Data compression method and device, computer readable storage medium, and terminal

Abstract

A data compression method and device, a computer-readable storage medium, and a terminal, the method comprising: determining the data of a Gaussian data table of a previous frame and a Gaussian data table of a current frame, and determining the offset between the Gaussian data center values ​​of the Gaussian data table of the current frame and the Gaussian data table of the previous frame; determining a first weight value between the data of a prediction area and the data of a reference area, and determining a second weight value between the data of the prediction area and the data of a mapping area; determining the prediction value of each data in the prediction area according to the first weight value and the second weight value; subtracting the corresponding prediction value from each data in the prediction area to obtain the residual value of each data; compressing the residual value of each data in the prediction area and each data outside the prediction area to obtain compressed data. The present invention can effectively compress the Gaussian data table and greatly reduce the bandwidth resources occupied by data.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a data compression method and device, a computer-readable storage medium, and a terminal. Background Art

[0002] In liquid crystal displays, local dimming technology can improve the performance of the display screen and achieve a higher contrast range by dividing the backlight into different "light zones" and using a control algorithm to control the lighting of each light zone individually. The local dimming point spread function (PSF) data table is a brightness data table measured when the backlight is lit in different zones. Specifically: the backlight of the entire liquid crystal panel is divided into a certain number of light zones, and each light zone (or each backlight lamp) is lit in turn. Each time a backlight lamp is lit, the brightness value of each light zone on the backlight is measured. The result of arranging all the measurement results is the local dimming PSF data table. The data in this data table presents Gaussian distribution characteristics, and there is an obvious inter-frame offset (translation) feature between the front and rear frame data tables. At the hardware level, if all local dimming PSF data tables are stored in the cache, a lot of resources will be consumed. Therefore, it is necessary to compress the original data table through a compression algorithm to save bandwidth resources.

[0003] The existing technologies commonly used in current data compression include intra-frame prediction, inter-frame prediction, variable-length coding, transform coding, etc.:

[0004] (1) Intra-frame prediction: By selecting a mode in the type value for each frame data table, the mode specifies the predicted value of a certain data X (for example, a mode uses the value of the decoded data B as the predicted value of data X). Once the predicted value of data X is obtained, only the difference between X and B needs to be transmitted when transmitting data. Since the difference between X and B occupies fewer bits than X itself, it can save bandwidth resources. However, this technology is suitable for data with relatively large overall values. For data tables with Gaussian distribution characteristics such as local dimming PSF data tables, the above intra-frame prediction technology may not achieve a good compression effect.

[0005] (2) Inter-frame prediction: By utilizing the continuity of previous and next frames, the amount of data transmission is reduced. The typical method is to search for the reference block that best matches the coding block of the current frame in the reference frame, and search for the best motion vector (MV) pointing from the coding block of the current frame to the reference block. The reference block is used as the prediction block of the coding block of the current frame. The coding block of the current frame is subtracted from the corresponding prediction block to obtain the residual block. In this way, only the motion vector MV and the residual block need to be transmitted during data transmission. However, the above inter-frame prediction technology often requires obtaining the inter-frame motion vector MV. The greatest complexity in obtaining the MV lies in the search for the best MV. This process greatly increases the complexity of hardware module design.

[0006] (3) Variable-length coding: A typical variable-length coding technique is Huffman coding. The core idea of ​​this coding technique is that the greater the probability of a symbol appearing in the source data, the shorter the corresponding code; the smaller the probability of a symbol appearing, the longer the code, so as to achieve the goal of using as few code symbols as possible to represent the source data. Later, various entropy coding algorithms evolved based on the idea of ​​Huffman coding. However, when a frame of data may have a large number of data with relatively large values ​​(such as the local dimming PSF data table), the use of variable-length coding will result in a large number of bits occupied by the data, and a good compression effect cannot be achieved; and various entropy coding techniques involve complex operations such as symbol probability statistics and transmission, window sliding operations, binarization and recursive operations. If such a complex encoding and decoding module is added to the hardware display module, it will occupy too much chip area.

[0007] (4) Transform coding uses cosine transform to convert data from the spatial domain to the frequency domain, so that the low-frequency components with larger values ​​are concentrated in the upper left corner of the matrix. It is combined with quantization and other methods to achieve data compression at the expense of some precision. However, since the use of quantization will cause a certain loss of information, this is unacceptable for display algorithms that require precise data.

[0008] In summary, there is an urgent need for a data compression method that can effectively compress Gaussian data tables that exhibit Gaussian distribution characteristics, while not losing original information and reducing hardware design complexity and computing costs, while significantly reducing the storage space or bandwidth resources occupied by the data. Summary of the invention

[0009] The technical problem solved by the present invention is how to utilize the Gaussian distribution characteristics and inter-frame translation characteristics of the Gaussian data table to be compressed to significantly reduce the storage space or bandwidth resources occupied by data without losing original information and reducing hardware design complexity and computing costs.

[0010] To solve the above technical problems, an embodiment of the present invention provides a data compression method, comprising the following steps: determining data in a Gaussian data table of a previous frame and a Gaussian data table of a current frame, and determining an offset between a Gaussian data center value in the Gaussian data table of the current frame and a Gaussian data center value in the Gaussian data table of the previous frame; determining a first weight value between data in a prediction area in the Gaussian data table of the current frame and data in a reference area in the Gaussian data table of the previous frame, and determining a second weight value between data in a prediction area in the Gaussian data table of the current frame and data in a mapping area in the Gaussian data table of the previous frame, wherein the prediction area is the Gaussian data table of the current frame. The first preset area around the Gaussian data center value in the Gaussian data table of the previous frame, the reference area is a second preset area around the Gaussian data center value in the Gaussian data table of the previous frame, the shapes and data volumes of the first preset area and the second preset area are consistent, and the mapping area is obtained by offsetting the reference area with the offset; according to the first weight value and the second weight value, the predicted value of each data in the prediction area is determined; the corresponding predicted value is subtracted from each data in the prediction area to obtain the residual value of each data; the residual value of each data in the prediction area and each data outside the prediction area are compressed to obtain compressed data.

