Dynamic HDR down-conversion method, device and medium based on human eye adaptation curve

Abstract

The present invention discloses a dynamic HDR down-conversion method, device and medium based on human eye adaptation curve, which belongs to the field of ultra-high-definition video, including the steps of: S1, scene segmentation; S2, determining the scene that needs to be smoothly transitioned and the required smooth transition time; S3, calculating the average maximum brightness L1 and L2 of the high-brightness scene C1 and the low-brightness scene C2 in each frame; S4, calculating the values ​​of the highlight proportion R1 and R2 of the high-brightness scene C1 and the low-brightness scene C2 in each frame; S5, calculating the corresponding conversion curve according to L1 and R1 of scene C1 in each frame and L2 and R2 of scene C2 in each frame during the transition period. The present invention makes the conversion of high and low brightness scenes smoother, conforms to the physiological laws of the human eye, makes the effect of ultra-high-definition video being converted from HDR to a lower dynamic range more natural and stable, and brings a better viewing experience.

Description

Technical Field

[0001] The present invention relates to the field of ultra-high definition video, and more specifically, to a dynamic HDR down-conversion method, device and medium based on a human eye adaptation curve. Background Art

[0002] The existing HDR down-conversion technology based on the PQ curve dynamically transforms with reference to the video metadata, introduces the electrical signal and electrical signal conversion curve EETF after EOTF, and compresses the brightness range of 0 to 100,000 nit to the target brightness range. This down-conversion effect is good and the conversion range is large, but it has high requirements on the display terminal.

[0003] The existing HDR down-conversion technology based on the HLG curve is to transform the dynamic range of the entire video according to a unified transformation formula. The original HDR based on the HLG curve supports a brightness range of 0 to 1000nit. The original video is completely mapped and compressed into the range of the display terminal in proportion to the maximum brightness that the display device can present. (For example, if the original video range is 0-1200nit and the TV has a maximum brightness of 600nit, then HLG will be mapped at a ratio of 1 / 2.) The advantage of this transformation method is that the transformation process is simple, there is no need to distinguish between scenes in the video, the entire video is transformed according to a unified curve, and metadata is not used. It has low requirements for display terminals and a wide range of applications, but the effect is not as good as PQ.

[0004] The above two existing down-conversion methods have the following technical problems: ① the universality of terminals is poor; ② the viewing experience needs to be improved. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a dynamic HDR down-conversion method, device and medium based on the human eye adaptation curve, so that the conversion of high and low brightness scenes becomes smoother, in line with the physiological laws of the human eye, and the effect of ultra-high-definition video being converted from HDR to a lower dynamic range is more natural and smooth, bringing a better viewing experience.

[0006] The object of the present invention is achieved through the following solutions:

[0007] A dynamic HDR down-conversion method based on a human eye adaptation curve comprises the following steps:

[0008] S1, perform scene segmentation based on metadata, divide the video into continuous segments, and record the average maximum brightness L and highlight ratio R of each scene;

[0009] S2, determine the scene that needs smooth transition and the required smooth transition time according to the human eye adaptation curve;

[0010] S3, calculating the average maximum brightness L1 and L2 of the high brightness scene C1 and the low brightness scene C2 in each frame according to the smooth transition time calculated in step S2 and the fitted human eye adaptation curve;

[0011] S4, calculating the values ​​of the highlight proportions R1 and R2 of the high-brightness scene C1 and the low-brightness scene C2 in each frame according to the smooth transition time calculated in step S2;

[0012] S5, calculating corresponding transformation curves according to L1 and R1 of scene C1 in each frame and L2 and R2 of scene C2 in each frame during the transition period.

[0013] Furthermore, in step S2, the sub-steps are included:

[0014] S21, according to the brightness value on the human eye adaptation curve, the following formula is designed:

[0015] y(L)=a1+a2log 10 (L) (1)

[0016] Wherein, y represents the ordinate of the selected brightness starting point on the human eye adaptation curve after logarithmic processing, a1 is a constant, and a2 is a constant;

[0017] S22, selecting a sample of the fitting curve, and calculating the un-logarithmized ordinate brightness value inversely according to the formula (1) designed in step S21;

[0018] S23, using the real brightness value obtained in step S22, and selecting the adaptation curve with the set value nit brightness as the initial brightness to fit the following formula (2):

[0019]

[0020] The function of the average maximum brightness difference between scenes with respect to the adaptation time is obtained; wherein t represents the time, b1 is a constant, b2 is a constant, and b3 is a constant;

[0021] S24, find the inverse function according to the formula in step S23 to obtain formula (3):

[0022]

[0023] The adaptation time t can be calculated from ΔL in formula (3):

[0024] S25, calculating the brightness difference L1-L2 between two adjacent scenes. If the absolute value of the difference is greater than a set value, a smooth transition of the scenes is required; otherwise, a smooth transition is not required.

