection result indicates that the metadata of the fourth image is normal.

[0200] S640: If the first computing device 310 determines that the proportion is less than the threshold value 3, the detection result is used to indicate that the metadata of the fourth image is normal.

[0201] Exemplarily, if the above proportion is greater than the threshold value 3, the detection result indicates that the metadata of the fourth image is abnormal.

[0202] In a possible example, when the ratio of the above-mentioned number 1 to the total number of pixels corresponding to the fourth image and the ratio of the number 2 to the total number of pixels corresponding to the fourth image are both less than the third threshold, it indicates that the metadata of the fourth image is normal. Conversely, if at least one of the above-mentioned two ratios is greater than the third threshold, it indicates that the metadata of the fourth image is abnormal.

[0203] In a possible scenario, if the proportion is equal to the threshold value 3, the detection result is used to indicate whether the metadata of the fourth image is normal or abnormal, which is not limited in this application.

[0204] In the present application, the first computing device 310 detects the rationality of the brightness mapping curve (brightness rationality), that is, detects whether there is an original brightness value in the brightness mapping curve that does not correspond to the display brightness value, thereby avoiding the problem of missing brightness of some pixels (i.e., not displayed) when the display device displays the video, thereby ensuring the quality of the picture when displaying the image.

[0205] In a possible embodiment, the metadata also includes a scene switching identifier, and the image whose metadata includes the scene switching identifier is a scene switching frame. If the one or more frames of images whose metadata is abnormal indicated by the detection result are scene switching frames, the first computing device 310 updates the one or more frames of images whose metadata is abnormal indicated by the detection result to have normal metadata. The scene switching frame is the next frame of the adjacent multiple frames of images whose similarity is less than or equal to the fourth threshold.

[0206] In one possible example, when the first computing device 310 determines that one or more frames of images with abnormal metadata are scene switching frames, it only updates the detection result of the one or more frames of images with abnormal metadata to: the metadata of the one or more frames of images is normal.

[0207] In the present application, since there may be scene switching in the video, sudden changes in brightness etc. during scene switching are normal, that is, when a frame of image is a scene switching frame, the metadata abnormality of the scene switching frame is allowed, that is, normal. The first computing device 310 sets the detection result corresponding to the scene switching frame as normal, which complies with the scene switching rule.

[0208] In a possible embodiment, the first computing device 310 in the above S210 obtains metadata of the image data between two scene switching frames. That is, when performing the above quality detection, the first computing device 310 only detects the metadata of the image data between the two scene switching frames, which reduces the amount of data calculation and improves data efficiency.

[0209] Two possible determination methods are provided below for the scene switching frame.

[0210] In an optional implementation, the first computing device 310 may determine whether the metadata of each frame image includes a scene switching identifier. The first computing device 310 obtains a scene switching identifier included in the metadata of the fifth image, and the scene switching identifier is used to indicate that the fifth image is a scene switching frame, and the fifth image is any frame image in the image data.

[0211] In another optional implementation, such as Figure 7 As shown, Figure 7A flow chart of a method for determining a scene switching frame provided by the present application. The sixth image and the seventh image are any frame images in the image data of the video, the fifth threshold value may also be referred to as threshold value 5, and the sixth threshold value may also be referred to as threshold value 6. The method may include the following steps S710 to S730.

[0212] S710: The first computing device 310 determines similarities between attributes of pixels in the sixth image and the seventh image.

[0213] The sixth image and the seventh image are in the same sliding window, and the seventh image is one or more frames of images adjacent to the sixth image.

[0214] The above-mentioned pixel attributes may include the brightness value (nit) of the pixel, brightness-chrominance (YUV) and other data. In the YUV, "Y" represents brightness (luminance or luma), while "U" and "V" represent chrominance (chroma).

[0215] The following provides a variety of similarity calculation methods: Pearson correlation coefficient, Spearman correlation coefficient, coefficient of determination.

[0216] The Pearson correlation coefficient is a statistic that measures the degree of linear correlation between two data.

[0217] Spearman correlation coefficient: It is a method to measure the correlation between data, but it does not require the data to be in a linear relationship.