[0011] Optionally, determining a first weight value between the data of the prediction area in the Gaussian data table of the current frame and the data of the reference area in the Gaussian data table of the previous frame, and determining a second weight value between the data of the prediction area in the Gaussian data table of the current frame and the data of the mapping area in the Gaussian data table of the previous frame includes: determining a first correlation coefficient and a first scaling value between the data of the prediction domain and the data of the reference area, and determining a second correlation coefficient and a second scaling value between the data of the prediction area and the data of the mapping area; determining the first weight value according to the first correlation coefficient, the second correlation coefficient, and the first scaling value, and determining the second weight value according to the first correlation coefficient, the second correlation coefficient, and the second scaling value; wherein the first correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the reference area, and the second correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the mapping area.

[0012] Optionally, the first correlation coefficient and the second correlation coefficient are Pearson correlation coefficients; the first correlation coefficient and the first scaling value between the data in the prediction area and the data in the reference area are determined using the following formula:

[0013]

[0014]

[0015] Wherein, r1 is used to indicate the first correlation coefficient between the data of the prediction area and the data of the reference area; X i It is used to indicate the original data value of the i-th data in the prediction area, Y i is used to indicate the original data value of the i-th data in the reference area; N is used to indicate the total amount of data in the prediction area or the reference area; scl1 is used to indicate the first scaling value between the data in the prediction area and the data in the reference area;

[0016] And / or using the following formula to determine the second correlation coefficient and the second scaling value between the data in the prediction area and the data in the mapping area:

[0017]

[0018]

[0019] Wherein, r2 is used to indicate the second correlation coefficient between the data in the prediction area and the data in the mapping area; j It is used to indicate the original data value of the jth data in the prediction area, Z j It is used to indicate the original data value of the jth data in the mapping area; N is used to indicate the total amount of data in the prediction area or the mapping area; scl2 is used to indicate the second scaling value between the data in the prediction area and the data in the mapping area.

[0020] Optionally, the first weight value is determined according to the first correlation coefficient, the second correlation coefficient, and the first scaling value using the following formula:

[0021]

[0022] And / or using the following formula to determine the second weight value according to the first correlation coefficient, the second correlation coefficient, and the second scaling value:

[0023]

[0024] Among them, w1 is used to indicate the first weight value; scl1 is used to indicate the first scaling value; w2 is used to indicate the second weight value; scl2 is used to indicate the second scaling value; r1 is used to indicate the first correlation coefficient; r2 is used to indicate the second correlation coefficient.

[0025] Optionally, the following formula is used to determine the predicted value of each data in the predicted area according to the first weight value and the second weight value:

[0026] Xi '=w1Y i +w2Z i ;

[0027] Among them, X i 'Used to indicate the predicted value of the i-th data in the prediction area; Y i It is used to indicate the original data value of the i-th data in the reference area; i Used to indicate the original data value of the i-th data in the mapping area.

[0028] Optionally, compressing the residual values ​​of each data within the prediction area and each data outside the prediction area to obtain compressed data includes: grouping the residual values ​​of each data within the prediction area and each data outside the prediction area to obtain one or more data groups; for each data group, determining the maximum value among the absolute values ​​of each data in the data group; determining the number of encoding bits for each data in the data group based on the maximum value among the absolute values ​​of each data in the data group, and using the encoding bit number to encode each data in the data group; and using the data obtained after encoding each data of each data group in the Gaussian data table of the current frame as the compressed data.

[0029] Optionally, the residual values ​​of each data within the prediction area and each data outside the prediction area are divided into groups to obtain one or more data groups selected from any one of the following: the residual values ​​of each data within the prediction area and each data outside the prediction area are evenly divided to obtain one or more data groups; the residual values ​​of each data within the prediction area and each data outside the prediction area are divided into groups of P×Q data to obtain one or more data groups, wherein P and Q are both positive integers.

[0030] Optionally, determining the number of coding bit bits for each data in the data group based on the maximum value among the absolute values ​​of each data in the data group includes: if there are negative numbers in the data in the data group, then adding one bit to the number of bits occupied by the maximum value among the absolute values ​​of each data in the data group as the number of coding bit bits for each data in the data group; if there are no negative numbers in the data in the data group, then using the number of bits occupied by the maximum value among the absolute values ​​of each data in the data group as the number of coding bit bits for each data in the data group.

[0031] Optionally, the Gaussian data table is brightness data obtained by collecting the brightness values ​​on the backlight panel after lighting one or more backlight lamps on the backlight panel, and the Gaussian data table also satisfies one or more of the following: the Gaussian data center value in the Gaussian data table corresponds to the maximum value of the brightness value on the backlight panel; the Gaussian data table is a local dimming PSF data table.

[0032] Optionally, the first preset area and the second preset area are both areas consisting of M×N data, where M and N are both positive integers.

[0033] The embodiment of the present invention also provides a data compression device, including: a Gaussian feature data and offset determination module, used to determine the data in the Gaussian data table of the previous frame and the Gaussian data table of the current frame, and determine the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame; a weight value determination module, used to determine the first weight value between the data of the prediction area in the Gaussian data table of the current frame and the data of the reference area in the Gaussian data table of the previous frame, and determine the second weight value between the data of the prediction area in the Gaussian data table of the current frame and the data of the mapping area in the Gaussian data table of the previous frame, wherein the prediction area is the Gaussian data in the Gaussian data table of the current frame. a first preset area around the center value, the reference area is a second preset area around the center value of the Gaussian data table in the previous frame, the shapes and data volumes of the first preset area and the second preset area are consistent, and the mapping area is obtained by offsetting the reference area using the offset; a prediction value determination module, used to determine the prediction value of each data in the prediction area according to the first weight value and the second weight value; a residual value calculation module, used to subtract the corresponding prediction value from each data in the prediction area to obtain the residual value of each data; a compression processing module, used to compress the residual value of each data in the prediction area and each data outside the prediction area to obtain compressed data.

