Display devices and image still frame detection methods

Abstract

This disclosure relates to a display device and an image still frame detection method. The display device includes: a display configured to display image frames according to a refresh direction; and a controller configured to, in a partitioning mode, partition the current image frame displayed on the display in the same direction as the refresh direction according to a current partitioning granularity table, obtaining multiple partitions; determine the feature value of each partition; compare the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and update the change integral value corresponding to each partition according to the comparison result; determine whether each partition meets the partitioning granularity adjustment condition based on the change integral value, and if at least one partition meets the partitioning granularity adjustment condition, determine an adjusted partitioning granularity table based on the partitioning granularity of the target partition that meets the partitioning granularity adjustment condition and the current partitioning granularity table; and partition the next image frame according to the adjusted partitioning granularity table. This scheme achieves adaptive adjustment of partitioning granularity.

Description

Technical Field

[0001] This disclosure relates to image processing technology. More specifically, it relates to a display device and a method for detecting still frames of an image. Background Technology

[0002] With the widespread application of technologies such as LCD displays, LED displays, and video codecs, image still frame detection has become one of the key technologies for improving the performance and energy efficiency of display systems. The purpose of still frame detection is to identify which areas in an image frame remain still and which areas change, thereby enabling selective refreshing or processing, avoiding repeated refreshing of still areas, and thus reducing power consumption and improving response speed.

[0003] In related technologies, fixed partition granularity or fixed row and column pattern is usually used for still frame detection. In this still frame detection method, the partition granularity is fixed and cannot be dynamically adjusted according to the actual image changes. This can easily lead to problems such as insufficient detection sensitivity or low refresh efficiency. For example, a large fixed partition granularity may mask subtle dynamic changes, resulting in missed detections, while a small fixed partition granularity will increase the number of detections and refreshes, reducing system efficiency. Summary of the Invention

[0004] To solve the above-mentioned technical problems, or at least partially solve them, this disclosure provides a display device and an image still frame detection method.

[0005] In a first aspect, embodiments of this disclosure provide a display device, including: a display configured to display image frames according to a preset refresh direction; and a controller connected to the display, configured to: in a partitioning mode, partition the current image frame displayed on the display in a direction consistent with the refresh direction according to a current partitioning granularity table to obtain multiple partitions; determine a feature value for each partition; compare the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and update the change integral value corresponding to each partition according to the comparison result, wherein the change integral value is used to represent the change information of the feature value of the corresponding partition; determine whether each partition meets the partitioning granularity adjustment condition based on the change integral value, and if at least one partition meets the partitioning granularity adjustment condition, determine an adjusted partitioning granularity table based on the partitioning granularity of the target partition that meets the partitioning granularity adjustment condition and the current partitioning granularity table; and when the display displays the next image frame, partition the next image frame according to the adjusted partitioning granularity table.

[0006] In some embodiments of this disclosure, the controller is specifically configured to: decrease the integral value of change corresponding to the first partition or keep the integral value of change corresponding to the first partition unchanged when the feature value of the first partition is consistent with the feature value of the corresponding partition in the previous image frame; increase the integral value of change corresponding to the first partition when the feature value of the first partition is inconsistent with the feature value of the corresponding partition in the previous image frame; wherein, the first partition is any one of the plurality of partitions.

[0007] In some embodiments of this disclosure, the change integral value is an integral score, and the partition granularity adjustment condition includes a partition merging condition and a partition splitting condition; wherein, the partition merging condition is that the integral scores of two adjacent partitions are both less than or equal to a first threshold, and the sum of the partition granularities of the two adjacent partitions is less than or equal to the maximum partition granularity; the partition splitting condition is that the integral score is greater than or equal to a second threshold and the partition granularity is greater than the minimum partition granularity, and the first threshold is less than the second threshold; the controller is further configured to: obtain a target integral score corresponding to a second partition, the second partition being any one of the plurality of partitions; if, when the target integral score is less than or equal to the first threshold, the integral score corresponding to a third partition adjacent to the second partition is also less than or equal to the first threshold, and the sum of the partition granularities of the second partition and the third partition is less than or equal to the maximum partition granularity, then determine that the second partition and the third partition satisfy the partition merging condition; if, when the target integral score is greater than or equal to the second threshold, the partition granularity of the second partition is greater than the minimum partition granularity, then determine that the second partition satisfies the partition splitting condition.

[0008] In some embodiments of this disclosure, the change integral value is the change count value, the initial value of the change count value corresponding to each partition is zero, and the partition granularity adjustment conditions include partition merging conditions and partition splitting conditions; wherein, the partition merging condition is that in two adjacent partitions of consecutive image frames of a first frame number, the feature values ​​of the same partition are consistent, and the sum of the partition granularities of the two adjacent partitions is less than or equal to the maximum partition granularity; the partition splitting condition is that in the feature values ​​of the same partition of consecutive image frames of a second frame number, any two adjacent feature values ​​are inconsistent, and the partition granularity is greater than the minimum partition granularity; the controller is specifically configured to: obtain the target change count value corresponding to the second partition, where the second partition is any one of the plurality of partitions; in the target change When the count value is zero, the historical image frames are sequentially queried backwards from the current image frame to obtain the number of first image frames containing the second partition in the first consecutive image frames including the current image frame, and the number of second consecutive image frames in the second consecutive image frames whose feature values ​​of the third partition are consistent with the feature values ​​of the third partition in the current image frame, wherein the third partition is the partition adjacent to the second partition, and the second consecutive image frames include the current image frame; if the number of the first image frames and the number of the second image frames are both greater than or equal to the number of the first frames, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, it is determined that the second partition and the third partition satisfy the partition merging condition.

[0009] In some embodiments of this disclosure, the controller is further configured to: if, when the target change count value is not zero, in the third consecutive image frames of the first frame number including the current image frame, the feature value of the second partition is consistent and the feature value of the third partition adjacent to the second partition is also consistent, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then determine that the second partition and the third partition satisfy the partition merging condition; if, when the feature value of the second partition is inconsistent in the third consecutive image frames, in the fourth consecutive image frames of the second frame number including the current image frame, the feature values ​​of the second partition of any two adjacent image frames are inconsistent, and the partition granularity of the second partition is greater than the minimum partition granularity, then determine that the second partition satisfies the partition splitting condition.

[0010] In some embodiments of this disclosure, the current partition granularity table is used to record the starting row and partition granularity of different partitions; the controller is specifically configured to: when it is determined that the second partition and the third partition meet the partition merging condition, based on the starting row of the second partition and the third partition, determine the corresponding partitions of the second partition and the third partition in the current partition granularity table, and merge the partition granularity of the corresponding partitions to obtain the adjusted partition granularity table; when it is determined that the second partition meets the partition splitting condition, based on the starting row of the second partition, split the corresponding partition of the second partition in the current partition granularity table into two partitions, and determine the starting row and partition granularity of the two partitions respectively to obtain the adjusted partition granularity table.

[0011] In some embodiments of this disclosure, the controller is specifically configured to: after partitioning the next image frame according to the adjusted partition granularity table, determine and store a first feature value corresponding to each partition, the first feature value being used for partition feature comparison with subsequent image frames of the next image frame; partition the next image frame according to the current partition granularity table and determine and store a second feature value corresponding to each partition, the second feature value being used for partition feature comparison with the current image frame.

[0012] In some embodiments of this disclosure, the controller is specifically configured to: when the still frame detection mode is row mode or column mode, after completing the still frame detection of each image frame, determine the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion degree between dynamic partitions based on the dynamic partitions of the target image frame that has been detected this time, wherein the dynamic partition refers to a partition whose feature value is inconsistent with the feature value of the corresponding partition in the previous image frame; if the dynamic row / column ratio is less than or equal to the ratio threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the dispersion degree is greater than the dispersion threshold, determine that the switching condition corresponding to the partition mode is met; and switch the still frame detection mode to the partition mode; wherein the dynamic row / column ratio represents the ratio of the number of rows / columns of all the dynamic partitions in the target image frame to the total number of rows / columns of the target image frame; the number of consecutive dynamic partition rows / columns represents the number of rows / columns covered by the longest continuous band formed by the consecutive dynamic partitions in the target image frame; and the dispersion degree represents the average interval between the dynamic partitions in the target image frame.

[0013] In some embodiments of this disclosure, the controller is specifically configured to switch the still frame detection mode to the partition mode when all consecutive image frames of the third frame number meet the switching conditions.

[0014] Secondly, embodiments of this disclosure provide an image still frame detection method applied to a display device, comprising: in a partitioning mode, partitioning the current image frame displayed on the display of the display device in a direction consistent with the refresh direction of the display according to a current partitioning granularity table, to obtain multiple partitions; determining the feature value of each partition; comparing the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and updating the change integral value corresponding to each partition according to the comparison result, wherein the change integral value is used to represent the change information of the feature value of the corresponding partition; determining whether each partition meets the partitioning granularity adjustment condition based on the change integral value, and if at least one partition meets the partitioning granularity adjustment condition, determining an adjusted partitioning granularity table based on the partitioning granularity of the target partition that meets the partitioning granularity adjustment condition and the current partitioning granularity table; and partitioning the next image frame according to the adjusted partitioning granularity table when the display displays the next image frame.

[0015] Thirdly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the image still frame detection method as described in the second aspect.

[0016] Fourthly, embodiments of this disclosure provide a computer program product that, when run on a computer, enables the computer to implement the image still frame detection method as described in the second aspect.

[0017] As can be seen from the above technical solutions, the display device and image still frame detection method provided in this disclosure, in partition mode, partitions the current image frame displayed on the display device in the direction consistent with the refresh direction according to the current partition granularity table, obtaining multiple partitions, and determining the feature value of each partition; then, comparing the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and updating the change integral value corresponding to each partition according to the comparison result, the change integral value is used to represent the change information of the feature value of the corresponding partition; based on the change integral value, it is determined whether each partition meets the partition granularity adjustment condition, and if at least one partition meets the partition granularity adjustment condition, based on the partition granularity of the target partition that meets the partition granularity adjustment condition and the current partition granularity table, the adjusted partition granularity table is determined, and then when the display displays the next image frame, the next image frame is partitioned according to the adjusted partition granularity table. Since the embodiment of this disclosure uses the change of integral value to represent the change information of feature value of corresponding partition, when at least one partition meets the partition granularity adjustment condition, the partition granularity table is updated to obtain the adjusted partition granularity table. The adjusted partition granularity table is used for partitioning of subsequent image frames. Thus, the partition granularity of the partition is dynamically adjusted by analyzing the historical changes of the feature value of the partition, realizing the adaptive adjustment and automatic optimization of the partition granularity. This enables the size of the detection partition to be automatically optimized according to the actual image changes during still frame detection, thereby ensuring the sensitivity of still frame detection and improving the refresh efficiency of partitions that have changed. Attached Figure Description

[0018] To more clearly illustrate the implementation methods in the embodiments of this disclosure or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings.