[0025] Further, in step S3, calculating the value of the average maximum brightness L1 of the high brightness scene C1 in each frame includes the sub-steps of: designing the maximum brightness moving iteration formula of the C1 scene:

[0026] L1 n =L1 n-1 -ΔL(t1+n / (60·f)) (4)

[0027] in, The initial moving brightness is L10 = L1, and the corresponding time in the transition interval is given by formula (3), t(L10) = t1; if If it is not an integer, it will be rounded up or down. f is the frame rate.

[0028] Further, in step S3, calculating the average maximum brightness L2 of the low brightness scene C2 in each frame includes the sub-steps of: designing the maximum brightness moving iteration formula of the C2 scene as:

[0029] L2 n =L2 n-1 +ΔL(t2+n / (60·f)) (5)

[0030] in, The initial moving brightness is L20=L2, and t2 is the corresponding time in the transition interval given by formula (3), that is, t(L20).

[0031] Further, in step S4, calculating the value of the highlight ratio R1 of the high brightness scene C1 in each frame includes the sub-steps of: designing an iterative formula for the highlight ratio of the C1 scene:

[0032]

[0033] in The initial moving highlight ratio is R10=R1, the original highlight ratio of scene C1 is R1, and the original highlight ratio of scene C2 is R2. Take the difference between the two and record ΔR=R1–R2.

[0034] Further, in step S4, calculating the value of the highlight ratio R2 of the low brightness scene C2 in each frame includes sub-steps: designing an iterative formula for the highlight ratio of the C2 scene:

[0035]

[0036] middle The starting moving highlight ratio is R20=R2.

[0037] Further, in step S5, the calculation of the corresponding transformation curve includes the sub-steps of: designing the following formula:

[0038]

[0039] Among them, c1, c2, c3, c4, c5, c6, and c7 are all constants; m0, m1, and m2 are determined by R' and L; R' is the highlight ratio of each frame calculated by the iterative formula in step S4, L is the average maximum brightness of each frame calculated by the iterative formula in step S3, and E is the independent variable, that is, the brightness coordinate axis in the OETF curve.

[0040] Furthermore, m0, m1, and m2 are calculated using the following formula:

[0041]

[0042] Among them, d1, d2, d3, d4, and d5 are all constants.

[0043] A computer device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, the method according to any one of claims 1 to 8 is executed.

[0044] A computer-readable storage medium stores a computer program, wherein the computer program is loaded by a processor and executes the method according to any one of claims 1 to 8.

[0045] The beneficial effects of the present invention include:

[0046] The embodiments of the present invention solve the problem of adapting to the human eye adaptation law that is not considered in the existing down-conversion technology, thereby making the conversion of high and low brightness scenes smoother, in line with the physiological laws of the human eye, and making the effect of ultra-high-definition video down-converted from HDR to a lower dynamic range more natural and smooth, bringing a better viewing experience.

[0047] The embodiment of the present invention takes into account the universality of terminals and studies a dynamic range down-conversion method for smooth transition between scenes based on interval inflection point transformation. The embodiment method uses metadata to segment video scenes, and at the same time refers to the metadata to fit the best dynamic range down-conversion curve for different scenes, and takes into account the viewing experience of the human eye during scene changes, thereby achieving smooth transition of dynamic range transformation between scenes in the video. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 is a flowchart of the steps of the method according to the embodiment of the present invention;

[0050] Figure 2 A function of the average maximum brightness difference between scenes provided by the method of the embodiment of the present invention with respect to the adaptation time;

[0051] Figure 3 : This is the adaptation curve of the Stokkerman human eye at different initial light brightnesses selected in the embodiment of the present invention. DETAILED DESCRIPTION

[0052] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0053] The following is based on the attached Figure 1-2 , further detailed and comprehensive explanation is given to the technical concept, technical problems solved, working principle, working process and beneficial effects of the present invention.