[0034] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above data compression method are executed.

[0035] An embodiment of the present invention further provides a terminal, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the steps of the above data compression method when running the computer program.

[0036] Optionally, the terminal is a display terminal, which includes a backlight panel and a plurality of backlight lamps; wherein, when the backlight lamps are turned on, the brightness value of at least a portion of the area on the backlight panel increases.

[0037] Compared with the prior art, the technical solution of the embodiment of the present invention has the following beneficial effects:

[0038] In an embodiment of the present invention, firstly, data of a Gaussian data table of a previous frame and a Gaussian data table of a current frame are determined, and an offset between a Gaussian data center value in the current frame and a Gaussian data center value in the previous frame is determined; then, a first weight value between data of a prediction area in the current frame and data of a reference area in the previous frame is determined, and a second weight value between data of a prediction area in the current frame and data of a mapping area in the previous frame is determined, wherein the prediction area and the reference area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area by the offset; then, based on the first weight value and the second weight value, a prediction value of each data in the prediction area is determined; each data in the prediction area is subtracted from the corresponding prediction value to obtain a residual value of each data; finally, the residual value of each data in the prediction area and each data outside the prediction area are compressed to obtain compressed data. Compared with various existing data compression technologies, intra-frame prediction cannot achieve good compression effects for Gaussian data tables with Gaussian distribution characteristics; inter-frame prediction often requires obtaining the best inter-frame motion vector MV, and the best MV search process will greatly increase the hardware complexity; when using variable-length coding technology, since each frame of the Gaussian data table may have a large number of data with relatively large values, it may cause the data table to occupy a large number of bits overall, and it is impossible to save storage space or bandwidth resources; and the various existing entropy coding technologies involve some complex calculation steps, which will occupy too much chip area of ​​the display module and increase calculation overhead; in addition, the quantization method used in the transform coding technology will cause a certain loss of information. The embodiment of the present invention starts with the Gaussian distribution and inter-frame translation law of the Gaussian data table itself, and directly obtains the predicted value of each data in the prediction area of ​​the current frame through a dual weight coefficient calculation method, and then calculates the residual value of each data in the prediction area, and finally compresses the residual value of each data in the prediction area of ​​the current frame and the original data value of other data outside the prediction area. This can avoid the increase in hardware design complexity and computing cost caused by entropy coding and MV calculation process, and ensure the integrity of the data, effectively reduce the storage space occupied by data, and save bandwidth resources.

[0039] Further, the residual value of each data in the prediction area and each data outside the prediction area are compressed, including: grouping the residual value of each data in the prediction area and each data outside the prediction area to obtain one or more data groups; then for each data group, determining the number of coding bits for each data in the data group, and using the coding bit number to encode each data in the data group; encoding each data of each data group in the Gaussian data table of the current frame as the compressed data. In an embodiment of the present invention, by grouping each data in the Gaussian data table of the current frame, a shorter coding bit number is used to encode smaller data, and a longer coding bit number is used to encode larger data, so that data can be effectively compressed and storage space and bandwidth resources can be saved. In addition, when performing group division, the number of divided data groups can be adjusted according to the different requirements of specific application scenarios, so as to achieve the optimal compression efficiency and compression effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a data compression method in an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a local dimming PSF data table in an embodiment of the present invention;

[0042] Figure 3 yes Figure 1 A flowchart of a specific implementation of step S15;

[0043] Figure 4 is a flow chart of another data compression method in an embodiment of the present invention;

[0044] Figure 5 It is a structural schematic diagram of a data compression device in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] As mentioned above, it is necessary to compress the Gaussian data table through a compression algorithm to save storage space or bandwidth resources.

[0046] In the prior art, the main methods of data compression include intra-frame prediction, inter-frame prediction, variable-length coding, transform coding, etc. Intra-frame prediction is to select a mode in the type value of each frame data table, which specifies that the prediction value of a certain data X is B, and only the difference between X and B is transmitted when the data is transmitted; inter-frame prediction searches for the reference block that best matches the coding block of the current frame in the reference frame, and searches for the best motion vector from the coding block to the reference block, and uses the reference block as the prediction block of the coding block of the current frame. The coding block of the current frame is subtracted from the corresponding prediction block to obtain a residual block, and only the motion vector and the residual block need to be transmitted when the data is transmitted; variable-length coding technology is to use codes of different lengths for different source data to achieve the effect of using as few code symbols as possible to represent the source data; however, this technology is suitable for data with relatively large overall values, and for data waiting to be compressed (each frame of data presents a Gaussian function distribution feature) such as the local dimming PSF data table, the above-mentioned intra-frame prediction technology may not be able to achieve a good compression effect; in transform coding technology, quantization and other methods are used to achieve data compression at the expense of a part of the degree of refinement.

[0047] The inventors of the present invention have discovered through research that the intra-frame prediction technology is suitable for data with relatively large overall values, but cannot achieve a good compression effect for data tables showing Gaussian distribution characteristics such as the local dimming PSF data table; and the existing inter-frame prediction technology greatly increases the complexity of hardware module design during the search for the best MV; and when a frame of data may have a large number of data with relatively large values ​​(such as a Gaussian data table with Gaussian distribution characteristics), the use of variable-length coding will cause the data to occupy more bits, and cannot effectively save bandwidth resources; in addition, the quantization method involved in the transform coding process often causes the loss of original data.