[0019] Figure 1 An operational scenario between a display device and a control device according to some embodiments is illustrated;

[0020] Figure 2 A hardware configuration block diagram of a control device 100 according to some embodiments is shown;

[0021] Figure 3 A hardware configuration block diagram of a display device 200 according to some embodiments is shown;

[0022] Figure 4 One of the schematic flowcharts of an image still frame detection method according to some embodiments is shown;

[0023] Figure 5A schematic diagram showing a portion of the current partition granularity table for row partitioning in an exemplary embodiment of this disclosure is illustrated.

[0024] Figure 6 A second schematic flowchart of an image still frame detection method according to some embodiments is shown;

[0025] Figure 7 The third schematic flowchart of an image still frame detection method according to some embodiments is shown;

[0026] Figure 8 A schematic diagram showing the list of image frames is provided.

[0027] Figure 9 A fourth schematic flowchart of an image still frame detection method according to some embodiments is shown;

[0028] Figure 10 Fifth of a series of schematic flowcharts illustrating an image still frame detection method according to some embodiments;

[0029] Figure 11 One of the schematic diagrams of an adjusted partitioning granularity table according to an exemplary embodiment of the present disclosure is shown;

[0030] Figure 12 A second schematic diagram of an adjusted partitioning granularity table according to an exemplary embodiment of the present disclosure is shown;

[0031] Figure 13 A third schematic diagram of an adjusted partitioning granularity table of an exemplary embodiment of the present disclosure is shown;

[0032] Figure 14 A flowchart of an image still frame detection method according to some embodiments is shown in diagram six.

[0033] Figure 15 The seventh schematic diagram of an image still frame detection method according to some embodiments is shown. Detailed Implementation

[0034] To make the objectives and implementation methods of this disclosure clearer, the exemplary embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this disclosure. Obviously, the exemplary embodiments described are only some embodiments of this disclosure, and not all embodiments.

[0035] It should be noted that the brief descriptions of terms in this disclosure are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this disclosure. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0036] The terms "first," "second," "third," etc., used in this disclosure, in the specification, claims, and accompanying drawings are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0037] The terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0038] Currently, the most common still frame detection solutions in the industry include the following categories:

[0039] (1) Frame buffer comparison method. This still frame detection method determines the still area by saving the complete pixel data of the previous frame and comparing it with the current frame line by line or pixel by pixel. This method has high detection accuracy, but it has extremely high requirements for storage resources and bandwidth, making it difficult to apply in low-cost or resource-constrained devices. In particular, with the increase in display resolution, the storage requirements and access bandwidth of the frame buffer have increased significantly, leading to increased hardware costs and system complexity. It has become a system bottleneck, especially in resource-constrained scenarios such as embedded devices and low-power terminals.

[0040] (2) Fixed-partition still frame detection method. This still frame detection method divides the image frame into several fixed-size partitions (such as 8 rows or 16 rows), and performs still frame detection on each partition separately. The partition granularity of this method is preset and cannot be dynamically adjusted according to the image content, which easily leads to the problem of difficulty in balancing detection accuracy and refresh efficiency.

[0041] (3) Feature value / hash comparison method. This still frame detection method calculates feature values ​​for each partition or row and only saves the feature values ​​for comparison, reducing storage requirements. However, the partition size is usually fixed and cannot adaptively adjust the partition granularity, which limits the detection sensitivity and efficiency.

[0042] (4) Row / column mode refresh method. This still frame detection method performs still frame detection and refresh in a fixed row or column. It is suitable for scenarios with many static areas, but the refresh efficiency is not high when the dynamic areas are unevenly distributed.

[0043] It is evident that current still frame detection methods have the following shortcomings: they rely heavily on frame buffers, resulting in high resource consumption; the partition granularity is fixed, making adaptive optimization impossible; and it is difficult to balance detection and refresh efficiency, making it hard to meet the needs of high-performance, low-power applications.

[0044] To address the aforementioned issues, this disclosure provides an adaptive image still frame detection scheme for display devices or video systems, enabling efficient and accurate still frame detection and refresh area determination without the need for frame buffering. This scheme dynamically adjusts the number of rows / columns (i.e., partition granularity) of each partition by analyzing historical changes in partitions in real time, achieving automatic optimization of partition granularity. Specifically, the size of the partition can be automatically expanded or shrunk based on changes in previous frames, and the detection area is automatically optimized based on actual image changes, achieving adaptive adjustment of partition granularity and thus balancing detection accuracy and refresh efficiency. Furthermore, this disclosure only needs to store the feature values ​​of each partition (such as cyclic redundancy check values ​​or hash values) without storing the complete pixel data of the previous frame, completely eliminating reliance on frame buffering and significantly reducing hardware resource consumption, making it suitable for resource-constrained scenarios. In this scheme, partition merging and splitting are both based on feature value change statistics, flexibly responding to dynamic changes in image content, ensuring both still frame detection sensitivity and improved refresh efficiency. The proposed solution for still frame detection is highly efficient in both detection and refresh processes. It can quickly locate dynamic areas where content changes, reduce invalid refreshes, and significantly improve system performance and energy efficiency. Furthermore, this solution is highly adaptable, automatically switching between row / column and partition modes based on different application scenarios and image content to further optimize the overall effect. The solution is simple to implement, easy to integrate with hardware and software, and readily applicable.

[0045] The display device provided in this disclosure can have various implementation forms, such as a television, a smart television, a laser projection device, a monitor, an electronic bulletin board, an electronic table, a mobile phone, a tablet computer, a laptop computer, a handheld computer, an in-vehicle electronic device, etc.

[0046] Figure 1 This is a schematic diagram illustrating an operational scenario between a display device and a control device according to an embodiment, wherein the control device includes a smart device or a control apparatus. Figure 1 As shown, the user can operate the display device 200 through the smart device 300 or the control device 100.

[0047] In some embodiments, the control device 100 may be a remote control. Communication between the remote control and the display device includes infrared protocol communication, Bluetooth protocol communication, and other short-range communication methods, controlling the display device 200 wirelessly or via wired means. Users can control the display device 200 by inputting user commands through buttons on the remote control, voice input, control panel input, etc.

[0048] In some embodiments, a smart device 300 (such as a mobile terminal, tablet computer, computer, laptop computer, etc.) can also be used to control the display device 200. For example, an application running on the smart device can be used to control the display device 200.

[0049] In some embodiments, the display device may receive instructions not through the aforementioned smart devices or control devices, but through touch or gestures.

[0050] In some embodiments, the display device 200 can also be controlled in ways other than the control device 100 and the smart device 300. For example, it can be controlled by directly receiving the user's voice commands through a module configured inside the display device 200 for acquiring voice commands, or it can be controlled by receiving the user's voice commands through a voice control device set outside the display device 200.

[0051] In some embodiments, the display device 200 also communicates with the server 400. The display device 200 may communicate via a local area network (LAN), wireless local area network (WLAN), and other networks. The server 400 may provide various content and interactive features to the display device 200. The server 400 may be a cluster or multiple clusters, and may include one or more types of servers.

[0052] Figure 2 An exemplary block diagram of the configuration of the control device 100 according to an exemplary embodiment is shown. Figure 2 As shown, the control device 100 includes a controller 110, a communication interface 130, a user input / output interface 140, an external memory, and a power supply. The control device 100 can receive user input operation commands and convert the operation commands into commands that the display device 200 can recognize and respond to, thus acting as an intermediary for interaction between the user and the display device 200.

[0053] like Figure 3 The display device 200 includes at least one of the following: a tuner 210, a communicator 220, a detector 230, an external device interface 240, a controller 250, a display 260, an audio output interface 270, a user interface 280, an external memory, and a power supply.

[0054] In some embodiments, the controller 250 includes a processor, a video processor, an audio processor, a graphics processor, RAM, ROM, and a first to an nth interface for input / output.

[0055] The display 260 includes a display screen assembly for presenting images, a driving assembly for driving image display, a component for receiving image signals from the controller output, and a user control UI interface for displaying video content, image content, menu control interface, and user control UI interface.

[0056] The display 260 can be an LCD display, an OLED display, or a projection display, and can also be a projection device and a projection screen.

[0057] The communicator 220 is a component used to communicate with external devices or servers according to various communication protocol types. For example, the communicator may include at least one of the following: a Wi-Fi module, a Bluetooth module, a wired Ethernet module, other network communication protocol chips or near-field communication protocol chips, and an infrared receiver. The display device 200 can establish the transmission and reception of control signals and data signals with the external control device 100 or the server 400 through the communicator 220.

[0058] User interface 280 can be used to receive control signals from control device 100 (such as an infrared remote control). It can also be used to directly receive user input operation commands and convert the operation commands into commands that display device 200 can recognize and respond to; in this case, it can be called a user input interface.

[0059] Detector 230 is used to collect signals from the external environment or to interact with the external environment. For example, detector 230 includes a light receiver, a sensor for collecting ambient light intensity; or, detector 230 includes an image acquisition device, such as a camera, which can be used to collect external environmental scenes, user attributes, or user interaction gestures; or, detector 230 includes a sound acquisition device, such as a microphone, for receiving external sounds.

[0060] The external device interface 240 may include, but is not limited to, one or more of the following: High Definition Multimedia Interface (HDMI), analog or high-definition component input interface (component), composite video input interface (CVBS), USB input interface (USB), RGB port, etc. It may also be a composite input / output interface formed by multiple interfaces mentioned above.

[0061] The tuner / demodulator 210 receives broadcast television signals via wired or wireless means, and demodulates audio and video signals, such as EPG data signals, from multiple wireless or wired broadcast television signals.

[0062] In some embodiments, the controller 250 and the tuner 210 may be located in different separate devices, that is, the tuner 210 may also be located in an external device of the main device where the controller 250 is located, such as an external set-top box.

[0063] The controller 250 controls the operation of the display device and responds to user operations through various software control programs stored in memory (internal or external memory). The controller 250 controls the overall operation of the display device 200. For example, in response to receiving a user command to select a UI object to display on the monitor 260, the controller 250 can perform operations related to the object selected by the user command.

