A method for realizing dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction

Abstract

The present invention discloses a method for dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction, generates dynamic nonlinear processing mode coefficient values ​​through empirical algorithms, and mixes the calculated dynamic nonlinear processing coefficient values ​​with original R, G, B nonlinear curves in proportion to generate new nonlinear curves, transforms the image according to the new nonlinear curves and displays it, and processes frame image features in real time. Through the method of the present invention, the existing standard dynamic (SDR) program source can produce a high contrast dynamic (HDR) effect on the display terminal, thereby improving the image quality and viewing experience.

Description

Technical Field

[0001] The invention relates to the field of LCD and LED display, and more specifically to a method for realizing dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction. Background Art

[0002] In the traditional image processing scheme for controlling LED display, the image source is standard dynamic range (SDR), and the source image is displayed after nonlinear transformation before display. This nonlinear relationship is mostly fixed, regardless of the characteristics of the frame quality, and the conversion is performed in a fixed manner; there are two common picture characteristics that seriously affect the visual perception. The first is a high-contrast feature low-gray picture state, which means that the low grayscale span is large after display, and serious low-gray fault blocks appear; the second is a low-contrast feature high-gray picture state, which means that the high grayscale span is large after display, and serious fault blocks also appear. Fixed coefficient image conversion cannot adapt to a wide range of image characteristics.

[0003] Therefore, in order to obtain better display effects under standard dynamic (SDR) image source conditions, it is only possible to associate it with the picture characteristics and use FPGA algorithms and processing flows to improve display performance and achieve high dynamic (HDR) picture effects. Summary of the invention

[0004] The present invention provides a method for realizing dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction, which is characterized by comprising the following steps:

[0005] S1, compressing the image in equal proportion to obtain compressed image information;

[0006] S2, extracting white background brightness from the compressed image information;

[0007] S3, traversing the two-dimensional array of white background brightness, counting the distribution of brightness values, and generating a dynamic nonlinear processing coefficient value through an empirical algorithm based on the measured value range distribution relationship;

[0008] S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve;

[0009] S5. Transform the image according to the new nonlinear curve and then display it.

[0010] In a possible embodiment, the step S2 of extracting white background brightness from the compressed image information includes:

[0011] S21, read the RGB value of the point on the compressed image to obtain the RGB The value range is 0-255;

[0012] S22. Select UVW values ​​(UVW) of normalized points under different color systems to obtain UVW values ​​in a range of 0.00-1.00.

[0013] S23. Multiply the RGB value and the UVW value to get the transformation result fruit.

[0014] In a possible embodiment, S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve includes:

[0015] S41, receiving a mixing ratio (A) input by a user;

[0016] S42, calculate and generate a new nonlinear curve (Y ＊ );

[0017] Y ＊ =Y1×A+Y2×(1-A);

[0018] Among them, Y1 is the original value coefficient, and Y2 is the dynamic nonlinear processing mode coefficient value.

[0019] Through the method of the present invention, compared with the serious fault block situation that may be caused by the fixed conversion in the prior art, the present invention adopts a dynamic change method to transform the image so that the existing standard dynamic (SDR) program source can produce a high contrast dynamic (HDR) effect on the display terminal, thereby improving the image quality and viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0021] Figure 1 is a system block diagram of the present invention;

[0022] Figure 2 Background brightness distribution;

[0023] Figure 3 Image before compression;

[0024] Figure 4 Compressed image;

[0025] Figure 5 The effect of each pixel matrix transformation. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other steps or modules that are inherent to these processes, methods, products or devices.

[0028] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] The following describes it in detail with reference to specific embodiments.

[0030] See also Figure 1 , Figure 1 The embodiment of the present invention provides a method for dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction, characterized in that it includes the following steps:

[0031] S1, compressing the image in equal proportion to obtain compressed image information;

[0032] S2, extracting white background brightness from the compressed image information;

[0033] S3, traversing the two-dimensional array of white background brightness, counting the distribution of brightness values, and generating dynamic nonlinear processing values ​​through an empirical algorithm based on the measured value range distribution relationship;

[0034] S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve;

[0035] S5. Transform the image according to the new nonlinear curve and then display it.

[0036] In a possible implementation, S1, the specific step of compressing the image in equal proportion to obtain compressed image information includes:

[0037] The image simplifies the associated original image information and compresses the original frame proportionally. For example, if the original image is 1920(W)x1080(H), it will be 128(W)x72(H). The total amount of information is about 1 / 225 of the original. Through compression, the main features of the image are retained. Proportional compression can minimize the impact of interpolation. The simplification of the amount of information enables subsequent rapid processing.

[0038] In a possible implementation, S3, traversing the two-dimensional array of white background brightness, counting brightness value distribution, and generating dynamic nonlinear processing mode coefficient values ​​by an empirical algorithm based on the measured value range distribution relationship also includes:

[0039] Mathematically expressing the relationship distribution function expansion:

[0040] D={X1=x1, X2=x2,…,X n =x n}

[0041] Background brightness distribution Figure 2 As shown:

[0042] The brightness range is calculated in 16 segments from 0 to 255. The following formula only represents one segment 0-A, AB.