[0048] In an embodiment of the present invention, firstly, data of a Gaussian data table of a previous frame and a Gaussian data table of a current frame are determined, and an offset between a Gaussian data center value in the current frame and a Gaussian data center value in the previous frame is determined; then, a first weight value between data of a prediction area in the current frame and data of a reference area in the previous frame is determined, and a second weight value between data of a prediction area in the current frame and data of a mapping area in the previous frame is determined, wherein the prediction area and the reference area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area by the offset; then, based on the first weight value and the second weight value, a prediction value of each data in the prediction area is determined; each data in the prediction area is subtracted from the corresponding prediction value to obtain a residual value of each data; finally, the residual value of each data in the prediction area and each data outside the prediction area are compressed to obtain compressed data. Compared with various existing data compression technologies, intra-frame prediction cannot achieve good compression effects for Gaussian data tables with Gaussian distribution characteristics; inter-frame prediction often requires obtaining the best inter-frame motion vector MV, and the best MV search process will greatly increase the hardware complexity; when using variable-length coding technology, since each frame of the Gaussian data table may have a large number of data with relatively large values, it may cause the data table to occupy a large number of bits overall, and it is impossible to save storage space or bandwidth resources; and the various existing entropy coding technologies involve some complex calculation steps, which will occupy too much chip area of ​​the display module and increase calculation overhead; in addition, the quantization method used in the transform coding technology will cause a certain loss of information. The embodiment of the present invention starts with the Gaussian distribution and inter-frame translation law of the Gaussian data table itself, and directly obtains the predicted value of each data in the prediction area of ​​the current frame through a dual weight coefficient calculation method, and then calculates the residual value of each data in the prediction area, and finally compresses the residual value of each data in the prediction area of ​​the current frame and the original data value of other data outside the prediction area. This can avoid the increase in hardware design complexity and computing cost caused by entropy coding and MV calculation process, and ensure the integrity of the data, effectively reduce the storage space occupied by data, and save bandwidth resources.

[0049] In order to make the above-mentioned objects, features and beneficial effects of the present invention more obvious and understandable, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] Reference Figure 1 , Figure 1 1 is a flow chart of a data compression method in an embodiment of the present invention. The method may include steps S11 to S15:

[0051] Step S11: determining data in the Gaussian data table of the previous frame and the Gaussian data table of the current frame, and determining an offset between a Gaussian data center value in the Gaussian data table of the current frame and a Gaussian data center value in the Gaussian data table of the previous frame;

[0052] Step S12: determining a first weight value between data of a prediction area in the Gaussian data table of the current frame and data of a reference area in the Gaussian data table of the previous frame, and determining a second weight value between data of a prediction area in the Gaussian data table of the current frame and data of a mapping area in the Gaussian data table of the previous frame, wherein the prediction area is a first preset area around a Gaussian data center value in the Gaussian data table of the current frame, and the reference area is a second preset area around a Gaussian data center value in the Gaussian data table of the previous frame, the first preset area and the second preset area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area by using the offset amount;

[0053] Step S13: determining the prediction value of each data in the prediction area according to the first weight value and the second weight value;

[0054] Step S14: Subtract the corresponding prediction value from each data in the prediction area to obtain the residual value of each data;

[0055] Step S15: compressing the residual values ​​of each data in the prediction area and each data outside the prediction area to obtain compressed data.

[0056] It is understandable that, in a specific implementation, the method can be implemented in the form of a software program, which runs in a processor integrated inside a chip or a chip module; or, the method can be implemented in the form of hardware or a combination of hardware and software.

[0057] In the specific implementation of step S11, the Gaussian data table can be various data tables showing Gaussian distribution characteristics and inter-frame translation characteristics. Specifically, Gaussian distribution (Gaussian distribution) can also be called normal distribution (Normal distribution). The Gaussian distribution curve is bell-shaped, low at both ends and high in the middle. The peak of the curve is located in the center, centered on the mean, symmetrical on the left and right, starting from the location of the mean, and gradually and evenly descending to the left and right sides. Therefore, the number of data with large values ​​in the Gaussian data table is large and relatively concentrated. The Gaussian data center value is the maximum value of each data in the Gaussian data table, that is, the peak value of the Gaussian distribution curve.

[0058] Specifically, as a typical Gaussian data table with Gaussian distribution characteristics, the local dimming PSF data table is a brightness data table used by the local dimming module of the digital signal processor (DSP) in the chips of various display terminals such as TV chips, mobile phone chips, and watch chips. This data table can be divided into a certain number of light zones by lighting each light zone (or each backlight lamp) in turn, lighting one backlight lamp at a time, and measuring the brightness value of each light zone on the backlight plate. The result of arranging all the measured brightness values ​​is the local dimming PSF data table. The original data value of each data in the single-frame local dimming PSF data table corresponds to the brightness value of each light zone on the backlight plate after lighting a single backlight lamp; the Gaussian data center value in the local dimming PSF data table corresponds to the maximum value of the brightness value of each light zone on the backlight plate.

[0059] Specifically, the previous frame Gaussian data table and the current frame Gaussian data table may be two frames of brightness data tables obtained after two backlights adjacent to each other on the left and right or adjacent to each other on the top and bottom are respectively lit.

[0060] In the specific implementation of step S12, in addition to the Gaussian center value (i.e., the maximum value) of the Gaussian data table, the first preset area and the second preset area should also contain at least other data with relatively large values ​​in the Gaussian data table. In different application scenarios, the shape and data volume of the first preset area and the second preset area may be different. For example, for display terminals, due to the different display screen sizes and resolutions of different display terminals, there may be dozens to hundreds of light zones on the backlight panel, so the amount of data in the single-frame local dimming PSF data table obtained each time is also different. For high-end TV chips with large screens or high resolution requirements, they are more sensitive to resolution and display effects, the light zone division and measurement are more refined, and the local dimming PSF data table is larger, so the prediction area needs to be larger; for chips such as mobile phones and watches with lower display requirements, the prediction area may be smaller.

[0061] It should be noted that, for the local dimming PSF data table, the shape and data volume of the first preset area and the second preset area are positively correlated with the brightness radiation range of the illuminated backlight.