[0054] In the process of solving the technical problems in the background, the embodiments of the present invention found that the two existing down-conversion methods encountered the following problems: when ultra-high-definition video is down-converted from HDR to a lower dynamic range, the display effect is abrupt and unnatural. After further analysis, the present invention believes that both existing down-conversion methods only consider the scene, but do not consider the smooth transition of the dynamic range between scenes. Therefore, it is very necessary to perform a smooth transition of brightness in scenes with strong brightness contrast.

[0055] Based on the recognition of the above problems, the embodiments of the present invention further consider how to solve the problem of adapting to the human eye adaptation law, and specifically in the actual application process, include the following implementation methods:

[0056] In step S1, scene segmentation is performed according to metadata, the entire video is divided into continuous segments Chapter, and the average maximum brightness L and highlight ratio R of each scene are recorded. (For example, the maximum brightness of scene 1 is L1, and the highlight ratio is R1).

[0057] According to the brightness range of 0 to 1200 nits that HLG can display, the embodiment of the present invention selects the curve starting at 800 nit in the human eye brightness adaptation curve, and selects data points in the high brightness area to fit with an exponential function to obtain a curve of brightness with respect to adaptation time. Specifically, in step S2, the smooth transition time can be determined according to the human eye adaptation curve, and whether a smooth transition is required can be determined based on this.

[0058] In step S21, the conversion relationship between the ordinate y and the actual light brightness L after logarithmic processing of the brightness starting points (25, -3), (800, -1.5), (6000, -0.6), (12000, -0.4), and (125000, 0.6) on the human eye adaptation curve can be obtained by the following formula:

[0059] y(L)=a1+a2log 10 (L) (1)

[0060] Among them, a1=-4.3397, a2=0.4222.

[0061] In step S22, points (0, -1.5), (1, -2.75), and (2, -3) in the curve where the initial brightness of the human eye adaptation curve is 800 nits can be selected as samples of the fitting curve. Substituting -1.5, -2.75, and -3 into the inverse function of formula (1), respectively, the actual brightness values ​​are obtained, which are 825.8620 nits, 42.9437 nits, and 23.7738 nits, respectively.

[0062] In step S23, the following formula can be fitted using the points (0, 825.8620), (1, 42.9437), and (2, 23.7738) to obtain a function of the average maximum brightness difference between scenes with respect to the adaptation time:

[0063]

[0064] Among them, b1=23.293, b2=802.569, b3=3.7097, which is the function of the average maximum brightness difference between scenes with respect to the adaptation time. Substituting into formula (2), we can get its function curve as follows: Figure 2 shown.

[0065] In step S24, the adaptation time t can be calculated based on the inverse function of function (2), as follows, by knowing ΔL.

[0066]

[0067] In step S25, the brightness difference L1-L2 between two adjacent scenes is calculated. If the absolute value of the difference is greater than 100 nit, a smooth transition of the scenes is required; otherwise, a smooth transition is not required.

[0068] According to the function obtained by fitting in the above steps, the embodiment of the present invention designs an iterative idea and formula that conforms to the slope variation law, and dynamically adjusts the average maximum brightness of the transition scene in each frame in the smooth transition area, so that the bright scene and the dark scene that need to transition reach the same maximum brightness at the connection point. Specifically, in step S3, according to the calculated smooth transition time and the fitted human eye adaptation curve, the average maximum brightness L1 and L2 of the high-brightness scene C1 and the low-brightness scene C2 are calculated at each step. Specifically, it includes the following sub-steps:

[0069] In step S31, it is known that:

[0070] 1) The frame rate is f;

[0071] 2) The total time required for C1 to smoothly transition to C2 is t, the time interval for C1 to participate in the scene transition is its last t / 2 seconds, and the time interval for C2 to participate in the scene transition is its first t / 2 seconds.

[0072] According to formula (3), it can be calculated that the initial moving brightness of scene C1 is the original average maximum brightness, and let L10 = L1, the corresponding time in the transition interval is given by formula (3), t(L10) = t1. Then the maximum brightness of each step is calculated by the iterative formula using formula (3), as shown in formula (4), and the total number of moving steps of the average maximum brightness of the highlight scene C1 is (Note that because the time unit of the horizontal axis of the function graph is minutes, unit conversion is required when moving the step length)

[0073] L1 n =L1 n-1 -ΔL(t1+n / (60·f)) (4)

[0074] in (if If it is not an integer, use the ceiling or floor function to process it).

