A method and device for automatically debugging screen image quality

Abstract

Embodiments of the present disclosure provide a method and device for automatically debugging screen image quality, which are applied to the field of debugging. The method includes using each test picture in a test picture set to perform PQ parameter debugging on the current screen respectively, obtaining the optimal PQ parameters corresponding to each test picture as a PQ parameter set; using each PQ parameter in the PQ parameter set to test each test picture in the test picture set respectively, and deleting the PQ parameters with test scores lower than a preset threshold from the PQ parameter set; sorting the PQ parameters in the PQ parameter set according to a preset rule, and taking the PQ parameter ranked first for debugging the current screen. In this way, automatic debugging of screen image quality can be achieved without manual observation, and the problem of low efficiency in debugging screen image quality in the prior art can be solved.

Description

Technical Field

[0001] The present disclosure relates to the field of debugging, and in particular, to a method and device for automatically debugging screen image quality. Background Art

[0002] When each TV leaves the factory, due to differences in materials, equipment, production lines, etc., the quality consistency cannot be guaranteed. For a simple example, the same red RGB value, such as FF 00 00, may not display the same red color on different screens. Therefore, there are dedicated personnel to debug the image quality of the TVs leaving the factory. They observe the effects with the naked eye, then adjust relevant parameters, confirm the effects, and readjust the parameters if they do not meet the requirements, and so on. Eventually, the debugging of the screen image quality of the TVs leaving the factory is achieved. The process is cumbersome and time-consuming. Summary of the Invention

[0003] The present disclosure provides a method, device, equipment, and storage medium for automatically debugging screen image quality.

[0004] According to a first aspect of the present disclosure, there is provided a method for automatically debugging screen image quality. The method includes:

[0005] Performing PQ parameter debugging on the current screen respectively using each test picture in the test picture set to obtain the optimal PQ parameters corresponding to each test picture, as a PQ parameter set;

[0006] Testing each test picture in the test picture set respectively using each PQ parameter in the PQ parameter set, and deleting the PQ parameters with test scores lower than a preset threshold from the PQ parameter set;

[0007] Sorting the PQ parameters in the PQ parameter set according to a preset rule, and taking the PQ parameter ranked first for debugging the current screen.

[0008] Further, the obtaining of the optimal PQ parameters corresponding to each test picture includes:

[0009] Respectively collecting the test scores corresponding to each PQ parameter of each test picture under different lighting environments;

[0010] Taking all PQ parameters and corresponding test scores of each test picture as a training set to obtain multiple groups of objective functions corresponding to the test picture;

[0011] Performing weight update iteration and gradient solution respectively on the multiple groups of objective functions to find the optimal solution corresponding to each group of objective functions;

[0012] Taking the optimal solution obtained for each group as the optimal PQ parameter corresponding to each test picture.

[0013] Further, using each PQ parameter in the PQ parameter set to test each test picture in the test picture set includes:

[0014] Testing according to all combinations of each PQ parameter in the PQ parameter set and each test picture in the test picture set.

[0015] Among them, the testing includes: AI recognition and / or pixel point acquisition, and scoring according to the AI recognition result and / or the value range of the collected pixel points.

[0016] Further, deleting the PQ parameters with test scores lower than the preset threshold from the PQ parameter set includes:

[0017] If the test score of any combination of a PQ parameter and a test picture is lower than the preset threshold, then delete the PQ parameter from the PQ parameter set.

[0018] Further, sorting the PQ parameters in the PQ parameter set according to a preset rule includes:

[0019] Performing weighted summation using one or more of brightness, contrast, and color temperature to obtain a weighted value;

[0020] Sorting the PQ parameters in the PQ parameter set from largest to smallest according to the weighted value.

[0021] According to a second aspect of the present disclosure, there is provided a device for automatically debugging screen image quality. The device includes:

[0022] A data acquisition module, configured to use each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively, and obtain the optimal PQ parameter corresponding to each test picture as the PQ parameter set;

[0023] A test module, configured to use each PQ parameter in the PQ parameter set to test each test picture in the test picture set, and delete the PQ parameters with test scores lower than the preset threshold from the PQ parameter set;

[0024] A debugging module, configured to sort the PQ parameters in the PQ parameter set according to a preset rule, and use the PQ parameter ranked first in the sorting to perform debugging on the current screen.

[0025] According to a third aspect of the present disclosure, there is provided an electronic device. The electronic device includes: a memory and a processor, and a computer program is stored on the memory, and when the processor executes the program, the method described above is implemented.

[0026] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements the method according to the first aspect and / or the second aspect of the present disclosure.

[0027] A method and apparatus for automatically debugging screen image quality provided by the present disclosure obtain the optimal PQ parameters of each test picture through deep learning, and use the optimal PQ parameters to test and score all test pictures, and select the PQ parameter ranked first to debug the screen image quality. In this way, automatic debugging of screen image quality can be achieved, and the efficiency of debugging screen image quality can be improved.