[0218] Coefficient of determination: The coefficient of determination measures the degree to which one data explains another data by calculating the square of the Pearson correlation coefficient.

[0219] The similarity calculation method is Pearson correlation coefficient, Spearman correlation coefficient, and Pearson correlation coefficient in the determination coefficient. The first computing device 310 determines the Pearson correlation coefficient of the attributes (such as brightness value) of the pixel points in the sixth image in the video and the previous frame image (seventh image) of the sixth image, such as by using the following formula:

[0220]

[0221] Among them, X i It represents the brightness value of the i-th pixel in the sixth image, Y i It represents the brightness value of the i-th pixel in the seventh image, X iThe position of the corresponding pixel in the sixth image and Y i The corresponding pixels have the same position in the seventh image. may represent the average brightness value of all pixels of the sixth image, It can represent the average brightness value of all pixels of the seventh image.

[0222] The above example is described with a sliding window size of two frames. In other embodiments of the present application, the sliding window may be 3 frames or 5 frames, etc. The size of the sliding window may be set by the user as required, and the present application does not limit this. The multiple frames of images in the sliding window are adjacent multiple frames of images, and the adjacent multiple frames of images refer to multiple images that appear continuously when playing a video.

[0223] In another embodiment of the present application, the first computing device 310 determines the similarity of the brightness values ​​and YUV of the pixels in the sixth image and the seventh image.

[0224] Regarding the determination of the brightness values ​​of the pixels of the sixth image and the seventh image and the YUV similarity by the first computing device 310, reference may be made to the above-mentioned contents of determining the brightness values ​​of the pixels of the sixth image and the seventh image, which will not be elaborated here.

[0225] When the brightness values ​​of the pixels of the sixth image and the seventh characteristic, and the YUV similarity of the pixels of the sixth image and the seventh characteristic are both less than or equal to the threshold 5, the similarity of the attributes of the pixels in the sixth image and the seventh image is less than or equal to the threshold 5.

[0226] S720: The first computing device 310 calculates the similarity between the metadata of the sixth image and the metadata of the seventh image.

[0227] When the similarity between the attributes of the pixel points in the sixth image and the seventh image is less than or equal to the threshold 5, the first computing device 310 calculates the similarity between the metadata of the sixth image and the metadata of the seventh image, that is, the change between the metadata of the sixth image and the metadata of the seventh image.

[0228] In a possible example, the first computing device 310 compares the similarity between the attributes of the pixel points in the sixth image and the attributes of the pixel points in the seventh image with a threshold value of 5 (e.g., 0.8). When the similarity is greater than the threshold value 5, it indicates that the sixth image in the sliding window is a non-scene switching frame. When the similarity is less than or equal to the threshold value 5, it is necessary to further judge the sixth image, that is, calculate the similarity between the metadata of the sixth image and the metadata of the seventh image.

[0229] Regarding the content of the first computing device 310 calculating the similarity between the metadata of the sixth image and the metadata of one or more frames of images adjacent to the sixth image, reference may be made to the content in S220 above, which will not be elaborated herein.

[0230] Exemplarily, the first computing device 310 calculates the similarity between the metadata of the sixth image and the metadata of each frame of the one or more frames of images indicated by the seventh image.

[0231] S730: The first computing device 310 determines that the sixth image is a scene switching frame.

[0232] Among them, the similarity between the metadata of the sixth image and the metadata of the seventh image is less than or equal to threshold 6. In other words, the similarity between the metadata of the sixth image and the metadata of one or more frames of images adjacent to the sixth image is less than or equal to threshold 6.

[0233] Exemplarily, when the similarity between the metadata of the sixth image and the metadata of one or more frames of images adjacent to the sixth image is less than or equal to a threshold value of 6 (eg, 0.8), the first computing device 310 determines that the sixth image is a scene switching frame.

[0234] If a similarity between the metadata of the sixth image and the metadata of one or more frames of images adjacent to the sixth image is greater than a threshold value 6, the first computing device 310 determines that the sixth image is a non-scene switching frame.