[0062] Specifically, the offset is a vector including a direction and a value. The offset between the Gaussian data center value in the current frame Gaussian data table and the Gaussian data center value in the previous frame Gaussian data table refers to the vector from the Gaussian data center value of the current frame to the Gaussian data center value of the previous frame.

[0063] In some non-limiting embodiments, the first preset area and the second preset area may both be areas composed of M×N data, where M and N are both positive integers, and M and N may be the same or different; the first preset area and the second preset area may be hexagonal areas with the same shape and data volume, or other regular polygonal or irregular areas with the same shape and data volume. The embodiment of the present invention does not limit the specific selection of the first preset area and the second preset area.

[0064] Reference Figure 2 , Figure 2 A schematic diagram of a local dimming PSF data table according to an embodiment of the present invention.

[0065] The upper and lower local dimming PSF data tables represent the previous frame Gaussian data table and the current frame Gaussian data table respectively. The 12×16 data in each local dimming PSF data table correspond to the brightness values ​​of the 12×16 light zones on the backlight panel. Figure 2 From the size and distribution of each data in the two-frame local dimming PSF data table, it can be found that the data with large values ​​in the table are distributed more concentratedly, and each table has a peak value (that is, the center value of the Gaussian data). The farther the area is from the peak, the faster the value decreases, showing the overall Gaussian distribution characteristics.

[0066] In addition, there is an obvious inter-frame translation feature between the data in the local dimming PSF data tables of the previous and next frames, and the translation direction is the row scanning direction (or the direction of lighting the backlight from left to right). Specifically, the 4th data in the 1st row of the local dimming PSF data table of the previous frame is the peak data (Gaussian data center value), and the 5th data in the 1st row of the local dimming PSF data table of the next frame is the peak data (Gaussian data center value), and the data within a certain range around the peak data in the two frames of local dimming PSF data tables also have the same offset relationship. Therefore, it can be determined that the value of the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame is 1, and the direction is from right to left.

[0067] In the local dimming PSF data table of the upper and lower frames, 8×8 data with larger values ​​including the peak value (areas outlined by dotted lines) are selected as the reference area and the prediction area respectively. Then, the reference area in the previous frame is offset by the offset (i.e., shifted by 1 bit from right to left) to obtain the mapping area, so that the prediction area of ​​the current frame and the mapping area of ​​the previous frame are in the same position in the local dimming PSF data table, and the position of each data in the prediction area corresponds to and is the same as the position of each data in the mapping area.

[0068] After determining the prediction area of ​​the current frame, the reference area of ​​the previous frame and the mapping area, respectively calculate the first weight value between each data in the prediction area of ​​the current frame and each data in the reference area of ​​the previous frame, and the second weight value between each data in the prediction area of ​​the current frame and each data in the mapping area of ​​the previous frame; determine the prediction value of each brightness data in the prediction area according to the calculated double weight value; then subtract the corresponding prediction value from the original data value of each data in the prediction area to obtain the residual value of each data; finally, compress the residual value in the prediction area of ​​the current frame and the original data value of each data outside the prediction area (non-shadow area) to obtain compressed data.

[0069] Continue to refer to Figure 1 In the specific implementation of step S13, the first weight value can be used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the reference area; the second weight value is used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the mapping area. The larger the first weight value or the second weight value, the higher the degree of correlation between the data in each area.

[0070] In the specific implementation of step S14, the residual value of each data is obtained by subtracting the corresponding prediction value from the data in the prediction area, specifically, the residual value of each data is obtained by subtracting the corresponding prediction value from the original data value of each data in the prediction area.

[0071] It can be understood that compared with the original data value of each data, the residual value of each data will be obviously smaller. In the subsequent data transmission, for each data in the prediction area, it is only necessary to encode its residual value, and the number of encoding bits is reduced. Especially when the original data value of each data in the prediction area is large and occupies more bits, by encoding the residual value instead of the original data value, the number of bits occupied by the data can be greatly reduced.

[0072] In the specific implementation of step S15, when performing compression processing, for the Gaussian data of the current frame, only the original data values ​​of each data in the prediction area are replaced with residual values, the original data values ​​of each data outside the prediction area are generally small, and the values ​​have not changed, and the positions of each data in the prediction area and each data outside the prediction area in the Gaussian data table of the current frame remain unchanged.

[0073] In the embodiment of the present invention, compared with various existing data compression technologies, intra-frame prediction cannot achieve a good compression effect for a Gaussian data table with Gaussian distribution characteristics; inter-frame prediction often requires obtaining the best inter-frame motion vector MV, and the best MV search process will greatly increase the hardware complexity; and when the variable-length coding technology is adopted, since there may be a lot of data with large values ​​in each frame of the Gaussian data table, it may cause the data table to occupy more bits overall, and it is impossible to save storage space or bandwidth resources; and the various existing entropy coding technologies involve some complex calculation steps, which will occupy too much chip area of ​​the display module and increase the calculation overhead; this In addition, the quantization method used in the transform coding technology will cause a certain loss of information; the embodiment of the present invention starts from the Gaussian distribution and inter-frame translation law of the Gaussian data table itself, and directly obtains the predicted value of each data in the current frame prediction area through a dual weight coefficient calculation method, and then calculates the residual value of each data in the prediction area, and finally compresses the residual value of each data in the current frame prediction area and the original data value of other data outside the prediction area, which can avoid the increase in hardware design complexity and computing cost caused by entropy coding and MV calculation process, and ensure the integrity of the data, effectively reduce the storage space occupied by data, and save bandwidth resources.

[0074] Reference Figure 3 , Figure 3 yes Figure 1 A specific implementation flow chart of step S15 in the above embodiment. Compressing the residual values ​​of each data in the prediction area and each data outside the prediction area to obtain compressed data may include steps S31 to S34, and each step is described below.

[0075] In step S31, the residual values ​​of each data in the prediction area and each data outside the prediction area are divided into groups to obtain one or more data groups.

