A data processing method, apparatus, processor, computing device, and storage medium

Abstract

This specification provides a data processing method in its embodiments. This method represents the undetermined Lagrange polynomial corresponding to the interpolation observation point as the ratio of the sum of multiple second operators to the first operator. Each of the first and second operators may include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial. Thus, during the Lagrange interpolation fitting process, the calculation results of the minimum operators included in the undetermined Lagrange polynomial can be obtained first. Then, based on the calculation results of the undetermined Lagrange polynomial and the minimum operators, the target Lagrange polynomial is determined. Finally, the undetermined observation point is solved using the target Lagrange polynomial. This process, by solving for the calculation results of the minimum operators and determining the target Lagrange polynomial based on those results, reduces redundant calculations in determining the target Lagrange polynomial, thereby improving the solution speed.

Description

Technical Field

[0001] This specification relates to the field of computer application technology, specifically to data processing technology within the field of computer application technology, and more specifically, to a data processing method, apparatus, processor, computing device, and storage medium. Background Technology

[0002] In scenarios such as image processing, numerical analysis methods may be used to obtain the values ​​of missing or uncorrected data points in order to predict unknown data points. For example, in image processing, due to limitations of the shooting environment or data loss during processing, some pixels in an image may be blurry or missing, resulting in the loss of some image information. To predict or correct these missing or blurry pixels, Lagrange interpolation can be used to fit these pixels, either to complete the image or to correct it to make it clearer.

[0003] However, in current image processing and other data processing methods, the calculation speed of the data point fitting process is relatively slow, resulting in a long fitting time. Summary of the Invention

[0004] This specification provides a data processing method, apparatus, processor, computing device, and storage medium to reduce the time required for fitting the observation points.

[0005] To achieve the above technical objectives, the embodiments of this specification provide the following technical solutions:

[0006] Firstly, one embodiment of this specification provides a data processing method, including:

[0007] In response to a data processing instruction carrying the observation points to be observed and the interpolation observation points, a Lagrange interpolation fitting process is performed; the interpolation observation points include key-value pairs of pixel positions and pixel parameters;

[0008] The Lagrange interpolation fitting process includes:

[0009] Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial;

[0010] Based on the undetermined Lagrange polynomial and the calculation results of the minimum operator, the target Lagrange polynomial is determined, and the undetermined observation point is solved using the target Lagrange polynomial to obtain the pixel position and pixel parameters of the undetermined observation point.

[0011] The undetermined Lagrange polynomial includes the ratio of the sum of multiple second operators to the first operator; both the first operator and the second operator include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes subtraction operations.

[0012] Secondly, one embodiment of this specification provides a data processing apparatus, comprising:

[0013] The instruction response module is used to respond to data processing instructions carrying observation points to be observed and interpolation observation points, and to perform the Lagrange interpolation fitting process; the interpolation observation points include key-value pairs of pixel positions and pixel parameters;

[0014] The Lagrange interpolation fitting process includes:

[0015] Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial;

[0016] Based on the undetermined Lagrange polynomial and the calculation results of the minimum operator, the target Lagrange polynomial is determined, and the undetermined observation point is solved using the target Lagrange polynomial to obtain the pixel position and pixel parameters of the undetermined observation point.

[0017] The undetermined Lagrange polynomial includes the ratio of the sum of multiple second operators to the first operator; both the first operator and the second operator include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes subtraction operations.

[0018] Thirdly, one embodiment of this specification provides a processor, including:

[0019] A decoder is used to decode data processing instructions into decoded instructions;

[0020] An execution unit is configured to execute the decoded instructions to implement the data processing method described in any of the preceding embodiments.

[0021] Fourthly, one embodiment of this specification also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data processing method described above.

[0022] Fifthly, one embodiment of this specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data processing method described above.

[0023] Sixthly, embodiments of this specification provide a computer program product or computer program, the computer program product including a computer program stored in a computer-readable storage medium; the processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program to implement the steps of the above-described data processing method.