[0235] In the present application, the first computing device 310 determines the scene switching frame from multiple frames of the video based on the similarity of the attributes of the pixel points and the similarity of the metadata. Then, when determining the abnormal metadata, the scene switching frame can be referred to. That is, when the monotonicity of the brightness mapping curve corresponding to the metadata of the scene switching frame is not uniform, the continuity is inconsistent, etc., the metadata of the scene switching frame is also normal, thereby improving the accuracy of determining the abnormal metadata.

[0236] In a possible embodiment, the first computing device 310 performs the above Figure 7 Before the content shown, each frame image in the video can be downsampled to reduce the resolution and improve the processing efficiency.

[0237] In addition, in order to avoid metadata errors during bitstream transmission and video decoding, the present application provides a metadata detection method, which can be applied to Figure 1 The computer system shown. Figure 8 Schematic diagram of the metadata detection method provided in this application Figure 2 The metadata detection method may be executed by a second computing device 320, which may be Figure 1 The decoding end 200 in FIG. Figure 8 As shown, the metadata detection method may include the following steps S810 and S820.

[0238] S810: The second computing device 320 obtains metadata of the image data.

[0239] The second computing device 320 receives the code stream sent by the first computing device 310, or obtains the stored code stream from the storage device 400. The code stream includes the encoded image data and metadata corresponding to the image data. The second computing device 320 decodes the code stream to obtain the metadata of the image data. The image data includes one or more frames of images.

[0240] In a possible example, the second computing device 320 uses the video decoder 220 to decode the code stream to obtain metadata of multiple frames of images in the video.

[0241] S820. The second computing device 320 processes the metadata of the image data according to the detection strategy to obtain a detection result.

[0242] The detection strategy is used to indicate: determining the similarity of metadata of two adjacent frames of images in the multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data. The brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of the image data.

[0243] For the description of S820, reference may be made to the content of obtaining the detection result shown in the above S220, which will not be described in detail here. Figure 2-Figure 7 The content shown executed by the first computing device 310 may also be executed by the second computing device 320 .

[0244] In the present application, the second computing device 320 (decoding end) further detects the metadata of the received image data to avoid metadata errors caused during the data transmission process or the video decoding process, thereby ensuring the picture quality when displaying the image.

[0245] In a possible embodiment, the second computing device 320 issues an alarm according to the detection result.

[0246] Among them, when the detection result indicates that the similarity of metadata of two adjacent frames of images in multiple frames is less than or equal to the first threshold, and / or the geometric characteristic detection of the brightness mapping curve of at least one frame of image in the image data is abnormal, the second computing device 320 issues an alarm based on the detection result.

[0247] In a possible implementation, the second computing device 320 may issue an alarm by displaying the alarm information on the front end of the second computing device 320, or sending the alarm information to the user by SMS or email. The alarm information is used to indicate that: the similarity of metadata of two adjacent frames of images in the multiple frames of images is less than or equal to the first threshold, and / or the geometric characteristic detection of the brightness mapping curve of at least one frame of image data is abnormal.

[0248] The front end here may refer to a display connected to the second computing device 320, or a display screen provided by the second computing device 320, etc., which is not limited in this application.

[0249] In a possible example, the second computing device 200 will also save the above-mentioned alarm information.

[0250] In another possible implementation, the second computing device 320 warns a processing unit according to the detection result. The processing unit is used to map the decoded image to a front-end processor. The processing unit is disposed inside the second computing device 320.

[0251] The alarm instructs the decoding end to perform mapping processing on one or more frames of images according to preset metadata, and the one or more frames of images include images with abnormal metadata.

[0252] The second computing device 320 may execute the following three examples of content according to the alarm indication.

[0253] Example 1: The second computing device 320 deletes the metadata of the next frame image whose similarity of the metadata is less than or equal to the first threshold according to the alarm indication. And, when the second computing device 320 maps the next frame image to the front end of the second computing device 320, the mapping process is performed according to the preset metadata.

[0254] Example 2: The second computing device 320 deletes the metadata of at least one frame of image with abnormal geometric feature detection in the image data according to the alarm indication, and performs mapping processing according to the preset metadata when mapping the at least one frame of image with abnormal geometric feature detection to the front end of the second computing device 320.