[0064] In some embodiments, the controller includes at least one of a central processing unit (CPU), a video processor, an audio processor, a graphics processing unit (GPU), and random access memory (RAM), read-only memory (ROM), a first to an nth interface for input / output, a communication bus, etc.

[0065] RAM, also known as main memory, is an internal memory that directly exchanges data with the controller. It can be read and written at any time (except during refresh) and is very fast, typically serving as temporary data storage for the operating system or other running programs. Its biggest difference from ROM is data volatility; data stored in RAM is lost when power is off. RAM is used in computers and digital systems to temporarily store programs, data, and intermediate results. ROM operates in a non-destructive read-only manner, allowing only reading and not writing. Once information is written, it is fixed and will not be lost even if power is cut off; therefore, it is also called fixed-function memory.

[0066] Users can input commands through a graphical user interface (GUI) displayed on the monitor 260, and the user input interface receives the user input commands through the GUI. Alternatively, users can input commands by entering specific sounds or gestures, and the user input interface receives the user input commands by recognizing the sounds or gestures through sensors.

[0067] A "user interface" is the medium through which an application or operating system interacts and exchanges information with the user. It converts information from its internal form to a form that the user can accept. A common form of user interface is the graphical user interface (GUI), which refers to a user interface related to computer operation displayed graphically. It can be an icon, window, control, or other interface element displayed on the screen of a display device. Controls can include visual interface elements such as icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, and widgets.

[0068] This disclosure belongs to the field of image processing technology, specifically protecting a strategy that dynamically adjusts the number of rows / columns in each partition by analyzing the historical change information of partitions in real time, thereby achieving automatic optimization of partition granularity.

[0069] In some embodiments of this disclosure, a display device is provided, including: a display configured to display image frames according to a preset refresh direction; and a controller connected to the display configured to: in a partitioning mode, partition the current image frame displayed on the display in a direction consistent with the refresh direction according to a current partitioning granularity table to obtain multiple partitions; determine the feature value of each partition; compare the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and update the change integral value corresponding to each partition according to the comparison result, wherein the change integral value is used to represent the change information of the feature value of the corresponding partition; determine whether each partition meets the partitioning granularity adjustment condition based on the change integral value, and if at least one partition meets the partitioning granularity adjustment condition, determine an adjusted partitioning granularity table based on the partitioning granularity of the target partition that meets the partitioning granularity adjustment condition and the current partitioning granularity table; and partition the next image frame according to the adjusted partitioning granularity table when the display displays the next image frame.

[0070] In some embodiments of this disclosure, the controller is configured to: decrease the integral value of change corresponding to the first partition or keep the integral value of change corresponding to the first partition unchanged when the feature value of the first partition is consistent with the feature value of the corresponding partition in the previous image frame; increase the integral value of change corresponding to the first partition when the feature value of the first partition is inconsistent with the feature value of the corresponding partition in the previous image frame; wherein, the first partition is any one of a plurality of partitions.

[0071] In some embodiments of this disclosure, the change integral value is the integral score, and the partition granularity adjustment conditions include partition merging conditions and partition splitting conditions. The partition merging condition is that the integral scores of two adjacent partitions are both less than or equal to a first threshold, and the sum of the partition granularities of the two adjacent partitions is less than or equal to the maximum partition granularity. The partition splitting condition is that the integral score is greater than or equal to a second threshold, and the partition granularity is greater than the minimum partition granularity, where the first threshold is less than the second threshold. The controller is further configured to: obtain the target integral score corresponding to a second partition, where the second partition is any one of multiple partitions; if, when the target integral score is less than or equal to the first threshold, the integral score corresponding to a third partition adjacent to the second partition is also less than or equal to the first threshold, and the sum of the partition granularities of the second and third partitions is less than or equal to the maximum partition granularity, then determine that the second and third partitions satisfy the partition merging condition; if, when the target integral score is greater than or equal to the second threshold, the partition granularity of the second partition is greater than the minimum partition granularity, then determine that the second partition satisfies the partition splitting condition.

[0072] In some embodiments of this disclosure, the change integral value is the change count value, the initial value of the change count value corresponding to each partition is zero, and the partition granularity adjustment conditions include partition merging conditions and partition splitting conditions; wherein, the partition merging condition is that in two adjacent partitions of consecutive image frames of the first frame number, the feature values ​​of the same partition are consistent, and the sum of the partition granularities of the two adjacent partitions is less than or equal to the maximum partition granularity; the partition splitting condition is that in the feature values ​​of the same partition of consecutive image frames of the second frame number, any two adjacent feature values ​​are inconsistent, and the partition granularity is greater than the minimum partition granularity; the controller is specifically configured to: obtain the target change count value corresponding to the second partition, wherein the second partition is any one of multiple partitions. Partition 1; When the target change count is zero, start from the current image frame and sequentially query historical image frames backward to obtain the number of first image frames containing the second partition in the first consecutive image frames including the current image frame, and obtain the number of second consecutive image frames in the second consecutive image frames whose feature values ​​of the third partition are consistent with the feature values ​​of the third partition in the current image frame. The third partition is the partition adjacent to the second partition, and the second consecutive image frames include the current image frame. If the number of first image frames and the number of second image frames are both greater than or equal to the number of first frames, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, it is determined that the second partition and the third partition meet the partition merging condition.

[0073] In some embodiments of this disclosure, the controller is further configured to: when the target change count value is not zero, if in a third consecutive image frame including the current image frame, the feature values ​​of the second partition are consistent and the feature values ​​of the third partition adjacent to the second partition are also consistent, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then determine that the second partition and the third partition meet the partition merging condition; if the feature values ​​of the second partition are inconsistent in a third consecutive image frame, if in a fourth consecutive image frame including the current image frame, the feature values ​​of the second partition of any two adjacent image frames are inconsistent, and the partition granularity of the second partition is greater than the minimum partition granularity, then determine that the second partition meets the partition splitting condition.

[0074] In some embodiments of this disclosure, the current partition granularity table is used to record the starting row and partition granularity of different partitions; the controller is specifically configured to: when it is determined that the second partition and the third partition meet the partition merging condition, based on the starting row of the second partition and the third partition, determine the corresponding partitions of the second partition and the third partition in the current partition granularity table, and merge the partition granularity of the corresponding partitions to obtain an adjusted partition granularity table; when it is determined that the second partition meets the partition splitting condition, based on the starting row of the second partition, split the corresponding partition of the second partition in the current partition granularity table into two partitions, and determine the starting row and partition granularity of the two partitions respectively to obtain an adjusted partition granularity table.

[0075] In some embodiments of this disclosure, the controller is specifically configured to: after partitioning the next image frame according to the adjusted partition granularity table, determine and store a first feature value corresponding to each partition, the first feature value being used for partition feature comparison with subsequent image frames of the next image frame; partition the next image frame according to the current partition granularity table and determine and store a second feature value corresponding to each partition, the second feature value being used for partition feature comparison with the current image frame.

[0076] In some embodiments of this disclosure, the controller is specifically configured to: when the still frame detection mode is row mode or column mode, after completing the still frame detection of each image frame, determine the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion between dynamic partitions based on the dynamic partitions of the target image frame that has been detected this time, wherein a dynamic partition refers to a partition whose feature value is inconsistent with the feature value of the corresponding partition in the previous image frame; if the dynamic row / column ratio is less than or equal to the ratio threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the dispersion is greater than the dispersion threshold, determine that the switching conditions corresponding to the partition mode are met; and switch the still frame detection mode to the partition mode; wherein the dynamic row / column ratio represents the ratio of the number of rows / columns of all dynamic partitions in the target image frame to the total number of rows / columns of the target image frame; the number of consecutive dynamic partition rows / columns represents the number of rows / columns covered by the longest continuous band formed by consecutive dynamic partitions in the target image frame; and the dispersion represents the average interval between dynamic partitions in the target image frame.

[0077] In some embodiments of this disclosure, the controller is further configured to switch the still frame detection mode to a partitioned mode when all consecutive image frames of the third frame number meet the switching conditions. The display device provided in this disclosure, in partitioned mode, partitions the current image frame displayed on the display device's screen in the same direction as the refresh direction according to the current partition granularity table, obtaining multiple partitions, and determining the feature value of each partition; then, comparing the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and updating the change integral value corresponding to each partition based on the comparison result, the change integral value representing the change information of the feature value of the corresponding partition; determining whether each partition meets the partition granularity adjustment conditions based on the change integral value, and if at least one partition meets the partition granularity adjustment conditions, determining an adjusted partition granularity table based on the partition granularity of the target partition that meets the partition granularity adjustment conditions and the current partition granularity table, and then partitioning the next image frame according to the adjusted partition granularity table when the display displays the next image frame. Since the embodiment of this disclosure uses the change of integral value to represent the change information of feature value of corresponding partition, when at least one partition meets the partition granularity adjustment condition, the partition granularity table is updated to obtain the adjusted partition granularity table. The adjusted partition granularity table is used for partitioning of subsequent image frames. Thus, the partition granularity of the partition is dynamically adjusted by analyzing the historical changes of the feature value of the partition, realizing the adaptive adjustment and automatic optimization of the partition granularity. This enables the size of the detection partition to be automatically optimized according to the actual image changes during still frame detection, thereby ensuring the sensitivity of still frame detection and improving the refresh efficiency of partitions that have changed.

[0078] In this embodiment of the disclosure, the descriptions of the features and technical effects of the various operations configured to be performed by the controller of the display device can be found in the descriptions of the image still frame detection method in the following embodiments, and will not be repeated here.

[0079] To illustrate this solution in more detail, the following will use examples to illustrate it. Figures 4 to 15 To explain, it is understandable that Figures 4 to 15 The steps involved may include more or fewer steps in actual implementation, and the order of these steps may also differ, as long as the image still frame detection method provided in the embodiments of this disclosure can be achieved. The executing entity of the image still frame detection method can be a display device, or a functional module or entity within the display device that can implement the image still frame detection method, such as a controller in the display device, which is not limited here. Moreover, the specific description of the image still frame detection method provided in the embodiments of this disclosure is also applicable to the aforementioned display device, and can achieve the same or similar technical effects.