[0024] As can be seen from the above technical solution, the data processing method provided in this specification represents the undetermined Lagrange polynomial corresponding to the interpolation observation point as the ratio of the sum of the plurality of second operators to the first operator. The first operator and the second operators can each include the product of the plurality of minimum operators corresponding to the undetermined Lagrange polynomial. Thus, in the Lagrange interpolation fitting process, the calculation results of the minimum operators included in the undetermined Lagrange polynomial can be obtained first. Then, based on the calculation results of the undetermined Lagrange polynomial and the minimum operators, the target Lagrange polynomial is determined. Finally, the undetermined observation point is solved using the target Lagrange polynomial. In this process, by solving for the calculation results of the minimum operators and determining the target Lagrange polynomial based on those results, redundant calculations in determining the target Lagrange polynomial are reduced. This helps reduce the number of instructions required to determine the target Lagrange polynomial, improving the calculation speed of the Lagrange interpolation fitting process, reducing the required time, and lowering the computational resource requirements of the process. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this specification. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a data processing method provided for one embodiment of this specification.

[0027] Figure 2 This is a flowchart illustrating another data processing method provided as one embodiment of this specification.

[0028] Figure 3 This is a schematic flowchart of a data processing apparatus provided for one embodiment of this specification.

[0029] Figure 4 This is a schematic diagram of a processor provided for one embodiment of this specification.

[0030] Figure 5 This is a schematic diagram of the structure of a computing device provided for one embodiment of this specification. Detailed Implementation

[0031] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0032] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0033] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0034] Overview

[0035] In related technologies, scenarios such as image processing or physical quantity observation may require numerical analysis methods to obtain the values ​​of missing or corrected data points in order to predict unknown data points. Many practical problems use functions to represent certain intrinsic relationships or patterns, and many functions can only be understood through experiments and observations. These functions are often complex nonlinear functions, difficult to represent with simple analytical expressions. Lagrange interpolation can effectively solve this problem. Lagrange interpolation is a polynomial function-based interpolation method used to fit a set of discrete data points. The core idea of ​​this method is to construct a polynomial function based on given data points, such that its values ​​at these points are equal to the given values. Then, the constructed polynomial function is used to solve for the data points to be predicted. Although Lagrange interpolation is simple and easy to use, when there are many data points, the computational load is very large, leading to long computation time and a decrease in performance.

[0036] To address this issue, the inventors discovered that the undetermined Lagrange polynomial corresponding to the interpolation observation point can be represented as the ratio of the sum of the plurality of second operators to the first operator. Both the first and second operators can include the product of the plurality of minimum operators corresponding to the undetermined Lagrange polynomial. Thus, during the Lagrange interpolation fitting process, the calculation results of the minimum operators included in the undetermined Lagrange polynomial can be obtained first. Then, based on the calculation results of the undetermined Lagrange polynomial and the minimum operators, the target Lagrange polynomial is determined. Finally, the undetermined observation point is solved using the target Lagrange polynomial. This process, by solving for the minimum operators and determining the target Lagrange polynomial based on those results, reduces redundant calculations in determining the target Lagrange polynomial, thus reducing the number of instructions required. This improves the calculation speed of the Lagrange interpolation fitting process, reduces the time required, and lowers the computational resource requirements of the process.

[0037] Based on the above concept, this specification provides a data processing method. The data processing method provided by this specification will be described exemplarily below with reference to the accompanying drawings.

[0038] Exemplary methods

[0039] To be applied Figure 1 Taking the processor 11 of the computing device 10 as an example, some embodiments of this specification exemplify the data processing method, which includes:

[0040] S101: In response to a data processing instruction carrying the observation points to be determined and the interpolation observation points, execute the Lagrange interpolation fitting process; the interpolation observation points include key-value pairs of observation point locations and observation point values.

[0041] The Lagrange interpolation fitting process includes:

[0042] Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial;

[0043] Based on the calculation results of the undetermined Lagrange polynomial and the minimum operator, the target Lagrange polynomial is determined. Using the target Lagrange polynomial, the observation point to be determined is solved to obtain the pixel position and pixel parameters of the observation point to be determined.

[0044] The observation point to be determined can be a data point with unknown values. This data point can be represented as (x, y) or (x, f(x)). The data point can be predicted by solving a defined target Lagrange polynomial. x can refer to the location of the observation point, and y or f(x) can represent the value of the observation point corresponding to the location. In some cases, the location of the observation point in the observation point to be determined is known, and the value of the observation point in the observation point to be determined can be solved by solving a defined target Lagrange polynomial.

[0045] Interpolation observation points can refer to data points with known values. Interpolation data points are typically used to determine the target Lagrange polynomial using the Lagrange interpolation method. Interpolation data points can be represented as (x... i y i ), i represents the i-th interpolated pixel, and x of the interpolated pixel. i and y i These represent the location of the observation point and the value of the observation point, respectively. Both of these values ​​are known.