[0076] Further, the residual values ​​of each data within the prediction area and each data outside the prediction area are divided into groups to obtain one or more data groups selected from any one of the following: the residual values ​​of each data within the prediction area and each data outside the prediction area are evenly divided to obtain one or more data groups; the residual values ​​of each data within the prediction area and each data outside the prediction area are divided into a group of P×Q data to obtain one or more data groups, wherein P and Q are both positive integers, and P and Q may be the same or different.

[0077] In a specific implementation, when performing group division, the above two division methods are only used as non-limiting embodiments. A suitable division method can also be used to divide the residual values ​​of each data within the prediction area and each data outside the prediction area into groups. The embodiment of the present invention does not impose any restrictions on this.

[0078] In step S32, for each data group, the maximum value among the absolute values ​​of the individual data in the data group is determined.

[0079] In step S33, the number of coding bits for each data in the data group is determined according to the maximum value among the absolute values ​​of each data in the data group, and each data in the data group is encoded respectively using the coding bit number.

[0080] Further, determining the number of coding bits for each data in the data group based on the maximum value among the absolute values ​​of each data in the data group includes: if there are negative numbers in the data in the data group, then adding one bit to the number of bits occupied by the maximum value among the absolute values ​​of each data in the data group as the number of coding bits for each data in the data group; if there are no negative numbers in the data in the data group, then using the number of bits occupied by the maximum value among the absolute values ​​of each data in the data group as the number of coding bits for each data in the data group.

[0081] As a non-limiting embodiment, for the current frame Gaussian data table, after the residual values ​​of each data in the prediction area and each data outside the prediction area are grouped to obtain multiple data groups, the maximum value of the absolute values ​​of each data in a data group is 265, occupying 9 bits (the specific calculation method can be, for example: N is the maximum value among the absolute values ​​of each data. is the round-up sign), and there are negative numbers in the data group, 1 sign bit needs to be added, so 10 bits are used to encode each data in the group during encoding; and the maximum value of the absolute value of each data in the other data group is 3, and there are no negative numbers. At this time, only 3 bits are needed to encode each data in the data group, which is much smaller than the 16 bits required for fixed-length encoding, thereby greatly reducing the pressure on hardware bandwidth occupancy.

[0082] In step S34, the data obtained by encoding each data group in the Gaussian data table of the current frame is used as the compressed data.

[0083] In the embodiment of the present invention, by grouping the data in the Gaussian data table of the current frame, a shorter encoding bit number is used to encode the smaller data, and a longer encoding bit number is used to encode the larger data, so that the data can be effectively compressed and the storage space and bandwidth resources can be saved. In addition, when performing group division, the number of divided data groups can be adjusted according to the different requirements of the specific application scenario, so as to achieve the optimal compression efficiency and compression effect.

[0084] In the specific implementation, for more details about steps S31 to S34, please refer to the above and Figure 1 , Figure 2 The relevant descriptions in the description are executed and will not be repeated here.

[0085] Reference Figure 4 , Figure 4 4 is a flow chart of another data compression method in an embodiment of the present invention. The another data compression method may include steps S41 to S46, and each step is described below.

[0086] In step S41, the data in the Gaussian data table of the previous frame and the Gaussian data table of the current frame are determined, and the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame is determined.

[0087] In step S42, a first correlation coefficient and a first scaling value are determined between the data of the prediction area in the Gaussian data table of the current frame and the data of the reference area in the Gaussian data table of the previous frame, and a second correlation coefficient and a second scaling value are determined between the data of the prediction area in the Gaussian data table of the current frame and the data of the mapping area in the Gaussian data table of the previous frame.

[0088] Among them, the prediction area is a first preset area around the Gaussian data center value in the Gaussian data table of the current frame, and the reference area is a second preset area around the Gaussian data center value in the Gaussian data table of the previous frame. The shape and data amount of the first preset area and the second preset area are consistent, and the mapping area is obtained by offsetting the reference area using the offset amount.

[0089] The first correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the reference area, and the second correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the mapping area.

[0090] Further, the first correlation coefficient and the second correlation coefficient are Pearson correlation coefficients; the first correlation coefficient and the first scaling value between the data of the prediction area and the data of the reference area are determined by the following formula:

[0091]

[0092]

[0093] Wherein, r1 is used to indicate the first correlation coefficient between the data of the prediction area and the data of the reference area; X i It is used to indicate the original data value of the i-th data in the prediction area, Y i is used to indicate the original data value of the i-th data in the reference area; N is used to indicate the total amount of data in the prediction area or the reference area; scl1 is used to indicate the first scaling value between the data in the prediction area and the data in the reference area;

[0094] And / or using the following formula to determine the second correlation coefficient and the second scaling value between the data in the prediction area and the data in the mapping area:

[0095]

[0096]

[0097] Wherein, r2 is used to indicate the second correlation coefficient between the data in the prediction area and the data in the mapping area; j It is used to indicate the original data value of the jth data in the prediction area, Z j It is used to indicate the original data value of the jth data in the mapping area; N is used to indicate the total amount of data in the prediction area or the mapping area; scl2 is used to indicate the second scaling value between the data in the prediction area and the data in the mapping area.

[0098] Specifically, the Pearson correlation coefficient (PCC), also known as the Pearson product-moment correlation coefficient (PPMCC), is a coefficient used to measure the degree of correlation (linear correlation) between two variables X and Y, and its value is between -1 and 1. A value of 1 for this coefficient means that X and Y can be well described by a straight line equation, all data points fall well on a straight line, and Y increases as X increases; a value of -1 for this coefficient means that all data points fall on a straight line, and Y decreases as X increases; a value of 0 for this coefficient means that there is no linear relationship between X and Y.

[0099] In step S43, a first weight value between the data of the prediction area and the data of the reference area is determined according to the first correlation coefficient, the second correlation coefficient, and the first scaling value, and a second weight value between the data of the prediction area and the data of the mapping area is determined according to the first correlation coefficient, the second correlation coefficient, and the second scaling value.