[0075] S32 is similar to the steps in S31, and the highest brightness moving iteration formula of the C2 scene can be calculated as:

[0076] L2 n =L2 n-1 +ΔL(t2+n / (60·f)) (5)

[0077] in The initial moving brightness is L20=L2, and t2 is the corresponding time in the transition interval given by formula (3), that is, t(L20).

[0078] The embodiment of the present invention designs an iterative idea and formula for the average change of the highlight inflection point of each frame in the smooth transition area of ​​the transition scene. Specifically, in step S4, the values ​​of the highlight proportions R1 and R2 of the high-brightness scene C1 and the low-brightness scene C2 at each step are calculated according to the calculated smooth transition time. Specifically, the following sub-steps are included:

[0079] In step S41, it is known that:

[0080] 1) The original highlight ratio of scene C1 is R1, and the original highlight ratio of scene C2 is R2. Take the difference between the two and record ΔR=R1–R2.

[0081] 2) The proportion of highlights that need to change in C1 and C2 is ΔR / 2, and the total number of steps for the two scenes to move is the same as S4, which is

[0082] In step S42, the highlight ratio iteration formula of the design scene C1 is as follows:

[0083]

[0084] in The starting moving highlight ratio is R10=R1.

[0085] The iterative formula for the highlight ratio of the design scene C2 is as follows:

[0086]

[0087] in The starting moving highlight ratio is R20=R2.

[0088] The embodiment of the present invention combines the average maximum brightness and highlight inflection point of each frame obtained by iteration in the above steps, and introduces the transformation formula based on interval inflection point transformation to obtain the OETF of each frame in the smooth transition interval, so as to achieve the effect of smooth transition between scenes. Specifically, in step S5, the corresponding transformation curve is calculated according to L1 and R1 of scene C1 at each step, i.e., each frame, and L2 and R2 of scene C2 at each step, i.e., each frame in the transition time period. The specific calculation formula is given as follows:

[0089]

[0090] The constants are: c1=4.5, c2=0.018, c3=1.099, c4=0.45, c5=0.099, c6=1.07152, c7=0.01. R' and L are the highlight ratio and average maximum brightness of each frame calculated by the iterative formulas of steps S3 and S4.

[0091] Parameters m0, m1, and m2 are determined by R' and L, and the specific formula relationship is as follows:

[0092]

[0093] The constants are: d1=0.166667, d2=1.025646, d3=0.45, d4=0.01, d5=1.07152.

[0094] Embodiment 1: A dynamic HDR down-conversion method based on human eye adaptation curve, comprising the steps of:

[0095] S1, perform scene segmentation based on metadata, divide the video into continuous segments, and record the average maximum brightness L and highlight ratio R of each scene;

[0096] S2, determine the scene that needs smooth transition and the required smooth transition time according to the human eye adaptation curve;

[0097] S3, calculating the average maximum brightness L1 and L2 of the high brightness scene C1 and the low brightness scene C2 in each frame according to the smooth transition time calculated in step S2 and the fitted human eye adaptation curve;

[0098] S4, calculating the values ​​of the highlight proportions R1 and R2 of the high-brightness scene C1 and the low-brightness scene C2 in each frame according to the smooth transition time calculated in step S2;

[0099] S5, calculating corresponding transformation curves according to L1 and R1 of scene C1 in each frame and L2 and R2 of scene C2 in each frame during the transition period.

[0100] Embodiment 2: Based on Embodiment 1, in step S2, the following sub-steps are included:

[0101] S21, according to the brightness value on the human eye adaptation curve, the following formula is designed:

[0102] y(L)=a1+a2log 10 (L) (1)

[0103] Wherein, y represents the ordinate of the selected brightness starting point on the human eye adaptation curve after logarithmic processing, a1 is a constant, and a2 is a constant;

[0104] S22 selects samples of the fitting curve, and combines the formula (1) designed in step S21 to calculate the unlogarithmized ordinate brightness value; in this embodiment, points on the adaptation curve of the Stokkerman human eye at different initial brightness are selected as samples of the fitting curve. Figure 3 As shown, Figure 3Source: MGMStokkermans and I. Heynderickx (2014), Temporal dark adaptation to spatially complex backgrounds: effect of an additional light source. JOSAA V31#7, pp.1485-1494.).