[0080] Figure 4A flowchart illustrating an image still frame detection method according to some embodiments is shown, such as... Figure 4 As shown, the image still frame detection method may include the following steps:

[0081] Step 401: In partition mode, the current image frame displayed on the display device is partitioned in the direction consistent with the refresh direction of the display according to the current partition granularity table, resulting in multiple partitions.

[0082] The refresh direction refers to the direction in which the display refreshes the image frame, including the row direction and the column direction. For factory-installed display devices, the refresh direction is fixed as either the row direction or the column direction. In this embodiment, the partition mode in the still frame detection mode is different from the row mode and the column mode. In the partition mode, the image frame is divided into several partitions according to rows or columns. Each partition contains several consecutive rows or columns and is aligned with the minimum refresh granularity of the display / bus (for example, with 1 row as the minimum alignment unit, a partition includes at least one row). Furthermore, in this embodiment, when the still frame detection mode is the partition mode, the granularity of the partition (i.e., the partition height or partition width) can be dynamically adjusted according to the historical change information of the partition. It can be merged into a larger partition or split into a smaller partition. Merging is only performed when adjacent partitions meet the static condition, and splitting is only performed when the partition is continuously dynamic and its height / width is greater than the minimum granularity.

[0083] In this embodiment of the disclosure, when the still frame detection mode is the partition mode, for the currently displayed image frame (referred to as the current image frame), the current image frame is partitioned in the same direction as the refresh direction according to the current partition granularity table, resulting in multiple partitions, so that still frame detection is performed according to the partitions. For example, if the refresh direction is the row direction, the current image frame is partitioned in the row direction according to the current partition granularity table, resulting in multiple partitions, and each partition includes at least one row. Correspondingly, if the refresh direction is the column direction, the current image frame is partitioned in the column direction according to the current partition granularity table, resulting in multiple partitions, and each partition includes at least one column.

[0084] It is understandable that the current partition granularity table is used to record the starting row and partition granularity of different partitions. For row-based partitioning, the sum of the partition granularities of all partitions in the current partition granularity table is the same as the total number of rows contained in the image frame; for column-based partitioning, the sum of the partition granularities of all partitions in the current partition granularity table is the same as the total number of columns contained in the image frame. For example, assuming the resolution of the image frame displayed on the monitor is 1920*1080, and the monitor's refresh direction is in the row direction... Figure 5 A schematic diagram illustrating a portion of the current partition granularity table for row partitioning, as shown in an exemplary embodiment of this disclosure, is provided. Figure 5 As shown, the current partition granularity table contains the starting rows and partition granularities of 150 partitions, then according to... Figure 5 The current partition granularity table shown divides the current image frame into 150 partitions starting from the first row. The first partition has a granularity of 10, and the last partition has a granularity of 12.

[0085] Step 402: Determine the feature value of each partition.

[0086] In this embodiment of the disclosure, for each partition, its pixel feature value is calculated in real time to obtain the feature value corresponding to each partition. The feature value of each partition is used to determine whether the content of the partition has changed.

[0087] To improve robustness, there are multiple ways to determine feature values. Specifically, the feature values ​​of each partition can be determined by using the corresponding algorithm based on the display requirements selected by the user.

[0088] For example, users can choose appropriate display requirements based on various needs such as the memory size of their display device and the detection intensity. For instance, in scenarios with limited memory, users can choose strong channel display requirements, with the corresponding feature value calculation algorithm being Cyclic Redundancy Check (CRC) or Rolling Hash (streaming, pixel-by-pixel / word-by-word update). If the memory is large, users can choose auxiliary channel display requirements, with the corresponding feature value calculation algorithm being differential operation. If the memory is sufficient and the detection intensity requirement is high, users can choose a combination of CRC / rolling hash and differential operation to determine the feature value.

[0089] CRC is a commonly used data integrity verification algorithm. Its core is to perform the following iterative calculation on each byte in the data stream:

[0090] Let the initial value be crc0, the data flow be D = [d1, d2, ..., dn], the polynomial be P, and the iterative formula be: .

[0091] The formula for calculating rolling hash is:

[0092] ;

[0093] Where seed is the hash seed, and Q is the modulus. P i,j Let be the pixel value in the i-th row and j-th column. H prev This represents the hash value (feature value) of the pixel data (the data updated last time) of a certain partition in the current image frame before this calculation. H currThis represents the hash value (feature value) calculated for this partition based on the current data (pixel data after this update) in the current image frame. N represents the number of rows in the partition, and M represents the number of columns in the partition.

[0094] In some embodiments, multi-channel verification (CRC + 64-bit rolling hash) can be performed on important segments to reduce false positives.

[0095] For auxiliary channel display requirements, differential operations can be used to calculate the differential feature values ​​of pixels within a partition, making it more sensitive to small changes. This calculation method is sensitive to slight variations (noise, micro-jitter, slight brightness fluctuations) and provides continuous measurement, supporting threshold + hysteresis stable decision-making. Considering that this scheme has a higher false detection rate compared to CRC, in practical applications, a multi-channel verification method can be selected based on memory availability.

[0096] The difference operation calculates the absolute difference between adjacent pixels within a block (partition), reflecting local dynamic changes. The core idea is to calculate the difference between all adjacent pixels within the block, take the absolute value, and then calculate the average. The specific calculation formula is shown below:

[0097] ;

[0098] In the above formula, d i Indicates the first in the partition i 1 pixel, d i-1 Indicates the first in the partition i- 1 pixel N This represents the total number of pixels within the partition.

[0099] The feature values ​​of the partitions are calculated using differential operations. The feature value calculation only depends on the current image frame and does not require saving the complete data of the previous frame. Only the feature values ​​of each partition in the previous frame need to be saved.

[0100] Step 403: Compare the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and update the change integral value corresponding to each partition according to the comparison result. The change integral value is used to represent the change information of the feature value of the corresponding partition.

[0101] In this embodiment, an integral transformation value is maintained for each partition to represent the change information of the feature value of the corresponding partition. After the feature value of each partition in the current image frame is determined, the feature value of each partition can be compared with the feature value of the corresponding partition in the previous image frame, and the change integral value corresponding to each partition is updated according to the comparison result.

[0102] In some embodiments, if the feature value of the first partition is consistent with the feature value of the corresponding partition in the previous image frame, the integral value of change corresponding to the first partition is decreased or kept unchanged; if the feature value of the first partition is inconsistent with the feature value of the corresponding partition in the previous image frame, the integral value of change corresponding to the first partition is increased. Here, the first partition is any one of multiple partitions, and the previous image frame is the image frame preceding the current image frame. That is, for each partition in the current image frame, its feature value is compared with the feature value of the corresponding partition in the previous image frame. For example, if the current partition is the first partition in the current image frame, its feature value is compared with the feature value of the first partition in the previous image frame. If they are consistent, the integral value of change for that partition is decreased or kept unchanged; if they are inconsistent, the integral value of change for that partition is increased.

[0103] For example, the change integral value can be an integral fraction, and the change integral value is increased by α when the feature value matching is inconsistent, and decreased by β when the feature value matching is consistent.

[0104] For example, the change integral value can be a change count value. A change counter can be maintained for each partition, with an initial value of 0. Each time the feature values ​​are inconsistent, the value of the change counter is accumulated to 1, resulting in a new change count value.

[0105] In some embodiments, for a partition in the current image frame, if it is determined that the feature value of the partition in the current image frame is inconsistent with the feature value in the previous image frame, then the partition is determined to be a dynamic partition; otherwise, the partition is determined to be a static partition. For dynamic partitions, a refresh signal is issued to refresh the dynamic partitions, while for static partitions, the refresh is skipped.

[0106] In this embodiment of the disclosure, after each frame ends, the feature values ​​of each partition in the frame are saved and the change integral values ​​of each partition are updated to prepare for the next frame.

[0107] Step 404: Determine whether each partition meets the partition granularity adjustment conditions based on the change integral value, and if at least one partition meets the partition granularity adjustment conditions, determine the adjusted partition granularity table based on the partition granularity of the target partition that meets the partition granularity adjustment conditions and the current partition granularity table.

[0108] In this embodiment of the disclosure, after updating the change integral values ​​corresponding to each partition in the current image frame, it can be determined whether each partition meets the partition granularity adjustment conditions based on the change integral values. If at least one partition in the current image frame meets the partition granularity adjustment conditions, the adjusted partition granularity table is determined based on the partition granularity of the target partition that meets the partition granularity adjustment conditions and the current partition granularity table. For example, when the change integral value of a partition indicates that the content of the partition changes frequently, the partition in the current partition granularity table is split into two smaller partitions. The sum of the partition granularity of the two smaller partitions is the same as the partition granularity of the original partition. Therefore, the adjusted partition granularity table adds the information of one partition compared to the original partition granularity table.

[0109] In some embodiments, the partition granularity is dynamically adjusted by statistically analyzing changes in partition feature values: if a partition remains unchanged for several consecutive frames, and its adjacent partitions also meet the static threshold (i.e., no change for several consecutive frames), the partition can be expanded (merged with adjacent static partitions) to reduce detection granularity and improve refresh efficiency; if a partition changes frequently, the partition is reduced (by decreasing the number of rows or splitting into two parts) to improve detection accuracy. Therefore, in this embodiment, partition adjustment relies solely on historical changes in partition feature values ​​and does not require frame buffering. To avoid jitter, hysteresis is introduced: merging and splitting use different trigger thresholds, and newly merged / split partitions enter a short cooling-off period and do not participate in further structural adjustments. In other words, newly merged or split partitions will not be merged or split again temporarily.

[0110] It should be noted that in this embodiment of the disclosure, the merging and splitting of the partition structure take effect uniformly at the end of the frame, and the updated and adjusted partition granularity table is used for the partitioning of the next image frame to avoid the impact of intra-frame structural changes on streaming computation.

[0111] Step 405: When the display shows the next image frame, partition the next image frame according to the adjusted partition granularity table.

[0112] In this embodiment, the adjusted partition granularity table only takes effect when the next image frame of the current image frame is displayed. When the display shows the next image frame, it is partitioned according to the adjusted partition granularity table to obtain multiple partitions. Then, the feature value of each partition is calculated and compared with the feature value of the corresponding partition in the previous image frame (i.e., the aforementioned current image frame). The change integral value of each partition is updated according to the comparison result. It is determined whether each partition meets the partition granularity adjustment condition based on the change integral value. If no partition meets the partition granularity adjustment condition, when the display shows the next image frame (i.e., the image frame immediately following the next image frame), the next image frame is still partitioned according to the adjusted partition granularity table to perform still frame detection on the next image frame. Otherwise, the partition granularity table is adjusted again to obtain a new table, and the next image frame is partitioned according to the new table to perform still frame detection on the next image frame. The above process is repeated until the still frame detection mode switches to row mode or column mode, or until the display stops displaying image frames.