[0028] It should be understood that the content described in the summary of the invention is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0030] Figure 1 shows a flowchart of a method for automatically debugging screen image quality according to an embodiment of the present disclosure;

[0031] Figure 2 shows a block diagram of an apparatus for automatically debugging screen image quality according to an embodiment of the present disclosure;

[0032] Figure 3 shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present disclosure fall within the scope of the present disclosure.

[0034] In addition, the term "and / or" in this article is merely an association relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0035] Figure 1 The flowchart of a method 100 for automatically debugging the screen image quality according to an embodiment of the present disclosure is shown. The method 100 includes:

[0036] 110. Use each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively, and obtain the optimal PQ parameters corresponding to each test picture as a PQ parameter set.

[0037] Among them, PQ is short for Picture Quality, which refers to the picture quality. The PQ parameters include: color temperature, color, brightness, contrast, sharpness, dynamics, dark field processing, etc. Each test picture in the test picture set includes collecting high-quality test pictures from previous projects and also includes high-quality pictures collected according to user usage feedback.

[0038] The step 110 specifically includes: using each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively, and collecting the test scores corresponding to each PQ parameter of each test picture in different lighting environments; taking all the PQ parameters and the corresponding test scores of each test picture as a training set to obtain multiple sets of objective functions corresponding to the test pictures; then performing weight update iteration and gradient solution on the multiple sets of objective functions respectively to find the optimal solution corresponding to each set of objective functions; finally, taking the optimal solution obtained for each set as the optimal PQ parameter corresponding to each test picture as a PQ parameter set. Among them, the test score refers to: performing AI recognition and / or pixel point collection on each test picture respectively, and scoring according to the AI recognition result and / or the value range of the collected pixel points to obtain the test score. A pixel point is the smallest light-emitting unit of a display screen, which is composed of three pixel units of red, green, and blue. The more pixel points in the screen, the higher the resolution of the picture, and the more delicate and realistic the image. Therefore, the larger the value range of the collected pixel points, the higher the test score. In some embodiments, a corresponding score is given according to the probability that the image output by the AI model belongs to the corresponding entity. For example, if the image is a black cat, the AI model is an image recognition model, and the output is the probability that the image belongs to each entity. If the probability of belonging to a black cat is greater than a preset threshold, the score is 1; if it is less than the preset threshold, the score is 0. The score for 1600 (horizontal) × 1200 (vertical) pixel points is 3, the score for 2048 (horizontal) × 1536 (vertical) is 3.5, the score for 2400 (horizontal) × 1800 (vertical) is 4, the score for 2560 (horizontal) × 1920 (vertical) is 4.5......

[0039] Deep learning realizes complex function approximation and input data representation by learning a deep non-linear network structure, demonstrating a powerful learning ability for the essential features of the dataset. The deep learning method inputs the PQ parameters of each test image and the corresponding test scores, automatically learns the features of different types of a large amount of data, obtains the corresponding objective function, updates the weights of the objective function iteratively and solves the gradient to obtain the optimal solution of the objective function. Subsequently, only by providing the test image, the optimal PQ parameters can be output, saving equipment and manpower and greatly improving efficiency. In some embodiments, a top-down supervised learning is adopted, and the network is fine-tuned by the PQ parameters of each test image and the corresponding test scores, with the error transmitted from top to bottom. Based on the parameters of each layer obtained in the first step, the parameters of the entire multi-layer model are further optimized, and finally the optimal network model between the PQ parameters and the test scores is obtained as the objective function. Then, the Adaptive Moment Estimation algorithm (referred to as the Adam algorithm) is used for weight update iteration and gradient descent convergence to converge the objective function to an extreme value and obtain the optimal PQ parameters.

[0040] 120. Use each PQ parameter in the PQ parameter set to test each test image in the test image set, and delete the PQ parameters with test scores lower than the preset threshold from the PQ parameter set.

[0041] The step 120 specifically includes: testing according to all combinations of each PQ parameter in the PQ parameter set and each test image in the test image set; if the test score of any PQ parameter and test image combination is lower than the preset threshold, then delete the PQ parameter from the PQ parameter set. Wherein, the test refers to: AI recognition and / or pixel point collection, and scoring is performed according to the AI recognition result and / or the value range of the collected pixel points, and the scoring rule is the same as the test scoring rule in step 110. In some embodiments, delete the corresponding PQ parameter with a failed AI recognition result, that is, a score of 0, or delete the PQ parameter with a pixel point score lower than the preset threshold of 5, or delete the PQ parameter with a weighted sum of the AI recognition result score and the pixel point score lower than the preset threshold of 5.

[0042] 130. Sort the PQ parameters in the PQ parameter set according to a preset rule, and take the PQ parameter ranked first for debugging the current screen.

[0043] The step 130 specifically includes: performing weighted summation on the PQ parameters in the PQ parameter set obtained in step 120 using one or more of brightness, contrast, and color temperature to obtain the weighted value corresponding to each PQ parameter; sorting the PQ parameters in the PQ parameter set from largest to smallest according to the weighted value; taking the PQ parameter ranked first for debugging the current screen.