[0105] S23, using the real brightness value obtained in step S22, and selecting a set value (for example, 800) nit brightness as the adaptation curve of the initial brightness to fit the following formula (2):

[0106]

[0107] The function of the average maximum brightness difference between scenes with respect to the adaptation time is obtained; wherein t represents the time, b1 is a constant, b2 is a constant, and b3 is a constant;

[0108] S24, find the inverse function according to the formula in step S23 to obtain formula (3):

[0109]

[0110] The adaptation time t can be calculated from ΔL in formula (3):

[0111] S25, calculating the brightness difference L1-L2 between two adjacent scenes. If the absolute value of the difference is greater than a set value, a smooth transition of the scenes is required; otherwise, a smooth transition is not required.

[0112] Embodiment 3: Based on Embodiment 1, in step S3, calculating the average maximum brightness L1 of the high brightness scene C1 in each frame includes the following sub-steps: Designing the maximum brightness moving iteration formula of the C1 scene:

[0113] L1 n =L1 n-1 -ΔL(t1+n / (60·f)) (4)

[0114] in, The initial moving brightness is L10 = L1, and the corresponding time in the transition interval is given by formula (3), t(L10) = t1; if If it is not an integer, it will be rounded up or down. f is the frame rate.

[0115] Embodiment 4: Based on Embodiment 1, in step S3, calculating the average maximum brightness L2 of the low brightness scene C2 in each frame includes the following sub-steps: Designing the maximum brightness moving iteration formula of the C2 scene as follows:

[0116] L2 n =L2 n-1 +ΔL(t2+n / (60·f)) (5)

[0117] in, The initial moving brightness is L20=L2, and t2 is the corresponding time in the transition interval given by formula (3), that is, t(L20).

[0118] Embodiment 5: Based on Embodiment 1, in step S4, calculating the value of the highlight ratio R1 of the high brightness scene C1 in each frame includes sub-steps: designing an iterative formula for the highlight ratio of the C1 scene:

[0119]

[0120] in The initial moving highlight ratio is R10=R1, the original highlight ratio of scene C1 is R1, and the original highlight ratio of scene C2 is R2. The difference between the two is taken, and ΔR=R1–R2 is recorded.

[0121] Embodiment 6: Based on Embodiment 1, in step S4, calculating the value of the highlight ratio R2 of the low brightness scene C2 in each frame includes sub-steps: designing an iterative formula for the highlight ratio of the C2 scene:

[0122]

[0123] middle The starting moving highlight ratio is R20=R2.

[0124] Embodiment 7: Based on Embodiment 1, in step S5, the calculation of the corresponding transformation curve includes the sub-steps of: designing the following formula:

[0125]

[0126] Among them, c1, c2, c3, c4, c5, c6, and c7 are all constants; m0, m1, and m2 are determined by R' and L; R' is the highlight ratio of each frame calculated by the iterative formula in step S4, L is the average maximum brightness of each frame calculated by the iterative formula in step S3, and E is the independent variable, that is, the brightness coordinate axis in the OETF curve.

[0127] Embodiment 8: Based on Embodiment 1, m0, m1, and m2 are calculated using the following formula:

[0128]

[0129] Among them, d1, d2, d3, d4, and d5 are all constants.

[0130] Embodiment 9: A computer device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, the method described in any one of embodiments 1 to 8 is executed.

[0131] Embodiment 10: A computer-readable storage medium stores a computer program, and the computer program is loaded by a processor to execute the method described in any one of Embodiments 1 to 8.

[0132] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0133] According to one aspect of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0134] As another aspect, the present invention further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0135] The parts not involved in the present invention are the same as the prior art or can be implemented by using the prior art.

[0136] In addition to the above examples, those skilled in the art may obtain other embodiments based on the above disclosure or by using the knowledge or technology in the relevant field to make changes. The features of each embodiment may be interchangeable or replaced. The changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention and should be within the scope of protection of the claims attached to the present invention.