[0046] Taking an image processing scenario as an example, the observation point to be determined may include a pixel to be determined. This pixel may be a missing pixel or a blurred pixel in an image. The interpolation observation point may include multiple interpolation pixels. The interpolation pixels may be known pixels in an image. The pixel position and pixel parameters of the interpolation pixels are known values. The pixel parameters include, but are not limited to, brightness, color gamut, grayscale, etc., which are not limited in this specification and depend on the actual situation. Using the data processing method provided in the embodiments of this specification, the prediction of the pixel to be determined can be realized, thereby achieving at least one of the functions of image completion, stitching, scaling, and smoothing.

[0047] The undetermined Lagrange polynomial can refer to a general expression for a Lagrange polynomial corresponding to its order. For example, the undetermined Lagrange polynomial may include first-order, second-order, and third-order Lagrange polynomials, etc., but this specification does not limit this. The minimum operator can refer to the common operator among the various operators in the undetermined Lagrange polynomial. By first solving for the minimum operator and then determining the target Lagrange polynomial based on the calculation result of the minimum operator, the repeated calculation of the minimum operator can be reduced, thus improving the execution efficiency of the Lagrange interpolation fitting process. Specifically, in this embodiment, during the Lagrange interpolation fitting process, the calculation results of the minimum operator included in the undetermined Lagrange polynomial are first obtained based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point. Then, the target Lagrange polynomial is determined based on the calculation results of the undetermined Lagrange polynomial and the minimum operator. In this process, the minimum operator is used as a basic calculation unit, which can avoid repeated calculations in the process of determining the target Lagrange polynomial and improve the execution efficiency of the Lagrange interpolation fitting process.

[0048] In one optional implementation, the undetermined Lagrange polynomial includes the ratio of the sum of a plurality of second operators to the first operator; both the first operator and the second operators include the product of a plurality of the minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operators include subtraction operations.

[0049] Thus, this data processing method represents the undetermined Lagrange polynomial corresponding to the interpolation observation point as the ratio of the sum of the plurality of second operators to the first operator, wherein both the first operator and the second operators may include the product of the plurality of minimum operators corresponding to the undetermined Lagrange polynomial. In this way, during the Lagrange interpolation fitting process, the calculation results of the minimum operators included in the undetermined Lagrange polynomial can be obtained first. Then, based on the calculation results of the undetermined Lagrange polynomial and the minimum operators, the target Lagrange polynomial is determined. Finally, the undetermined observation point is solved using the target Lagrange polynomial. This process, by solving for the calculation results of the minimum operators and determining the target Lagrange polynomial based on those results, reduces redundant calculations in determining the target Lagrange polynomial, which helps reduce the number of instructions required to determine the target Lagrange polynomial. This improves the calculation speed of the Lagrange interpolation fitting process, reduces the required time, and lowers the computational resource requirements of the process.

[0050] The specific formulas and methods for determining undetermined Lagrange polynomials of various orders are illustrated below. In one embodiment, the undetermined Lagrange polynomials include first-order polynomials, second-order polynomials, and third-order polynomials;

[0051] The first-order polynomial includes: ;in, Represents a first-order polynomial. ; ; ; ; , and The second operator represents the first-order polynomial. Denotes the first operator of the first-order polynomial. , and Describes the minimal operator corresponding to the first-order polynomial; )and( () represents the key-value pair between the pixel position and pixel parameter of the interpolation observation point;

[0052] The second-order polynomial includes: ;in, Represents a second-order polynomial. , , , ; , , , , , , and This represents the second operator of the second-order polynomial. Denotes the first operator of the second-order polynomial. , , , , and Denotes the minimal operator corresponding to the second-order polynomial; ), ( )and( () represents the key-value pair between the pixel position and pixel parameter of the interpolation observation point;

[0053] The third-order polynomial includes: ;in, Represents a third-order polynomial. , , , , ; , , , , , , , , , , , and This represents the second operator of the third-order polynomial. Denotes the first operator of the third-order polynomial. , , , , , , , , and Denotes the minimal operator corresponding to the second-order polynomial; ), ( ), ( )and( ) represents the key-value pair between the pixel position and pixel parameter of the interpolation observation point.