[0255] Example 3: When the second computing device 320 maps the above video to the front end according to the alarm indication, it processes it according to the preset metadata. In other words, the second computing device 320 deletes the metadata decoded from the bitstream, and maps the image decoded from the bitstream to the front end for display according to the preset metadata.

[0256] Combined with the above Figures 1 to 7 , describes in detail the metadata detection method provided by this application, and will be combined with Fig. 9 , Fig. 9 A schematic diagram of the structure of a metadata detection device provided in this application Figure 1 , describes the first metadata detection device provided by the present application. The first metadata detection device 900 can be used to implement the function of the first computing device 310 in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment.

[0257] like Fig. 9 As shown, the first metadata detection device 900 includes a first acquisition module 910 and a first processing module 920. The first metadata detection device 900 is used to implement the above Figures 1 to 7 The function of the first computing device 310 in the corresponding method embodiment. In a possible example, the specific process of the first metadata detection device 900 for implementing the above metadata detection method includes the following process:

[0258] The first acquisition module 910 is used to acquire metadata of image data. The image data includes one or more frames of images.

[0259] The first processing module 920 is used to process the metadata of the image data according to the detection strategy to obtain a detection result. The detection strategy is used to indicate: determining the similarity of the metadata of two adjacent frames of images in the multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data, the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of the image data.

[0260] To further achieve the above Figures 1 to 7 The present application also provides a metadata detection device, such as Fig.10 As shown, Fig.10 A schematic diagram of the structure of a metadata detection device provided in this application Figure 2 The first metadata detection device 900 also includes a first updating module 930 and a second updating module 940.

[0261] Among them, the first updating module 930 is used to update the one or more frames of images with abnormal metadata indicated by the detection result to normal metadata if the one or more frames of images with abnormal metadata indicated by the detection result are scene switching frames; the scene switching frame is the next frame of image in the adjacent multiple frames of images whose metadata similarity is less than or equal to the fourth threshold.

[0262] The second updating module 940 is configured to update the metadata of the one or more frames of images with abnormal metadata if the detection result indicates that the metadata of the one or more frames of images are abnormal.

[0263] For the above description of determining the scene switching frame in the video, please refer to the above Figure 7The contents shown will not be repeated here.

[0264] As an example of a hardware functional unit, the first acquisition module 910 may include at least one computing device, such as a server, etc. Alternatively, the first acquisition module 910 may also be a device implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0265] It should be noted that, in other embodiments, the first acquisition module 910 can be used to execute any step in the metadata detection method, and the first processing module 920 can be used to execute any step in the metadata detection method. The steps that the first acquisition module 910 and the first processing module 920 are responsible for implementing can be specified as needed. The first acquisition module 910 and the first processing module 920 respectively implement different steps in the metadata detection method to achieve all the functions of the first metadata detection device.

[0266] It is worth noting that the first computing device 310 of the aforementioned embodiment may correspond to the first metadata detection device 900, and may correspond to executing the method according to the embodiment of the present application. Figures 2 to 7 The corresponding corresponding subjects, and the operations and / or functions of each module in the first metadata detection device 900 are respectively to achieve Figures 2 to 7 For the sake of brevity, the corresponding processes of each method in the corresponding embodiments are not repeated here.

[0267] Combined with the above Figure 1 and Figure 8 , describes in detail the metadata detection method provided by this application, and will be combined with Fig.11 , Fig.11 A schematic diagram of the structure of a metadata detection device provided in this application Figure 3 , describes the second metadata detection device provided by the present application. The second metadata detection device 1100 can be used to implement the function of the second computing device 320 in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment.

[0268] like Fig.11As shown, the second metadata detection device 1100 includes a second acquisition module 1110 and a second processing module 1120. The second metadata detection device 1100 is used to implement the above Figure 1 and Figure 8 The function of the second computing device 320 in the corresponding method embodiment. In a possible example, the specific process of the second metadata detection device 1100 for implementing the above metadata detection method includes the following process:

[0269] The second acquisition module 1110 is used to acquire metadata of the image data.