[0100] Further, the first weight value is determined according to the first correlation coefficient, the second correlation coefficient, and the first scaling value using the following formula:

[0101]

[0102] And / or using the following formula to determine the second weight value according to the first correlation coefficient, the second correlation coefficient, and the second scaling value:

[0103]

[0104] Among them, w1 is used to indicate the first weight value; scl1 is used to indicate the first scaling value; w2 is used to indicate the second weight value; scl2 is used to indicate the second scaling value; r1 is used to indicate the first correlation coefficient; r2 is used to indicate the second correlation coefficient.

[0105] In step S44, the prediction value of each data in the prediction area is determined according to the first weight value and the second weight value.

[0106] Further, the following formula is used to determine the predicted value of each data in the predicted area according to the first weight value and the second weight value:

[0107] X i '=w1Y i +w2Z i ;

[0108] Among them, X i 'Used to indicate the predicted value of the i-th data in the prediction area; Y i It is used to indicate the original data value of the i-th data in the reference area; i Used to indicate the original data value of the i-th data in the mapping area.

[0109] In step S45, the corresponding prediction value is subtracted from each data in the prediction area to obtain a residual value of each data.

[0110] In step S46, the residual values ​​of each data in the prediction area and each data outside the prediction area are compressed to obtain compressed data.

[0111] In the specific implementation, for more details about steps S41 to S46, please refer to the above and Figures 1 to 3 The steps described in the procedure are executed and will not be repeated here.

[0112] Reference Figure 5 , Figure 5 1 is a schematic diagram of the structure of a data compression device in an embodiment of the present invention. The data compression device may include:

[0113] The Gaussian feature data and offset determination module 51 is used to determine the data in the Gaussian data table of the previous frame and the Gaussian data table of the current frame, and to determine the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame;

[0114] A weight value determination module 52, used to determine a first weight value between data of a prediction area in the Gaussian data table of the current frame and data of a reference area in the Gaussian data table of the previous frame, and to determine a second weight value between data of a prediction area in the Gaussian data table of the current frame and data of a mapping area in the Gaussian data table of the previous frame, wherein the prediction area is a first preset area around a Gaussian data center value in the Gaussian data table of the current frame, and the reference area is a second preset area around a Gaussian data center value in the Gaussian data table of the previous frame, the first preset area and the second preset area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area using the offset;

[0115] A prediction value determination module 53, configured to determine a prediction value of each data in the prediction area according to the first weight value and the second weight value;

[0116] A residual value calculation module 54 is used to obtain a residual value of each data by subtracting the corresponding prediction value from each data in the prediction area;

[0117] The compression processing module 55 is used to compress the residual value of each data in the prediction area and each data outside the prediction area to obtain compressed data.

[0118] For the principle, specific implementation and beneficial effects of the data compression device, please refer to the previous article and Figures 1 to 4 The related description about the data compression method shown is not repeated here.

[0119] In a specific implementation, the above-mentioned data compression device may correspond to a chip with a data compression function; or correspond to a chip module with a data compression function in a terminal, or correspond to a terminal.

[0120] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above data compression method are executed. The computer-readable storage medium may include a non-volatile memory or a non-transitory memory, and may also include an optical disk, a mechanical hard disk, a solid-state hard disk, etc.

[0121] Specifically, in the embodiment of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM) and direct rambus RAM (DR RAM).

[0123] The embodiment of the present invention further provides a terminal, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor executes the steps of the above data compression method when running the computer program. The terminal may include but is not limited to terminal devices such as mobile phones, computers, tablet computers, watches, etc., and may also be a server, a cloud platform, etc.

[0124] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless means.

[0125] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units. For example, for each device or product applied to or integrated in a chip, each module / unit contained therein may be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for each device or product applied to or integrated in a chip module, each module / unit contained therein may be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be implemented in the form of software programs. The element can be implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0127] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0128] The "plurality" appearing in the embodiments of the present application refers to two or more.

[0129] The first, second, etc. descriptions appearing in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor do they indicate any special limitation on the number of devices in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application.

[0130] It should be pointed out that the serial numbers of the steps in this embodiment do not limit the execution order of the steps.

[0131] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A data compression method, characterized in that: include: Determine the data of the Gaussian data table of the previous frame and the Gaussian data table of the current frame, and determine the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame; Determine a first weight value between data of a prediction area in the Gaussian data table of the current frame and data of a reference area in the Gaussian data table of the previous frame, and determine a second weight value between data of a prediction area in the Gaussian data table of the current frame and data of a mapping area in the Gaussian data table of the previous frame, wherein the prediction area is a first preset area around a Gaussian data center value in the Gaussian data table of the current frame, and the reference area is a second preset area around a Gaussian data center value in the Gaussian data table of the previous frame, the first preset area and the second preset area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area using the offset; Determining a prediction value of each data in the prediction area according to the first weight value and the second weight value; Subtract the corresponding prediction value from each data in the prediction area to obtain the residual value of each data; Compressing the residual values ​​of each data in the prediction area and each data outside the prediction area to obtain compressed data; Among them, the first weight value is used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the reference area; the second weight value is used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the mapping area.

2. The method according to claim 1, characterized in that Determining a first weight value between data of a prediction area in the current frame Gaussian data table and data of a reference area in the previous frame Gaussian data table, and determining a second weight value between data of a prediction area in the current frame Gaussian data table and data of a mapping area in the previous frame Gaussian data table includes: determining a first correlation coefficient and a first scaling value between the data of the prediction area and the data of the reference area, and determining a second correlation coefficient and a second scaling value between the data of the prediction area and the data of the mapping area; Determine the first weight value according to the first correlation coefficient, the second correlation coefficient, and the first scaling value, and determine the second weight value according to the first correlation coefficient, the second correlation coefficient, and the second scaling value; The first correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the reference area, and the second correlation coefficient is used to indicate the degree of correlation between the data of the prediction area and the data of the mapping area.