[0043]

[0044]

[0045]

[0046]

[0047] This method can be used to obtain information about how many pixels are scattered in each interval and generate a two-dimensional array. This information is compared through an algorithm and compared with the empirical value relationship table to obtain a dynamic linear adjustment coefficient.

[0048] The empirical value relationship table is a set of nonlinear adjustment coefficient tables generated by pre-processing various types of picture frames by computer according to the device-side processing method, obtaining the digital quantification relationship of brightness distribution and combining it with the physiological habits of human vision. The various types of picture frames refer to movies, DV digital videos, studio programs and other categories. Biological perception refers to the different visual habits of different races such as Asians, Europeans, and Africans. The process of combining the digital quantification of brightness distribution with the visual physiological perception and correction, associates the biological habits of human vision with the mathematical distribution relationship into a digital coefficient table.

[0049] The method of generating the empirical value relationship table is similar to the method of FPGA processing on the device side. The difference is that the number of video frames processed in this process is extremely small. It is to associate the picture features with their digital quantized distribution map, and to correct the physiological visual habits, which involves human participation.

[0050] During implementation, the program types and audience habits are preset in advance on the software at the front end of the computer, and various experience value relationship query tables are corresponding in advance, which are configured to the FPGA processing layer at the device end after the settings are confirmed. Similarly, it also supports the generation of associated video frames. The programs to be intervened by HDR are extracted, converted, and quantified by the computer in advance, and the physiological visual correction is determined and corrected by the executor in a dedicated software environment to determine and correct the picture and associated digital features.

[0051] For example, the image before compression is Figure 3 As shown, the compressed image is Figure 4 shown.

[0052] In a possible embodiment, the step S2 of extracting white background brightness from the compressed image information includes:

[0053] S21, read the RGB value of the point on the compressed image, so as to obtain The range of the RGB value is 0-255;

[0054] S22. Select normalized UVW values ​​under different color formats to obtain (UVW), wherein the UVW value ranges from 0.00 to 1.00.

[0055] S23. Multiply the RGB value and UVW value Get the transformation result.

[0056] Specifically, the white background brightness is extracted from the compressed information, and the transformation result is obtained using the coefficient matrix. R\G\B are frame pixels, ranging from 0-255, and U\V\W are brightness weight factors, ranging from 0.00-1.00, usually U: 0.30V: 0.59W: 0.11.

[0057] For example, after each pixel is transformed into a matrix, the effective brightness range is 0-255. Figure 5 .

[0058] In a possible embodiment, S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve includes:

[0059] S41, receiving a mixing ratio (A) input by a user;

[0060] S42, calculate and generate a new nonlinear curve (Y ＊ );

[0061] Y * =Y1×A+Y2×(1-A);

[0062] Among them, Y1 is the original value coefficient, and Y2 is the dynamic nonlinear processing mode coefficient value.

[0063] Specifically, in the display application, after a new nonlinear curve is generated, after the frame synchronization signal, before the image is transmitted, the corresponding nonlinear curve lookup table is updated; when the image starts to be transmitted, a pipeline transformation is performed. Each frame of the image displayed is related to the picture characteristics, which solves the problem of grayscale jumps and fault blocks in the picture.

[0064] Through the method of the present invention, compared with the serious fault block situation that may be caused by the fixed conversion in the prior art, the present invention adopts a dynamic change method to transform the image so that the existing standard dynamic (SDR) program source can produce a high contrast dynamic (HDR) effect on the display terminal, thereby improving the image quality and viewing experience.

[0065] It should be noted that, for the above-mentioned product embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0066] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] In the several embodiments provided by the present invention, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.

[0068] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0070] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the product of the present invention and its core idea. At the same time, for those skilled in the art, according to the product of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for realizing dynamic nonlinear adjustment of high contrast of video images based on FPGA algorithm prediction, characterized in that: The following steps are involved: S1, compressing the image in equal proportion to obtain compressed image information; S2, extracting the white background brightness of the compressed image information; S3, traversing the two-dimensional array of white background brightness, counting the distribution of brightness values, and generating a dynamic nonlinear processing coefficient value through an empirical algorithm based on the measured value range distribution relationship; S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve; S5, transforming the image according to the new nonlinear curve and then displaying it; The step S2, extracting white background brightness from the compressed image information, comprises: S21, read the RGB value of the point on the compressed image to obtain The RGB value ranges from 0 to 255; S22, selecting normalized UVW coefficient values ​​under different color systems to obtain (UVW), wherein the UVW value ranges from 0.00 to 1.00; S23. Multiply the RGB value and UVW value Get the white background brightness transformation result; The step S4, mixing the calculated dynamic nonlinear processing coefficient value with the original R, G, B nonlinear curves in proportion to generate a new nonlinear curve comprises: S41, receiving a mixing ratio A input by a user; S42. Calculate and generate a new nonlinear curve Y according to the following formula * ; AND * =Y1×A+Y2×(1-A); Among them, Y1 is the original value coefficient, and Y2 is the dynamic nonlinear processing mode coefficient value.

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