[0270] The second processing module 1120 is used to process the metadata of the image data according to the detection strategy to obtain a detection result. The detection strategy is used to indicate: determining the similarity of the metadata of two adjacent frames of images in the multiple frames of images, and / or detecting the geometric characteristics of the brightness mapping curve of the image data, the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame of the image data.

[0271] To further achieve the above Figure 1 and Figure 8 The present application also provides a metadata detection device, such as Fig.12 As shown, Fig.12 A schematic diagram of the structure of a metadata detection device provided in this application Figure 4 The second metadata detection device 1100 also includes an alarm module 1130 , a third updating module 1140 , and a fourth updating module 1150 .

[0272] Among them, the alarm module 1130 is used to issue an alarm according to the detection result when the detection result indicates that the similarity of metadata of two adjacent frames of images in multiple frames is less than or equal to a first threshold, and / or the geometric characteristic detection of the brightness mapping curve of at least one frame of image in the image data is abnormal.

[0273] The third updating module 1140 is used to update the one or more frames of images with abnormal metadata indicated by the detection result to normal metadata if the one or more frames of images with abnormal metadata indicated by the detection result are scene switching frames; the scene switching frame is the next frame of image in the adjacent multiple frames of images whose metadata similarity is less than or equal to a fourth threshold.

[0274] The fourth updating module 1150 is configured to perform mapping processing on the one or more frames of images according to preset metadata if the detection result indicates that the metadata of one or more frames of images are abnormal, and the one or more frames of images include the image with abnormal metadata.

[0275] It is worth noting that the second computing device 320 in the aforementioned embodiment may correspond to the second metadata detection device 1100, and may correspond to executing the method according to the embodiment of the present application. Figure 8 The corresponding corresponding subjects, and the operations and / or functions of each module in the second metadata detection device 1100 are respectively to achieve Figure 8 For the sake of brevity, the corresponding processes of each method in the corresponding embodiments are not repeated here.

[0276] in addition, Figures 9 to 12 The metadata detection device shown can also be implemented by a communication device, where the communication device may refer to the computing device (the first computing device 310 or the second computing device 320) in the aforementioned embodiment, or, when the communication device is a chip or a chip system applied to a computing device, the metadata detection device can also be implemented by the chip or the chip system.

[0277] An embodiment of the present application also provides a chip system, which includes a control circuit and an interface circuit. The interface circuit is used to obtain metadata of image data, and the control circuit is used to implement the function of the computing device in the above method according to the metadata of the image data.

[0278] In a possible design, the chip system further includes a memory for storing program instructions and / or data. The chip system may be composed of a chip, or may include a chip and other discrete devices.

[0279] The present application also provides a computing device. Fig.13 As shown, Fig.13 The present application provides a schematic diagram of the structure of a computing device, wherein the computing device 1300 includes: a bus 1302, a processor 1304, a memory 1306, and a communication interface 1308. The processor 1304, the memory 1306, and the communication interface 1308 communicate with each other through the bus 1302. The computing device 1300 may be a server, a terminal device, or a decoder, and the computing device 1300 may be the first computing device 310 or the second computing device 320 mentioned above. It is worth noting that the present application does not limit the number of processors and memories in the computing device 1300, and the decoder may include a decoder or an encoder. When the computing device 1300 is an encoder, the computing device 1300 may be the first computing device 310 mentioned above; when the computing device 1300 is a decoder, the computing device 1300 may be the second computing device 320 mentioned above.

[0280] The bus 1302 may be, but is not limited to: a PCIe bus, a universal serial bus (USB), or an inter-integrated circuit bus (I2C), an EISA bus, an UB, a CXL, a CCIX, etc. The bus 1302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.13 The bus 1302 may include a path for transmitting information between various components of the computing device 1300 (eg, the memory 1306, the processor 1304, and the communication interface 1308).

[0281] The processor 1304 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0282] The memory 1306 may include a volatile memory, such as a random access memory (RAM). The memory 1306 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0283] The memory 1306 stores executable program codes, and the processor 1304 executes the executable program codes to respectively implement the functions of the first acquisition module and the first processing module, thereby implementing the above metadata detection method. That is, the memory 1306 stores instructions for executing the metadata detection method.