[0054] In one implementation, the process of determining the undetermined Lagrange polynomial includes:

[0055] The original Lagrange polynomial is expressed as the ratio of the sum of multiple second operators to the first operator;

[0056] Extract the smallest operator from the first operator and the second operator, so that the first operator and the second operator are represented as a product of the plurality of said smallest operators.

[0057] In this way, the expression of the undetermined Lagrange polynomial can be simplified, allowing it to include the product of multiple repeated minimal operators. This reduces redundant calculations and improves computational efficiency when calculating the results of the minimal operators.

[0058] The process of determining the aforementioned undetermined Lagrange polynomial is explained in detail below:

[0059] According to the definition of Lagrange interpolation: in the plane, there exist (x0, y0), (x1, y1), ..., (x...). n ,y n Given n+1 points, construct a function f(x) whose graph passes through these n+1 points. Let set Dn be the set of indices of the point (x,y), Dn={0,1,2,……,n}. Construct n+1 polynomials p j (x), j∈Dn. For any k∈Dn, we have p k (x), B k ={i | i≠k,i∈Dn}, such that:

[0060]

[0061] in, p represents cumulative multiplication. k (x) is an nth-degree polynomial, and satisfies m∈B k , p k (xm)=0 and p k (x k =1; Finally, the formula for constructing the Lagrange interpolation polynomial can be obtained:

[0062]

[0063] The first-order Lagrange interpolation function has two interpolation observation points, corresponding to n=1, and the corresponding polynomial is:

[0064]

[0065] The second-order Lagrange interpolation function has three interpolation observation points, corresponding to n=2, and the corresponding polynomial is:

[0066]

[0067] The third-order Lagrange interpolation function has four interpolation observation points, corresponding to n=3, and the corresponding polynomial is:

[0068]

[0069] For processors, division instructions require significantly more clock cycles than multiplication. Excessive division instructions can lead to performance degradation because they involve multiple steps, including estimating the quotient, adjusting a portion of the quotient, and calculating the remainder. These steps are often interdependent, making multiplication even more complex. Furthermore, processors typically provide dedicated hardware multipliers for efficient multiplication, while division demands substantial hardware resources and may lack dedicated units, requiring a series of instructions and general-purpose arithmetic logic units to perform the operation, resulting in relatively slower division. Therefore, polynomial rearrangement can be used to minimize division operations in Lagrange polynomials of various orders.

[0070] In this embodiment, the polynomial can be rearranged into the following operational form, so that the number of divisions in each degree of the Lagrange polynomial is reduced to 1:

[0071]

[0072] in

[0073] From Equation 4-1, we can obtain the final calculation factors for the first, second, and third order Lagrange interpolation polynomials:

[0074]

[0075]

[0076]

[0077] in,

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Using the above method, the first and second operators of each order of undetermined Lagrange polynomial are obtained. The minimum operator among the first and second operators can be extracted as follows: For a first-order polynomial, its minimum operator includes: , , In a first-order polynomial, the factors that can be computed may include: , , This expression reduces the number of subtraction operations in a first-order polynomial from four in Equation 1-1 to three, thereby reducing the number of subtraction operations and the number of subtraction instructions required.

[0089] For a second-order polynomial, its minimal operators include: ， ， ， ， ， In a second-order polynomial, the factors that can be computed may include: , , , This expression reduces the number of subtraction operations in a second-order polynomial from 12 in Equation 2-1 to 6, thereby reducing the number of subtraction operations and the number of subtraction instructions required.

[0090] For a third-order polynomial, its minimal operators include: ， ， ， ， ， ， In a third-order polynomial, the factors that can be computed may include: ， ， ， ， This expression reduces the number of subtraction operations in a third-order polynomial from 24 in Equation 3-1 to 10, thereby reducing the number of subtraction operations and the number of subtraction instructions required.

[0091] In one implementation, the undetermined Lagrange polynomial corresponds to multiple minimal operators, and each of these minimal operators is distinct. This minimizes redundant computation of the minimal operators, thereby improving method execution efficiency.

[0092] To improve the computational efficiency of the minimum operator, in one embodiment of this specification, reference is made to... Figure 2 The number of interpolation observation points is multiple. The calculation result of obtaining the minimum operator included in the undetermined Lagrange polynomial based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point includes:

[0093] S300: Based on the number of interpolation observation points, determine the undetermined Lagrange polynomial corresponding to the number of interpolation observation points, wherein the order of the undetermined Lagrange polynomial is equal to the number of interpolation observation points minus one;

[0094] S301: Using the Single Instruction Stream Multiple Data Stream (SIMD) instruction, load the observation point to be determined and the interpolation observation point;

[0095] S302: Using the SIMD instruction, calculate the calculation result of the minimum operator included in the undetermined Lagrange polynomial based on the observation point to be determined and the interpolation observation point.