3. The method according to claim 2, characterized in that The first correlation coefficient and the second correlation coefficient are Pearson correlation coefficients; The following formula is used to determine the first correlation coefficient and the first scaling value between the data in the prediction area and the data in the reference area: ; ; in, for indicating a first correlation coefficient between the data of the prediction area and the data of the reference area; It is used to indicate the first The original data value of the data, For indicating the first The original data value of each data; used to indicate the total amount of data in the prediction area or the reference area; for indicating a first scaling value between the data of the prediction area and the data of the reference area; And / or using the following formula to determine the second correlation coefficient and the second scaling value between the data in the prediction area and the data in the mapping area: ； ； in, a second correlation coefficient for indicating the data of the prediction area and the data of the mapping area; It is used to indicate the first The original data value of the data, Used to indicate the first The original data value of each data; Used to indicate the total amount of data in the prediction area or the mapping area; A second scaling value is used to indicate between the data of the prediction area and the data of the mapping area.

4. The method according to claim 2, characterized in that: The first weight value is determined according to the first correlation coefficient, the second correlation coefficient, and the first scaling value using the following formula: ； And / or using the following formula to determine the second weight value according to the first correlation coefficient, the second correlation coefficient, and the second scaling value: ； in, used to indicate the first weight value; used to indicate the first scaling value; used to indicate the second weight value; used to indicate the second scaling value; used to indicate the first correlation coefficient; Used to indicate the second correlation coefficient.

5. The method according to claim 1, characterized in that The following formula is used to determine the predicted value of each data in the predicted area according to the first weight value and the second weight value: ; in, It is used to indicate the first The predicted value of data; For indicating the first The original data value of each data; Used to indicate the first The original data value of the data.

6. The method according to claim 1, characterized in that Compressing the residual values ​​of each data in the prediction area and each data outside the prediction area to obtain compressed data includes: Dividing the residual values ​​of each data within the prediction area and each data outside the prediction area into groups to obtain one or more data groups; For each data group, determine the maximum value among the absolute values ​​of the individual data in the data group; Determine the number of coding bits for each data in the data group according to the maximum value among the absolute values ​​of each data in the data group, and respectively encode each data in the data group using the number of coding bits; The data obtained by encoding each data group in the Gaussian data table of the current frame is used as the compressed data.

7. The method according to claim 6, characterized in that The residual values ​​of each data in the prediction area and each data outside the prediction area are divided into groups to obtain one or more data groups selected from any one of the following: Evenly dividing the residual values ​​of each data within the prediction area and each data outside the prediction area to obtain one or more data groups; The residual values ​​of each data within the prediction area and each data outside the prediction area are divided into a group of P×Q data to obtain one or more data groups, wherein P and Q are both positive integers.

8. The method according to claim 6, characterized in that Determining the number of encoding bits of each data in the data group according to the maximum value of the absolute values ​​of each data in the data group comprises: If there are negative numbers in the data group, the number of bits occupied by the maximum value of the absolute values ​​of the data in the data group plus one bit is used as the number of bits for encoding the data in the data group; If there are no negative numbers in the data set, the number of bits occupied by the maximum value of the absolute values ​​of the data in the data set is used as the number of encoding bits for the data in the data set.

9. The method according to claim 1, characterized in that: The Gaussian data table is brightness data obtained by collecting brightness values ​​on the backlight panel after lighting one or more backlight lamps on the backlight panel. The Gaussian data table also satisfies one or more of the following: The Gaussian data center value in the Gaussian data table corresponds to the maximum value of the brightness value on the backlight panel; The Gaussian data table is a local dimming point spread function PSF data table.

10. The method according to claim 1, characterized in that The first preset area and the second preset area are both areas consisting of M×N data, where M and N are both positive integers.

11. A data compression device, characterized in that: include: A Gaussian feature data and offset determination module, used to determine the data in the Gaussian data table of the previous frame and the Gaussian data table of the current frame, and to determine the offset between the Gaussian data center value in the Gaussian data table of the current frame and the Gaussian data center value in the Gaussian data table of the previous frame; A weight value determination module, used to determine a first weight value between data of a prediction area in the Gaussian data table of the current frame and data of a reference area in the Gaussian data table of the previous frame, and to determine a second weight value between data of a prediction area in the Gaussian data table of the current frame and data of a mapping area in the Gaussian data table of the previous frame, wherein the prediction area is a first preset area around a Gaussian data center value in the Gaussian data table of the current frame, and the reference area is a second preset area around a Gaussian data center value in the Gaussian data table of the previous frame, the first preset area and the second preset area have the same shape and data volume, and the mapping area is obtained by offsetting the reference area using the offset; A prediction value determination module, used to determine the prediction value of each data in the prediction area according to the first weight value and the second weight value; A residual value calculation module, used for subtracting the corresponding prediction value from each data in the prediction area to obtain the residual value of each data; A compression processing module, used for compressing the residual value of each data in the prediction area and each data outside the prediction area to obtain compressed data; Among them, the first weight value is used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the reference area; the second weight value is used to indicate the degree of correlation or similarity between each data in the prediction area and each data in the mapping area.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data compression method according to any one of claims 1 to 10 are executed.

13. A terminal comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the processor performs the steps of the data compression method according to any one of claims 1 to 10.

14. The terminal according to claim 13, characterized in that: The terminal is a display terminal, and the display terminal comprises a backlight panel and a plurality of backlight lamps; Wherein, when the backlight is turned on, the brightness value of at least a part of the area on the backlight panel increases, and each time a backlight is turned on, the brightness value of each light area on the backlight panel is measured, and all the measured brightness values ​​are arranged to obtain a local dimming PSF data table, and the local dimming PSF data table is the Gaussian data table with Gaussian distribution characteristics; The light area is obtained by dividing the backlight panel.

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