[0284] Alternatively, the memory 1306 stores executable codes, and the processor 1304 executes the executable codes to respectively implement the functions of the second acquisition module and the second processing module, thereby implementing the metadata detection method. That is, the memory 1306 stores instructions for executing the metadata detection method.

[0285] The communication interface 1308 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device 1300 and other devices or communication networks.

[0286] The embodiment of the present application further provides a computing device cluster, which includes at least one computing device 1300. The memory 1306 in one or more computing devices 1300 in the computing device cluster may store the same instructions for executing the metadata detection method.

[0287] The computing device 1300 may be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device 1300 may also be a terminal device such as a desktop computer, a notebook computer, or a smart phone.

[0288] In some possible implementations, one or more computing devices in the computing device cluster may be connected via a network, which may be a wide area network or a local area network.

[0289] The embodiment of the present application also provides a computer program product including instructions. The computer program product may be software or a program product including instructions that can be run on a computing device or stored in any available medium. When the computer program product is run on at least one computing device, the at least one computing device executes the metadata detection method.

[0290] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that can be stored by the computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute the metadata detection method.

[0291] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiment of the present application is executed in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device or other programmable device. The computer program or instruction may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instruction may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium, for example, a floppy disk, a hard disk, a tape; it may also be an optical medium, for example, a digital video disc (DVD); it may also be a semiconductor medium, for example, a solid state drive (SSD).

[0292] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A metadata detection method, characterized in that: The method is applied to a computing device, and the method comprises: Acquire metadata of image data; the image data includes one or more frames of image; Process the metadata of the image data according to the detection strategy to obtain a detection result; Among them, the detection strategy is used to indicate: determining the similarity of metadata of two adjacent frames of images in the multiple frames of images, and / or, detecting the geometric characteristics of the brightness mapping curve of the image data; the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame image in the image data.

2. The method according to claim 1, characterized in that: The metadata of the image data obtained includes: Receiving a trigger operation of a control component on a user interface; In response to the user's triggering operation on the control component, metadata of the image data is acquired.

3. The method according to claim 1 or 2, characterized in that: The step of processing the metadata of the image data according to the detection strategy to obtain the detection result includes: Determining a brightness mapping curve of the two adjacent frames of images according to metadata of the two adjacent frames of images; If the similarity of the brightness mapping curves of the two adjacent frames of images is less than a first threshold, the detection result is used to indicate that metadata of one of the two adjacent frames of images is abnormal; If the similarity of the brightness mapping curves of the two adjacent frames of images is greater than the first threshold, the detection result is used to indicate that the metadata of one of the two adjacent frames of images is normal; If the similarity of the brightness mapping curves of the two adjacent image frames is equal to the first threshold, the detection result is used to indicate whether the metadata of one of the two adjacent image frames is normal or abnormal.

4. The method according to any one of claims 1 to 3, characterized in that The brightness mapping curve corresponding to the second image includes multiple curves, the second image is any frame image in the image data, and the metadata of the image data is processed according to the detection strategy to obtain the detection result, including: Determine a plurality of display brightness values ​​corresponding to each of a plurality of original brightness values ​​of a plurality of curve segments corresponding to the second image; Calculating the difference between the plurality of display brightness values; If the difference values ​​are all less than or equal to the second threshold, the detection result is used to indicate that the metadata of the second image is normal; If there is at least one difference value greater than the second threshold, the detection result is used to indicate that the metadata of the second image is abnormal.

5. The method according to any one of claims 1 to 4, characterized in that The brightness mapping curve corresponding to the third image includes multiple curves, the third image is any frame image in the image data, and the metadata of the image data is processed according to the detection strategy to obtain the detection result, including: Determining the monotonicity of the multiple curves corresponding to the third image; If the monotony corresponding to the multiple curve segments is not uniform, the detection result is used to indicate that: the metadata of the third image is abnormal; If the monotonicity corresponding to the multiple curve segments is uniform, the detection result is used to indicate that the metadata of the third image is normal.