[0113] The image still frame detection method of this disclosure, in partition mode, partitions the current image frame displayed on the display device in the same direction as the refresh direction according to the current partition granularity table, obtaining multiple partitions, and determining the feature value of each partition; then, comparing the feature value of each partition with the feature value of the corresponding partition in the previous image frame, and updating the change integral value corresponding to each partition according to the comparison result, the change integral value is used to represent the change information of the feature value of the corresponding partition; based on the change integral value, it is determined whether each partition meets the partition granularity adjustment condition, and if at least one partition meets the partition granularity adjustment condition, based on the partition granularity of the target partition that meets the partition granularity adjustment condition and the current partition granularity table, an adjusted partition granularity table is determined, and then when the display displays the next image frame, the next image frame is partitioned according to the adjusted partition granularity table. Since the embodiment of this disclosure uses the change of integral value to represent the change information of feature value of corresponding partition, when at least one partition meets the partition granularity adjustment condition, the partition granularity table is updated to obtain the adjusted partition granularity table. The adjusted partition granularity table is used for partitioning of subsequent image frames. Thus, the partition granularity of the partition is dynamically adjusted by analyzing the historical changes of the feature value of the partition, realizing the adaptive adjustment and automatic optimization of the partition granularity. This enables the size of the detection partition to be automatically optimized according to the actual image changes during still frame detection, thereby ensuring the sensitivity of still frame detection and improving the refresh efficiency of partitions that have changed.

[0114] In some embodiments, the integral value is the integral score. The integral score is increased when the feature values ​​of the partitions are inconsistent, and decreased or kept constant otherwise. In other words, a larger integral score indicates more frequent changes in the content of that partition, and a smaller integral score indicates less frequent changes. Partition granularity adjustment conditions include partition merging conditions and partition splitting conditions. The partition merging condition is that the scores of two adjacent partitions are both less than or equal to a first threshold, and the sum of the partition granularities of two adjacent partitions is less than or equal to the maximum partition granularity. The specific value of the maximum partition granularity can be set according to actual needs. For example, taking partitioning in the row direction as an example, the maximum partition granularity can be set to 16 rows or the upper limit allowed by the panel (i.e., the display). That is, when partitioning in the row direction, the number of rows contained in a partition does not exceed the maximum partition granularity. The partition splitting condition is that the score is greater than or equal to a second threshold and the partition granularity is greater than the minimum partition granularity. The specific value of the minimum partition granularity can be set according to actual needs. For example, taking partitioning in the row direction as an example, the minimum partition granularity can be set to be consistent with the panel refresh granularity (e.g., 1 row). In some scenarios, the minimum partition granularity is allowed to be slightly larger than the panel refresh granularity (e.g., 2 rows), but it is recommended to align with the minimum refresh granularity of the panel. In this embodiment, the first threshold is less than the second threshold. The specific values ​​of the first threshold and the second threshold can be set according to actual needs, and this disclosure does not impose any restrictions on them. Based on this, as Figure 6 As shown, based on the aforementioned embodiments, step 404, which determines whether each partition meets the partition granularity adjustment conditions based on the change integral value, may include the following sub-steps:

[0115] Step 501: Obtain the target score corresponding to the second partition, which can be any one of multiple partitions.

[0116] In this embodiment of the disclosure, for any partition on the current image frame (referred to as the second partition for ease of description and distinction), its corresponding integral score (referred to as the target integral score) can be obtained. Based on the target integral score, it is determined whether the second partition meets the partition merging condition or the partition splitting condition, so that when the corresponding condition is met, the second partition can be merged with other partitions or the second partition can be split.

[0117] Step 502: If the target integral score is less than or equal to the first threshold, and the integral score of the third partition adjacent to the second partition is also less than or equal to the first threshold, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then the second partition and the third partition are determined to meet the partition merging condition.

[0118] In this embodiment of the disclosure, if the target integral score of the second partition is less than or equal to the first threshold, it can be determined that the second partition remains continuously static, that is, the content of multiple consecutive frames remains unchanged. At this time, it can be further determined whether the integral score of the adjacent partition (called the third partition) is also less than or equal to the first threshold. If so, it can be determined that both the second partition and the third partition meet the condition of being static for multiple consecutive frames. Then, the sum of the partition granularity corresponding to the second partition and the third partition is further determined to obtain the sum of the partition granularity of the two. If the sum of the partition granularity is less than or equal to the maximum partition granularity, it can be determined that the second partition and the third partition meet the partition merging condition. When updating the partition granularity table, the second partition and the third partition are merged into one partition.

[0119] Step 503: If the target integral score is greater than or equal to the second threshold, and the partition granularity of the second partition is greater than the minimum partition granularity, then the second partition is determined to meet the partition splitting condition.

[0120] In this embodiment of the disclosure, if the target integral score of the second partition is greater than or equal to the second threshold, it can be determined that the content of the second partition changes frequently. In this case, it can be further determined whether the partition granularity of the second partition is greater than the minimum partition granularity. If it is greater than the minimum partition granularity, it is determined that the second partition meets the partition splitting condition.

[0121] The image still frame detection method of this disclosure obtains the integral score of any partition while maintaining a corresponding integral score for each partition. If the integral score is less than or equal to a first threshold, and the integral score of the third partition adjacent to the second partition is also less than or equal to the first threshold, and the sum of the partition granularity of the second and third partitions is less than or equal to the maximum partition granularity, then the second and third partitions are determined to meet the partition merging condition. If the integral score is greater than or equal to a second threshold, and the partition granularity of the second partition is greater than the minimum partition granularity, then the second partition is determined to meet the partition splitting condition. Thus, it is possible to determine whether the partitions need to be merged or split based on the integral score of the partitions, achieving fast and efficient determination of whether the partition granularity needs to be adjusted, and improving convenience.

[0122] In some embodiments, the change integral value is the change count value. The initial value of the change count value corresponding to each partition is zero. For a partition, if the feature value of the partition in the current image frame is inconsistent with its feature value in the previous image frame, the change count value of the partition is accumulated to a preset value (e.g., 1); otherwise, the change count value remains unchanged. The partition granularity adjustment conditions include partition merging conditions and partition splitting conditions. The partition merging condition is that in two adjacent partitions of consecutive image frames of the first frame number, the feature values ​​of the same partition are consistent, and the sum of the partition granularities of the two adjacent partitions is less than or equal to the maximum partition granularity. The specific value of the first frame number can be set according to actual needs, for example, setting the first frame number to 3 frames. The specific value of the maximum partition granularity can be set according to actual needs. For example, taking partitioning in the row direction as an example, the maximum partition granularity can be set to 16 rows or the upper limit allowed by the panel (i.e., the display). That is, when partitioning in the row direction, the number of rows contained in a partition does not exceed the maximum partition granularity. The partitioning condition is that, within the same partition of consecutive image frames of the second frame number, any two adjacent feature values ​​are inconsistent, and the partition granularity is greater than the minimum partition granularity. The specific value of the second frame number can be set according to actual needs, for example, setting the second frame number to 2 frames. The specific value of the minimum partition granularity can also be set according to actual needs. For example, taking partitioning in the row direction as an example, the minimum partition granularity can be set to be consistent with the panel refresh granularity (e.g., 1 row). In some scenarios, the minimum partition granularity is allowed to be slightly larger than the panel refresh granularity (e.g., 2 rows). Based on this, such as Figure 7 As shown, based on the aforementioned embodiments, step 404, which determines whether each partition meets the partition granularity adjustment conditions based on the change integral value, may include the following sub-steps:

[0123] Step 601: Obtain the target change count value corresponding to the second partition, where the second partition is any one of multiple partitions.

[0124] In this embodiment of the present disclosure, for any partition on the current image frame (referred to as the second partition for ease of description and distinction), its corresponding change count value (referred to as the target change count value) can be obtained. Based on the target change count value, it can be determined whether the second partition meets the partition merging condition or the partition splitting condition, so that when the corresponding condition is met, the second partition can be merged with other partitions or the second partition can be split.

[0125] Step 602: When the target change count is zero, query the historical image frames sequentially from the current image frame to obtain the number of first image frames containing the second partition in the first consecutive image frames including the current image frame, and obtain the number of second consecutive image frames in the second consecutive image frames whose feature values ​​of the third partition are consistent with the feature values ​​of the third partition in the current image frame. The third partition is the partition adjacent to the second partition, and the second consecutive image frames include the current image frame.

[0126] In this embodiment of the disclosure, if the target change count value corresponding to the second partition is zero, it can be determined that the content of the partition has remained unchanged since the partition was divided. In this case, it can be further determined whether the number of image frames that keep the partition unchanged meets the requirement of consecutive still frames, and whether the partition adjacent to the partition (referred to as the third partition) also meets the requirement of keeping multiple consecutive still frames. It should be noted that keeping multiple consecutive still frames means that the content of the partition remains unchanged for multiple consecutive frames including the current image frame. When counting the number of image frames that keep the second partition unchanged, historical image frames can be queried backwards from the current image frame to obtain the number of image frames containing the second partition in the consecutive image frames including the current image frame (referred to as the first consecutive image frames for ease of description and distinction) (referred to as the first image frame number for ease of description and distinction). Since the content of the second partition remains unchanged, the first image frame number can be understood as the number of consecutive image frames from the division of the second partition to the end of the current image frame. To determine whether the third partition meets the requirement of a consecutive static frame count, we can sequentially query historical image frames from the current image frame backwards to obtain the number of consecutive image frames (called the second consecutive image frames) whose feature values ​​of the third partition are consistent with those of the third partition in the current image frame. This second consecutive image frame includes the current image frame, but it should be noted that this second consecutive image frame is not necessarily the same as the first consecutive image frame used to count the first image frame. In other words, the second image frame count refers to the number of image frames whose feature values ​​of the third partition remain unchanged from a certain image frame to the end of the current image frame.

[0127] For example, Figure 8 The diagram illustrates a list of image frames. Assuming the current image frame is frame t, the second partition is a newly created partition starting from frame t-1. This partition has the same feature value in frames t and t-1, and its change count is 0. Therefore, the first image frame count is the number of consecutive image frames formed by frames t and t-1, which is 2. The feature value of the third partition, adjacent to the second partition, remains unchanged from frame t-2 to frame t. Therefore, the second image frame count is the number of consecutive image frames formed by frames t-2, t-1, and t, which is 3.