Claims

1. A dynamic HDR down-conversion method based on human eye adaptation curve, characterized in that: Includes steps: S1, perform scene segmentation based on metadata, divide the video into continuous segments, and record the average maximum brightness L and highlight ratio R of each scene; S2, determine the scene that needs smooth transition and the required smooth transition time according to the human eye adaptation curve; S3, calculating the average maximum brightness L1 and L2 of the high brightness scene C1 and the low brightness scene C2 in each frame according to the smooth transition time calculated in step S2 and the fitted human eye adaptation curve; S4, calculating the values ​​of the highlight proportions R1 and R2 of the high-brightness scene C1 and the low-brightness scene C2 in each frame according to the smooth transition time calculated in step S2; S5, calculating corresponding transformation curves according to L1 and R1 of scene C1 in each frame and L2 and R2 of scene C2 in each frame during the transition period.

2. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 1, characterized in that: In step S2, the sub-steps are included: S21, according to the brightness value on the human eye adaptation curve, the following formula is designed: （1） Wherein, y represents the ordinate of the selected brightness starting point on the human eye adaptation curve after logarithmic processing, a1 is a constant, and a2 is a constant; S22, selecting a sample of the fitting curve, and calculating the un-logarithmized ordinate brightness value inversely according to the formula (1) designed in step S21; S23, using the real brightness value obtained in step S22, and selecting the adaptation curve with the set value nit brightness as the initial brightness to fit the following formula (2): （2） The function of the average maximum brightness difference between scenes with respect to the adaptation time is obtained; wherein t represents the time, b1 is a constant, b2 is a constant, and b3 is a constant; S24, find the inverse function according to the formula in step S23 to obtain formula (3): （3） The adaptation time t is calculated from ΔL in formula (3); S25, calculating the average maximum brightness difference L1-L2 between two adjacent scenes. If the absolute value of the difference is greater than a set value, a smooth transition of the scenes is required; otherwise, a smooth transition is not required.

3. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 2, characterized in that: In step S3, calculating the average maximum brightness L1 of the high brightness scene C1 in each frame includes sub-steps: designing the maximum brightness moving iteration formula of the C1 scene: （4） Where n = 1, 2, 3, 4, …, , the initial moving brightness is L10 = L1, and the smooth transition time is given by formula (3), t1; if If it is not an integer, it will be rounded up or down. f is the frame rate.

4. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 3, characterized in that: In step S3, calculating the average maximum brightness L2 of the low brightness scene C2 in each frame includes sub-steps: designing the maximum brightness moving iteration formula of the C2 scene as: （5） where n = 1, 2, 3, …, , the initial moving brightness is L20 = L2, t2 is the smooth transition time given by formula (3), that is, .

5. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 3, characterized in that: In step S4, calculating the value of the highlight ratio R1 of the high brightness scene C1 in each frame includes sub-steps: designing an iterative formula for the highlight ratio of the C1 scene: （6） where n = 1, 2, 3, …, , the starting moving highlight ratio is R10 = R1, the original highlight ratio of scene C1 is R1, and the original highlight ratio of scene C2 is R2. Take the difference between the two and record ΔR = R1 – R2.

6. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 5, characterized in that: In step S4, calculating the value of the highlight ratio R2 of the low brightness scene C2 in each frame includes sub-steps: designing an iterative formula for the highlight ratio of the C2 scene: （7） where n = 1, 2, 3, …, , the starting moving highlight ratio is R20 = R2.

7. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 1, characterized in that: In step S5, the calculation of the corresponding transformation curve includes the sub-steps of: designing the following formula: Among them, c1, c2, c3, c4, c5, c6, and c7 are all constants; , , Determined by R' and L; R' is the highlight ratio of each frame calculated by the iterative formula in step S4, L is the average maximum brightness of each frame calculated by the iterative formula in step S3, E is the independent variable, that is, the brightness coordinate axis in the OETF curve, The dependent variable is the digitally encoded pixel value.

8. The dynamic HDR down-conversion method based on human eye adaptation curve according to claim 7, characterized in that: Calculate using the following formula , , : Among them, d1, d2, d3, d4, and d5 are all constants.

9. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, the method according to any one of claims 1 to 8 is executed.

10. A computer-readable storage medium, characterized in that: A computer program is stored in a readable storage medium, and the computer program is loaded by a processor to execute the method according to any one of claims 1 to 8.

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