6. The method according to any one of claims 1 to 5, characterized in that The step of processing the metadata of the image data according to the detection strategy to obtain the detection result includes: Determine a target original brightness value corresponding to a target display brightness value in a brightness mapping curve corresponding to a fourth image, wherein the fourth image is any frame image in the image data; Calculate the ratio of the number of pixels in the fourth image that are greater than or less than the target original brightness value to the total number of pixels in the fourth image; If the proportion is greater than a third threshold, the detection result is used to indicate that: the metadata of the fourth image is abnormal; If the proportion is less than the third threshold, the detection result is used to indicate that the metadata of the fourth image is normal; If the proportion is equal to the third threshold, the detection result is used to indicate whether the metadata of the fourth image is normal or abnormal.

7. The method according to any one of claims 1 to 6, characterized in that The multiple frames of images are images between two scene switching frames, and the scene switching frame is a subsequent frame of image in the multiple adjacent frames of images whose metadata similarity is less than or equal to a fourth threshold.

8. The method according to any one of claims 1 to 6, characterized in that The method further comprises: If the one or more frames of images with abnormal metadata indicated by the detection result are scene switching frames, the one or more frames of images with abnormal metadata indicated by the detection result are updated to have normal metadata; the scene switching frame is the next frame of image in the adjacent multiple frames of images whose metadata similarity is less than or equal to a fourth threshold.

9. The method according to claim 7 or 8, characterized in that: The scene switching frame can be determined by: A scene switching identifier included in metadata of the fifth image is obtained, where the scene switching identifier is used to indicate that the fifth image is a scene switching frame, and the fifth image is any frame image in the image data.

10. The method according to claim 7 or 8, characterized in that: The scene switching frame can be determined by: Determine the similarity of attributes of pixel points in a sixth image and a seventh image; the sixth image and the seventh image are in the same sliding window, the seventh image is one or more frames of images adjacent to the sixth image, the sixth image and the seventh image are any frame of images in the image data, and the attributes include: one or more of brightness value or brightness-chrominance YUV; If the similarity between the attributes of the pixels in the sixth image and the seventh image is less than or equal to a fifth threshold, calculating the similarity between the metadata of the sixth image and the metadata of the seventh image; The sixth image is determined to be the scene switching frame; and a similarity between the metadata of the sixth image and the metadata of the seventh image is less than or equal to a sixth threshold.

11. The method according to any one of claims 1 to 10, characterized in that The method further comprises: If the detection result indicates that the metadata of one or more frames of images are abnormal, the metadata of the one or more frames of images with the abnormal metadata are updated.

12. The method according to any one of claims 1 to 10, characterized in that The method further comprises: If the detection result indicates that the similarity of metadata of two adjacent frames of images in the multiple frames is less than or equal to a first threshold, and / or the geometric characteristic detection of the brightness mapping curve of at least one frame of image in the image data is abnormal, an alarm is issued based on the detection result.

13. The method according to claim 12, characterized in that The method further comprises: If the detection result indicates that metadata of one or more frames of images are abnormal, mapping processing is performed on the one or more frames of images according to preset metadata, and the one or more frames of images include the image with abnormal metadata.

14. A metadata detection device, characterized in that: The device is applied to a computing device, and comprises: A first acquisition module, used to acquire metadata of image data; the image data includes one or more frames of images; A first processing module, used for processing the metadata of the image data according to the detection strategy to obtain a detection result; Among them, the detection strategy is used to indicate: determining the similarity of metadata of two adjacent frames of images in the image data, and / or, detecting the geometric characteristics of a brightness mapping curve of the image data, the brightness mapping curve of the first image is obtained based on the metadata of the first image, and the first image is any frame image in the multiple frames of images.

15. A chip, characterized in that: include: Processor and power supply circuit; The power supply circuit is used to supply power to the processor; The processor is configured to execute the method according to any one of claims 1 to 13.

16. A decoder, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store computer instructions; when the processor executes the computer instructions, the method according to any one of claims 1 to 13 is implemented.

17. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a processing device, the method according to any one of claims 1 to 13 is implemented.

18. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed on a processing device, the method according to any one of claims 1 to 13 is implemented.

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