[0128] Step 603: If the number of first image frames and the number of second image frames are both greater than or equal to the number of first frames, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then the second partition and the third partition are determined to meet the partition merging condition.

[0129] In this embodiment of the disclosure, after determining the number of first image frames in which the feature values ​​of the second partition have not changed continuously, and the number of second image frames in which the feature values ​​of the adjacent third partition have not changed continuously, the number of first image frames and the number of second image frames can be compared with the number of first frames respectively. If the number of first image frames and the number of second image frames are both greater than or equal to the number of first frames, it can be determined that both the second partition and the third partition meet the requirement of consecutive static frames, that is, the two adjacent partitions are static for multiple consecutive frames. In this case, the sum of the partition granularity corresponding to the second partition and the third partition can be further determined to obtain the sum of the partition granularity of the two. If the sum of the partition granularity is less than or equal to the maximum partition granularity, it can be determined that the second partition and the third partition meet the partition merging condition, and the second partition and the third partition are merged into one partition when updating the partition granularity table.

[0130] The image still frame detection method of this disclosure obtains the change count value of any partition when maintaining a corresponding change count value for each partition. If the change count value is zero, it can be determined that the content of the partition has not changed. Furthermore, if the number of consecutive still frames in the second partition and the number of consecutive still frames in the adjacent third partition are both greater than or equal to the number of first frames, and the sum of the partition granularity of the second and third partitions is less than or equal to the maximum partition granularity, then it is determined that the second and third partitions meet the partition merging condition. Thus, it realizes the determination of whether the partition meets the partition merging condition when the partition content remains unchanged, realizes the quick and efficient determination of whether the partition granularity needs to be increased, and improves convenience.

[0131] Furthermore, such as Figure 9 As shown, in Figure 7 Based on the illustrated embodiment, after step 601, the image still frame detection method of this disclosure embodiment may further include the following steps:

[0132] Step 701: If the target change count value is not zero, and in the third consecutive image frame including the current image frame, the feature value of the second partition is consistent and the feature value of the third partition adjacent to the second partition is also consistent, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then the second partition and the third partition are determined to meet the partition merging condition.

[0133] In this embodiment of the disclosure, if the target change count value of the second partition is not zero, it can be determined that its content has changed since the partition was created. In this case, it is necessary to further determine whether the feature value of the second partition has remained unchanged in the consecutive image frames containing the first frame number of the current image frame (referred to as the third consecutive image frame). For example, assuming the first frame number is 3, then... Figure 8 For example, it is necessary to determine whether the feature values ​​of the second partition are the same in frames t-2, t-1, and t. If they are the same, it is further determined whether the feature values ​​of the adjacent third partition have remained unchanged in the third consecutive image frame. If they still have not changed, the sum of the partition granularity corresponding to the second and third partitions is determined to obtain the sum of the partition granularity of the two. If the sum of the partition granularity is less than or equal to the maximum partition granularity, it can be determined that the second and third partitions meet the partition merging condition. When updating the partition granularity table, the second and third partitions are merged into one partition.

[0134] Step 702: If the feature values ​​of the second partition are inconsistent in the third consecutive image frame, and the feature values ​​of the second partition of any two adjacent image frames in the second consecutive image frame including the current image frame are inconsistent, and the partition granularity of the second partition is greater than the minimum partition granularity, then the second partition is determined to satisfy the partition splitting condition.

[0135] In this embodiment, if the feature values ​​of the second partition in the third consecutive image frame are inconsistent, i.e., the second partition does not meet the partition merging condition, it can be further determined whether the partition meets the partition splitting condition. In this embodiment, a second number of consecutive image frames (referred to as the fourth consecutive image frame) including the current image frame can be determined first, and it can be determined whether the feature values ​​of the second partition of any two adjacent image frames in the fourth consecutive image frame are inconsistent. For example, assuming the second frame number is 3, then continue with... Figure 8 For example, it is necessary to determine whether the feature values ​​of the second partition are different in frame t-2 and frame t-1, and whether they are also different in frame t-1 and frame t. If the feature values ​​of the second partition are inconsistent between any two adjacent image frames in the fourth consecutive image frame, it can be determined that the feature values ​​of the second partition have changed for two consecutive frames. At this time, it can be further determined whether the partition granularity of the second partition is greater than the minimum partition granularity. If it is greater, it is determined that the second partition meets the partition splitting condition; otherwise, it is determined that the second partition does not meet the partition splitting condition.

[0136] The image still frame detection method of this disclosure first determines whether the second partition and the adjacent third partition both satisfy the conditions of being still for multiple consecutive frames (the first frame number) and whether the sum of the partition granularity of the two partitions is not greater than the maximum partition granularity when the change count value of the second partition is not zero. If both conditions are met, it is determined that the two partitions meet the partition merging condition. If the partition merging condition is not met, it is further determined whether the second partition meets the requirements of changing for multiple consecutive frames (the second frame number) and the partition granularity being greater than the minimum partition granularity. If it meets the requirements, it is determined that the second partition meets the partition splitting condition. Thus, it is possible to determine whether the partition needs to be merged or split based on the number of consecutive still frames or the number of consecutive changing frames of the partition, thereby achieving fast and efficient determination of whether the partition granularity needs to be adjusted and improving convenience.

[0137] In some embodiments, the current partition granularity table is used to record the starting row and partition granularity of different partitions, for example... Figure 5 The partition granularity table is shown below. Figure 10 As shown, based on the aforementioned embodiments, step 404, where at least one partition meets the partition granularity adjustment conditions, determines the adjusted partition granularity table based on the partition granularity of the target partition that meets the partition granularity adjustment conditions and the current partition granularity table. This may include the following sub-steps:

[0138] Step 801: If the second partition and the third partition meet the partition merging conditions, based on the starting rows of the second partition and the third partition, determine the corresponding partitions of the second partition and the third partition in the current partition granularity table, and merge the partition granularity of the corresponding partitions to obtain the adjusted partition granularity table.

[0139] The current partition granularity table records the starting row and partition granularity of each partition. In this embodiment, when it is determined that the second and third partitions meet the partition merging conditions, the starting rows corresponding to the second and third partitions can be obtained respectively. Based on the obtained starting rows, the corresponding partitions are found in the current partition granularity table, and then the partition granularities of the corresponding partitions found in the current partition granularity table are merged, resulting in the updated adjusted partition granularity table. It is understood that when merging partitions, the starting row of the newly merged partition retains the smaller value between the second and third partitions.

[0140] For example, suppose the current partition granularity table is as follows: Figure 5 As shown, the second partition, which meets the partition merging condition, starts at line 1905, and the third partition starts at line 1909. Based on their starting lines, from... Figure 5 The corresponding partitions identified in the current partition granularity table shown are partition 149 and partition 150, respectively. The adjusted partition granularity table obtained by merging the partition granularities of the two is as follows. Figure 11 As shown, the starting value of the merged partition is 1905.

[0141] Step 802: If the second partition meets the partition splitting conditions, based on the starting row of the second partition, split the corresponding partition of the second partition in the current partition granularity table into two partitions, and determine the starting row and partition granularity of the two partitions respectively to obtain the adjusted partition granularity table.

[0142] In this embodiment of the disclosure, when it is determined that the second partition meets the partition splitting condition, the starting row of the second partition can be obtained, and the corresponding partition can be found from the current partition granularity table according to the obtained starting row, and the partition can be split into two partitions, and the starting row and partition granularity corresponding to the two partitions can be determined respectively to obtain the adjusted partition granularity table.

[0143] When performing partition splitting, either binary splitting or random splitting can be used. The only requirement is that the sum of the partition granularity of the two split partitions is the same as the partition granularity of the original partition. Generally speaking, the two split partitions are adjacent.

[0144] For example, suppose we use a binary partitioning approach for partitioning. Figure 5 Taking the current partition granularity table shown as an example, assuming the starting line of the determined second partition to be split is 1909, based on the starting line of this partition, from... Figure 5 The partition identified in the current partition granularity table is partition 150. Using a binary splitting method, this partition is divided into two new partitions of equal size, resulting in the following: Figure 12 The adjusted partitioning granularity table shown is as follows: Figure 12 As shown, the two partitions obtained from the split are partition 150 and partition 151. The starting row of partition 150 is still 1909, but the partition granularity is adjusted to 6. The starting row of partition 151 is determined to be 1915, and the partition granularity is also 6. In some embodiments, when using a binary split to split partitions, if the granularity of the partition to be split is not divisible, it can be split into two partitions with the smallest difference in granularity. For example, if the granularity of the second partition is 11, it can be split into two partitions with granularities of 6 and 5 respectively.

[0145] For example, suppose the partitioning is performed using a random partitioning method. Figure 5 Taking the current partition granularity table shown as an example, assuming the starting line of the determined second partition to be split is 1909, based on the starting line of this partition, from... Figure 5 The partition identified in the current partition granularity table is partition 150. This partition is then randomly split into two new partitions, as shown below. Figure 13 The adjusted partitioning granularity table shown is as follows: Figure 12As shown, the two partitions obtained by splitting are partition 150 and partition 151. The starting row of partition 150 is still 1909, but the partition granularity is adjusted to 8. The starting row of partition 151 is determined to be 1917, and the partition granularity is 4.

[0146] It should be noted that in this embodiment, the execution order of steps 801 and 802 is not important. In practical applications, it is possible that only steps 801 or 802 need to be executed, or both steps may need to be executed. Figure 10 The flowchart shown is for illustrative purposes only and should not be construed as a limitation of this disclosure.

[0147] The image still frame detection method of this disclosure uses a current partition granularity table to record the starting row and partition granularity of different partitions. When it is determined that the second and third partitions meet the partition merging conditions, the corresponding partitions of the second and third partitions in the current partition granularity table are determined based on the starting row of the second and third partitions, and the partition granularity of the corresponding partitions is merged to obtain an adjusted partition granularity table. When it is determined that the second partition meets the partition splitting conditions, the corresponding partition of the second partition in the current partition granularity table is split into two partitions based on the starting row of the second partition, and the starting row and partition granularity of the two partitions are determined respectively to obtain an adjusted partition granularity table. Thus, partition merging and splitting of partitions that meet the partition merging and partition splitting conditions are realized, and the adaptive dynamic adjustment of partition granularity is realized, improving the flexibility of partition adjustment.