[0044] In some embodiments, for the PQ parameters in the PQ parameter set obtained in step 120, the luminance, contrast, and color temperature value data of the PQ parameters are extracted, weighted and summed according to preset weight coefficients, and the PQ parameters in the PQ parameter set are sorted from largest to smallest according to the summation result. The PQ parameter ranked first is taken to debug the current screen, and it can be realized that the PQ parameter ranked first at this time is the optimal parameter for the current screen image quality.

[0045] In some embodiments, the method further includes:

[0046] Use the PQ parameter ranked first to perform image quality debugging on the screens of the same production batch of the current screen.

[0047] It is not only applicable to the image quality debugging of one screen, but also applicable to the image quality debugging of any one or more screens of the same production batch.

[0048] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0049] A method and device for automatically debugging screen image quality provided by the present disclosure obtain the optimal PQ parameters of each test picture through deep learning, and use the optimal PQ parameters to test and score all test pictures, and select the PQ parameter ranked first to debug the screen image quality. In this way, automatic debugging of screen image quality can be realized, and the efficiency of debugging screen image quality can be improved.

[0050] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0051] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0052] Figure 2 The block diagram of a device 200 for automatically debugging screen image quality according to an embodiment of the present disclosure is shown. The device 200 includes:

[0053] A data acquisition module 210, configured to use each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively, and obtain the optimal PQ parameters corresponding to the test pictures as a PQ parameter set;

[0054] A test module 220 is configured to test each test image in the test image set by using each PQ parameter in the PQ parameter set, and delete the PQ parameters with test scores lower than a preset threshold from the PQ parameter set;

[0055] A debugging module 230 is configured to sort the PQ parameters in the PQ parameter set according to a preset rule, and take the PQ parameter ranked first for debugging the current screen.

[0056] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0057] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device and a readable storage medium.

[0058] Figure 3 FIG. shows a schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0059] The device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0060] A plurality of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0061] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as a method for automatically debugging the screen image quality. For example, in some embodiments, a method for automatically debugging the screen image quality can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method for automatically debugging the screen image quality described above can be executed. Alternatively, in other embodiments, the computing unit 301 can be configured to execute a method for automatically debugging the screen image quality by any other suitable means (e.g., by means of firmware).

[0062] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0063] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0064] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0065] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0066] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0067] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0068] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0069] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for automatically debugging screen image quality, characterized in that including: using each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively; collecting the test scores corresponding to each PQ parameter of each of the test pictures in different lighting environments respectively; taking all the PQ parameters and the corresponding test scores of the test pictures as a training set to obtain multiple groups of objective functions corresponding to the test pictures; performing weight update iteration and gradient solution on the multiple groups of objective functions respectively to find the optimal solution corresponding to each group of objective functions; taking the optimal solution obtained for each group as the optimal PQ parameter corresponding to each of the test pictures, serving as a PQ parameter set; using each PQ parameter in the PQ parameter set to test each test picture in the test picture set respectively, and deleting the PQ parameters with test scores lower than a preset threshold from the PQ parameter set; sorting the PQ parameters in the PQ parameter set according to a preset rule, and taking the PQ parameter ranked first for debugging the current screen.

2. The method according to claim 1, characterized in that, The step of using each PQ parameter in the PQ parameter set to test each test picture in the test picture set respectively includes: performing tests according to all combinations of each PQ parameter in the PQ parameter set and each test picture in the test picture set.

3. The method according to claim 1 or 2, characterized in that, The test includes: AI recognition and / or pixel point collection, and scoring according to the AI recognition result and / or the value range of the collected pixel points.

4. The method according to claim 1 or 2, characterized in that, The step of deleting the PQ parameters with test scores lower than a preset threshold from the PQ parameter set includes: if the test score of any combination of a PQ parameter and a test picture is lower than the preset threshold, deleting the PQ parameter from the PQ parameter set.

5. The method according to claim 1, characterized in that, The step of sorting the PQ parameters in the PQ parameter set according to a preset rule includes: performing weighted summation using one or more of brightness, contrast, and color temperature to obtain a weighted value; sorting the PQ parameters in the PQ parameter set from large to small according to the weighted value.

6. An apparatus for automatically debugging screen image quality, characterized in that, including: a data acquisition module, configured to use each test picture in the test picture set to perform PQ parameter debugging on the current screen respectively; collecting the test scores corresponding to each PQ parameter of each of the test pictures in different lighting environments respectively; taking all the PQ parameters and the corresponding test scores of the test pictures as a training set to obtain multiple groups of objective functions corresponding to the test pictures; performing weight update iteration and gradient solution on the multiple groups of objective functions respectively to find the optimal solution corresponding to each group of objective functions; taking the optimal solution obtained for each group as the optimal PQ parameter corresponding to each of the test pictures, serving as a PQ parameter set; a test module, configured to use each PQ parameter in the PQ parameter set to test each test picture in the test picture set respectively, and deleting the PQ parameters with test scores lower than a preset threshold from the PQ parameter set; a debugging module, configured to sort the PQ parameters in the PQ parameter set according to a preset rule, and taking the PQ parameter ranked first for debugging the current screen.

7. An electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are for causing the computer to execute the method according to any one of claims 1-5.

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