[0148] In summary, in this embodiment, a change count or integral score (denoted as G_i) is maintained for each partition to record the content change information of the corresponding partition. For a partition (denoted as the current partition), if the current partition and its adjacent partitions both satisfy "N consecutive static (i.e., the first frame number) frames of stillness" or G_i≤θ_merge (i.e., the first threshold), and the granularity after merging is ≤h_max (i.e., the maximum partition granularity), then they are merged into one partition after the frame ends, and the granularity after merging is limited to the range of h_max. The change count or decay value of the merged partition is set to the smaller value of the two partitions, and it enters a cooling-off period. If the partition changes for N consecutive dynamic (i.e., the second frame number) frames or G_i≥θ_split (i.e., the second threshold), and the partition granularity>h_min (i.e., the minimum partition granularity), then after the frame ends, the partition is split into two equal-sized (or approximately equal-sized and aligned to multiples of h_min) sub-partitions, the change count values ​​of the two sub-partitions are cleared to zero or set to the decay value of the original partition, and they enter a cooling-off period. After each frame ends, the feature values ​​of each partition are stored and the corresponding integral values ​​of change are updated to prepare for the next frame.

[0149] In some embodiments, such as Figure 14As shown, based on the foregoing embodiments, the image still frame detection method of this disclosure may further include the following steps:

[0150] Step 901: After partitioning the next image frame according to the adjusted partition granularity table, determine and store the first feature value corresponding to each partition. The first feature value is used to compare the partition features with the subsequent image frames of the next image frame.

[0151] In this embodiment of the disclosure, after updating the partition granularity table to obtain the adjusted partition granularity table, the next image frame is partitioned according to the adjusted partition granularity table, and the feature value corresponding to each partition is determined (for ease of description and differentiation, it is called the first feature value). These first feature values ​​are stored for partition feature comparison with the subsequent image frames of the next image frame to determine whether the content of each partition in the subsequent image frame has changed relative to the next image frame.

[0152] Step 902: Divide the next image frame into partitions according to the current partition granularity table, determine the second feature value corresponding to each partition, and store it. The second feature value is used to compare the partition features with the current image frame.

[0153] In this embodiment of the present disclosure, in order to facilitate the comparison of the feature values ​​of the corresponding partitions of the next image frame with the previous image frame, in addition to determining and storing the first feature value of each partition obtained by partitioning the next image frame according to the adjusted partition granularity table, it is also necessary to partition the next image frame according to the current partition granularity table before the update, and determine the feature value of each partition (called the second feature value). These second feature values ​​are stored for comparison of the partition features with the previous image frame of the next image frame (i.e., the current image frame in the aforementioned embodiment) to determine whether the content of each partition in the next image frame has changed relative to the previous image frame.

[0154] In some embodiments, in order to improve the efficiency of feature value calculation, after partitioning the next image frame according to the adjusted partition granularity table and determining the first feature value of each partition, for each partition obtained by partitioning the next image frame according to the current partition granularity table before the update, when determining the first feature value of each partition, the current partition granularity table and the adjusted partition granularity table can be compared first to determine the partition before and after the change. For these changed partitions, the feature value is recalculated to obtain the second feature value. For unchanged partitions, the corresponding first feature value is directly used as the second feature value of these partitions and stored.

[0155] In some embodiments, when updating the partition size, an appropriate feature value storage method can be selected based on whether the feature value characteristic is a continuous metric. If it is not a continuous metric, then the updated frame needs to store feature information for both partition methods to facilitate comparison with the previous frame and the next frame; if it is a continuous metric, then it needs to be checked whether feature merging or splitting is supported.

[0156] The image still frame detection method of this disclosure, by partitioning the next image frame according to the adjusted partition granularity table, determining and storing the first feature value corresponding to each partition, and partitioning the next image frame according to the current partition granularity table and determining and storing the second feature value corresponding to each partition, achieves the following for the first image frame after the adjusted partition granularity table takes effect: storing the feature values ​​of each partition before the update for comparison with the partition features of subsequent image frames, and storing the feature values ​​of each partition after the update for comparison with the partition features of the previous image frame, thus ensuring the accuracy of still frame detection.

[0157] In some embodiments, such as Figure 15 As shown, based on the foregoing embodiments, the image still frame detection method of this disclosure may further include the following steps:

[0158] Step 1001: When the still frame detection mode is row mode or column mode, after each still frame detection of an image frame is completed, based on the dynamic partition of the target image frame that has been detected this time, determine the proportion of dynamic rows / columns, the number of consecutive dynamic partition rows / columns, and the degree of dispersion between dynamic partitions. Here, dynamic partition refers to a partition whose feature value is inconsistent with the feature value of the corresponding partition in the previous image frame.

[0159] When the still frame detection mode is row mode, still frame detection is performed row by row; when the still frame detection mode is column mode, still frame detection is performed column by column. The display device defaults to performing still frame detection in either row or column mode. In both modes, after each image frame is detected, the dynamic row / column ratio, the number of consecutive dynamic partitions, and the dispersion between dynamic partitions are determined based on the dynamic partitions of the target image frame that has been detected.

[0160] In this context, a dynamic partition refers to a partition whose feature value is inconsistent with the feature value of the corresponding partition in the previous image frame. If the feature value of a partition is completely consistent with that of the partition in frame t-1, then the partition is a static partition; otherwise, the partition is determined to be a dynamic partition.

[0161] In this embodiment of the disclosure, the dynamic row / column ratio represents the ratio of the number of rows / columns of all dynamic partitions in the target image frame to the total number of rows / columns of the target image frame. It is understood that when the still frame detection mode is row mode, the dynamic row ratio is determined, which is the ratio of the number of rows of all dynamic partitions in the target image frame to the total number of rows in the target image frame; when the still frame detection mode is column mode, the dynamic column ratio is determined, which is the ratio of the number of columns of all dynamic partitions in the target image frame to the total number of columns in the target image frame.

[0162] The number of consecutive dynamic partition rows / columns indicates the number of rows / columns covered by the longest continuous band formed by consecutive dynamic partitions in the target image frame. It can be understood that when the still frame detection mode is row mode, the number of consecutive dynamic partition rows is determined, which refers to the number of rows covered by the longest continuous band formed by the dynamic partitions after aggregation along the row direction; when the still frame detection mode is column mode, the number of consecutive dynamic partition columns is determined, which refers to the number of columns covered by the longest continuous band formed by the dynamic partitions after aggregation along the column direction.

[0163] The degree of dispersion represents the average interval between dynamic partitions in the target image frame, reflecting the degree of concentration or dispersion of dynamic partitions, and is obtained by calculating the average interval between dynamic bands.

[0164] Step 1002: If the proportion of dynamic rows / columns is less than or equal to the proportion threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the dispersion is greater than the dispersion threshold, determine that the switching conditions corresponding to the partition mode are met.

[0165] In this embodiment, after determining the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion between dynamic partitions corresponding to the target image frame, each index value can be compared with its corresponding threshold. The dynamic row / column ratio corresponds to a ratio threshold; the number of consecutive dynamic partition rows / columns corresponds to a dynamic concentration threshold, which is a threshold used for the bandgap condition in row / column mode determination, representing the minimum number of rows / columns required to achieve the longest continuous band formed by the dynamic region in the row / column direction; and the dispersion corresponds to a dispersion threshold. If the dynamic row / column ratio is less than or equal to the ratio threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the dispersion is greater than the dispersion threshold, then the current switching condition corresponding to the partition mode is determined to be met. That is, when at least one of the three conditions—dynamic row / column ratio less than or equal to the ratio threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the dispersion is greater than the dispersion threshold—is met, the current switching condition corresponding to the partition mode is determined to be met. If none of the three conditions are met, the current switching condition corresponding to the partition mode is determined to be unmet, and still frame detection is performed in either row or column mode.

[0166] Step 1003: Switch the still frame detection mode to the partition mode.

[0167] In this embodiment of the disclosure, when it is determined that the switching conditions corresponding to the partition mode are met, the still frame detection mode is immediately switched from the row / column mode to the partition mode, triggering the merge / split judgment mode. The mode continues until all three conditions of the partition mode are not met (i.e., the proportion of dynamic rows / columns is greater than the proportion threshold, the number of continuous dynamic partition rows / columns is not less than the dynamic concentration threshold, and the dispersion is not greater than the dispersion threshold). Then, the mode returns to the row / column mode. In other words, in the partition mode, when the dynamic partitions are continuous in the row (or column) direction, have a high proportion and low sparsity, the mode is switched to the row (or column) mode; when the dynamic partitions are scattered in local small areas and have a low overall proportion, the partition mode is maintained for fine refresh.

[0168] In some embodiments, to prevent jitter, the still frame detection mode can be switched to the partition mode only after the switching conditions for the partition mode are met for a third consecutive frame. The specific value of the third frame can be set according to actual needs, for example, setting the third frame to 2. That is, assuming the third frame is 2, when the dynamic partitioning of the target image frame first detects that the switching conditions for the partition mode are met, the row / column mode is temporarily maintained for still frame detection. If the dynamic partitioning of the next image frame also determines that the switching conditions for the partition mode are met, i.e., the dynamic partitioning of two consecutive image frames determines that the switching conditions for the partition mode are met, then the still frame detection mode is switched from the row / column mode to the partition mode. In this scheme, mode switching uses upper and lower thresholds and frame hysteresis: switching from partition mode to row / column mode requires a certain number of consecutive frames to meet the row / column switching conditions; switching from row / column mode to partition mode requires a certain number of consecutive frames to meet the dynamic dispersion conditions. This prevents jitter caused by frequent mode switching.

[0169] The image still frame detection method of this disclosure, when the still frame detection mode is row mode or column mode, after completing the still frame detection of each image frame, determines the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion degree between dynamic partitions based on the dynamic partitions of the target image frame that has been detected. Dynamic partitions refer to partitions whose feature values ​​are inconsistent with the feature values ​​of the corresponding partitions in the previous image frame. When the dynamic row / column ratio is less than or equal to the ratio threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, and the dispersion degree is greater than the dispersion threshold, the still frame detection mode is switched to partition mode. Thus, the dynamic switching of the still frame detection mode is realized, and the partition mode is switched to in a timely manner when the switching conditions of the partition mode are met. The optimal refresh mode can be automatically selected according to the spatial shape of the global dynamic area, thereby improving the refresh efficiency.

[0170] In some embodiments, in partitioning mode, after each image frame is detected as a still frame, the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion between dynamic partitions can be determined based on the dynamic partitioning of the currently completed image frame. If the conditions are met—that the dynamic row / column ratio is less than or equal to a ratio threshold (i.e., the dynamic ratio is low), the number of consecutive dynamic partition rows / columns is less than a dynamic concentration threshold, and the dispersion is greater than a dispersion threshold (i.e., the dispersion is small)—partition merging is triggered, merging at least two dynamic partitions with an interval less than a preset value. It should be noted that the thresholds corresponding to each indicator (i.e., the dynamic row / column ratio, the number of consecutive dynamic partition rows / columns, and the dispersion between dynamic partitions) in this embodiment can be the same as or different from the thresholds corresponding to each indicator in the partitioning mode switching conditions in the aforementioned embodiments. The specific settings can be made according to actual needs, and this disclosure does not impose any restrictions on this.

[0171] In some embodiments, when entering row mode, the partition granularity table can be virtualized so that each row is a minimum partition (or a row group aligned to the minimum refresh granularity of the panel), using the same feature calculation and judgment process, with refresh based on a dynamic row mask; when entering column mode, the partition granularity table can be virtualized so that each column is a minimum partition (or a column group aligned to the minimum refresh granularity of the panel), using column-to-column feature accumulation, with refresh based on a dynamic column mask; when maintaining partition mode, the current adaptive partition granularity is maintained, and only dynamic partitions are refreshed. This ensures consistency in the implementation process when different still frame detection modes are applied.

[0172] This disclosure also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described image still frame detection method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0173] The computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0174] This disclosure provides a computer program product that, when run on a computer, enables the computer to perform the various processes of the above-described image still frame detection method and achieve the same technical effect. To avoid repetition, further details are omitted here.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

[0176] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the described embodiments and various different variations of embodiments suitable for specific use considerations.

Claims

1. A display device, characterized in that, include: The monitor is configured to display image frames according to a preset refresh direction; The controller connected to the display is configured to: In partitioning mode, the current image frame displayed on the display is partitioned in the same direction as the refresh direction according to the current partitioning granularity table, resulting in multiple partitions; Determine the feature values ​​for each of the partitions; The feature value of each partition is compared with the feature value of the corresponding partition in the previous image frame, and the change integral value corresponding to each partition is updated according to the comparison result. The change integral value is used to represent the change information of the feature value of the corresponding partition. Based on the change integral value, it is determined whether each partition meets the partition granularity adjustment condition. If at least one partition meets the partition granularity adjustment condition, the adjusted partition granularity table is determined based on the partition granularity of the target partition that meets the partition granularity adjustment condition and the current partition granularity table. When the display shows the next image frame, the next image frame is partitioned according to the adjusted partition granularity table; Wherein, the change integral value is an integral fraction or a change count value, and the partition granularity adjustment conditions include partition merging conditions and partition splitting conditions; When the change integral value is an integral score, the partition merging condition is that the integral scores of two adjacent partitions are both less than or equal to a first threshold, and the sum of the partition granularity of the two adjacent partitions is less than or equal to the maximum partition granularity; the partition splitting condition is that the integral score is greater than or equal to a second threshold and the partition granularity is greater than the minimum partition granularity, and the first threshold is less than the second threshold. When the change integral value is the change count value, the initial value of the change count value corresponding to each partition is zero. The partition merging condition is that in two adjacent partitions of consecutive image frames of the first frame number, the feature values ​​of the same partition are consistent, and the sum of the partition granularity of the two adjacent partitions is less than or equal to the maximum partition granularity. The partition splitting condition is that in the feature values ​​of the same partition of consecutive image frames of the second frame number, any two adjacent feature values ​​are inconsistent, and the partition granularity is greater than the minimum partition granularity.

2. The display device according to claim 1, characterized in that, The controller is specifically configured as follows: If the feature value of the first partition is the same as the feature value of the corresponding partition in the previous image frame, the change integral value of the first partition is reduced or the change integral value of the first partition is kept unchanged. If the feature value of the first partition is inconsistent with the feature value of the corresponding partition in the previous image frame, the change integral value corresponding to the first partition is increased. The first partition is any one of the plurality of partitions.

3. The display device according to claim 1, characterized in that, The integral value of the change is an integral fraction, and the controller is further configured to: Obtain the target score corresponding to the second partition, where the second partition is any one of the plurality of partitions; If the target integral score is less than or equal to the first threshold, and the integral score of the third partition adjacent to the second partition is also less than or equal to the first threshold, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then it is determined that the second partition and the third partition satisfy the partition merging condition. If the target integral score is greater than or equal to the second threshold, and the partition granularity of the second partition is greater than the minimum partition granularity, then the second partition is determined to satisfy the partition splitting condition.

4. The display device according to claim 1, characterized in that, The change integral value is the change count value, and the controller is specifically configured as follows: Obtain the target change count value corresponding to the second partition, where the second partition is any one of the plurality of partitions; When the target change count is zero, the historical image frames are sequentially queried backward from the current image frame to obtain the number of first image frames containing the second partition in the first consecutive image frames including the current image frame, and the number of second consecutive image frames in the second consecutive image frames whose feature values ​​of the third partition are consistent with the feature values ​​of the third partition in the current image frame. The third partition is the partition adjacent to the second partition, and the second consecutive image frames include the current image frame. If both the first image frame count and the second image frame count are greater than or equal to the first frame count, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then the second partition and the third partition are determined to satisfy the partition merging condition.

5. The display device according to claim 4, characterized in that, The controller is further configured as follows: If the target change count value is not zero, and in the third consecutive image frame of the first frame number including the current image frame, the feature value of the second partition is consistent and the feature value of the third partition adjacent to the second partition is also consistent, and the sum of the partition granularity of the second partition and the third partition is less than or equal to the maximum partition granularity, then it is determined that the second partition and the third partition satisfy the partition merging condition. If the feature values ​​of the second partition are inconsistent in the third consecutive image frame, and if the feature values ​​of the second partition of any two adjacent image frames in the fourth consecutive image frame (including the current image frame) are inconsistent, and the partition granularity of the second partition is greater than the minimum partition granularity, then the second partition is determined to satisfy the partition splitting condition.

6. The display device according to any one of claims 3-4, characterized in that, The current partition granularity table is used to record the starting row and partition granularity of different partitions; The controller is specifically configured as follows: If it is determined that the second partition and the third partition meet the partition merging condition, based on the starting row of the second partition and the third partition, the corresponding partitions of the second partition and the third partition in the current partition granularity table are determined, and the partition granularity of the corresponding partitions is merged to obtain the adjusted partition granularity table; If the second partition satisfies the partition splitting condition, based on the starting row of the second partition, the corresponding partition of the second partition in the current partition granularity table is split into two partitions, and the starting row and partition granularity of the two partitions are determined respectively to obtain the adjusted partition granularity table.

7. The display device according to any one of claims 1 to 5, characterized in that, The controller is specifically configured as follows: After partitioning the next image frame according to the adjusted partition granularity table, the first feature value corresponding to each partition is determined and stored. The first feature value is used to compare the partition features with the subsequent image frames of the next image frame. The next image frame is partitioned according to the current partition granularity table, and the second feature value corresponding to each partition is determined and stored. The second feature value is used to compare the partition features with the current image frame.

8. The display device according to any one of claims 1 to 5, characterized in that, The controller is specifically configured as follows: When the still frame detection mode is row mode or column mode, after each still frame detection is completed, the dynamic row / column ratio, the number of consecutive dynamic partitions and the degree of dispersion between dynamic partitions are determined based on the dynamic partitions of the target image frame that has been detected. The dynamic partition refers to the partition whose feature value is inconsistent with the feature value of the corresponding partition in the previous image frame. If the proportion of dynamic rows / columns is less than or equal to the proportion threshold, the number of consecutive dynamic partition rows / columns is less than the dynamic concentration threshold, or the degree of dispersion is greater than the dispersion threshold, then the switching conditions corresponding to the partition mode are determined to be met. Switch the still frame detection mode to the partition mode; Wherein, the dynamic row / column ratio represents the ratio of the number of rows / columns of all the dynamic partitions in the target image frame to the total number of rows / columns of the target image frame; The number of consecutive dynamic partition rows / columns represents the number of rows / columns covered by the longest consecutive band formed by the consecutive dynamic partitions in the target image frame; The degree of dispersion represents the average interval between the dynamic partitions in the target image frame.

9. The display device according to claim 8, characterized in that, The controller is specifically configured as follows: If the switching conditions are met in all consecutive image frames of the third frame number, the still frame detection mode is switched to the partition mode.

10. A method for detecting still frames in an image, characterized in that, Applied to display devices, including: In partitioning mode, the current image frame displayed on the display device is partitioned in the same direction as the refresh direction of the display according to the current partitioning granularity table, resulting in multiple partitions; Determine the feature values ​​for each of the partitions; The feature value of each partition is compared with the feature value of the corresponding partition in the previous image frame, and the change integral value corresponding to each partition is updated according to the comparison result. The change integral value is used to represent the change information of the feature value of the corresponding partition. Based on the change integral value, it is determined whether each partition meets the partition granularity adjustment condition. If at least one partition meets the partition granularity adjustment condition, the adjusted partition granularity table is determined based on the partition granularity of the target partition that meets the partition granularity adjustment condition and the current partition granularity table. When the display shows the next image frame, the next image frame is partitioned according to the adjusted partition granularity table; Wherein, the change integral value is an integral fraction or a change count value, and the partition granularity adjustment conditions include partition merging conditions and partition splitting conditions; When the change integral value is an integral score, the partition merging condition is that the integral scores of two adjacent partitions are both less than or equal to a first threshold, and the sum of the partition granularity of the two adjacent partitions is less than or equal to the maximum partition granularity; the partition splitting condition is that the integral score is greater than or equal to a second threshold and the partition granularity is greater than the minimum partition granularity, and the first threshold is less than the second threshold. When the change integral value is the change count value, the initial value of the change count value corresponding to each partition is zero. The partition merging condition is that in two adjacent partitions of consecutive image frames of the first frame number, the feature values ​​of the same partition are consistent, and the sum of the partition granularity of the two adjacent partitions is less than or equal to the maximum partition granularity. The partition splitting condition is that in the feature values ​​of the same partition of consecutive image frames of the second frame number, any two adjacent feature values ​​are inconsistent, and the partition granularity is greater than the minimum partition granularity.

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