[0096] In step S300, the number of interpolation observation points can be determined based on the start and end indices of the interpolation observation points, thereby determining the undetermined Lagrange polynomial corresponding to the number of interpolation observation points. For example, assuming the input is a batch of data to be processed, generally only a few consecutive points (i.e., interpolation observation points) need to be constructed into a polynomial. The data range for constructing the polynomial is determined by the interpolation observation point index range (m, n). In this case, the interpolation observation points for constructing the polynomial are determined to be (Xm, Ym) ~ (Xn, Yn) by mn. According to the definition of an interpolation polynomial, K interpolation nodes correspond to a K-1 order polynomial. In this case, the number of interpolation observation points determined by mn is n-m+1, so it corresponds to an nm order interpolation polynomial.

[0097] In step S301, the observation points to be determined and the interpolation observation points are loaded from memory into vector registers 1 to n in a vectorized manner.

[0098] In step S302, parallel computation of multiple data points is achieved through vector operations to obtain the minimum operator stored in vector registers 1~n (these vector registers 1~n can be different from or the same as the vector registers used to load the observation points to be determined and the interpolation observation points in step S301; this specification does not impose any limitations on this). Figure 2 (represented by operators in Chinese).

[0099] In this embodiment, based on SIMD (Single Instruction Multiple Data) instructions and vector registers, the loading of the observation points to be determined and the interpolation observation points and the calculation of the minimum operator are performed. This enables parallel calculation of multiple data points using a single instruction, which is beneficial for improving computational efficiency and reducing the number of instructions required.

[0100] Still referencing Figure 2In one optional embodiment, the undetermined Lagrange polynomial includes the ratio of the sum of a plurality of second operators to the first operator; both the first operator and the second operators include the product of a plurality of the minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes a subtraction operation;

[0101] The step of determining the target Lagrange polynomial based on the calculation results of the undetermined Lagrange polynomial and the minimum operator includes:

[0102] S303: Using SIMD instructions, based on the expressions of the first operator and the second operator included in the undetermined Lagrange polynomial, the calculation results of the minimum operator are combined into the first operator and the second operator to obtain the target Lagrange polynomial.

[0103] In this embodiment, by using vectorized computation of factors, the process of combining the minimum operators into the first and second operators and obtaining the final target Lagrange polynomial is parallelized, which helps to improve the execution efficiency of the method. Figure 3 In this process, the final output value can refer to the calculation result of substituting the observation point into the target Lagrange polynomial.

[0104] In some implementations, in addition to using SIMD instructions and vector registers to calculate the results of the minimum operators included in the undetermined Lagrange polynomial in parallel through steps S301 to S303, and determining the target Lagrange polynomial based on the undetermined Lagrange polynomial and the results of the minimum operators, it is also possible to use a scalar processor to load individual data elements (e.g., observation points to be determined or interpolated observation points) separately using multiple scalar registers to sequentially calculate the minimum operators and determine the target Lagrange polynomial. This specification does not limit this, and the specific implementation depends on the actual situation.

[0105] Exemplary device

[0106] In one exemplary embodiment of this specification, a data processing apparatus is also provided, characterized in that it is applied to a computing device, such as... Figure 3 As shown, the data processing device includes:

[0107] The instruction response module 401 is used to respond to a data processing instruction carrying the observation points to be observed and the interpolation observation points, and to perform a Lagrange interpolation fitting process; the interpolation observation points include key-value pairs of pixel positions and pixel parameters;

[0108] The Lagrange interpolation fitting process includes:

[0109] Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial;

[0110] Based on the calculation results of the undetermined Lagrange polynomial and the minimum operator, the target Lagrange polynomial is determined. Using the target Lagrange polynomial, the observation point to be determined is solved to obtain the pixel position and pixel parameters of the observation point to be determined.

[0111] In one embodiment, the undetermined Lagrange polynomial includes the ratio of the sum of a plurality of second operators to a first operator; both the first operator and the second operators include the product of a plurality of the minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operators include subtraction operations.

[0112] In one implementation, the number of interpolation observation points is multiple. The instruction response module obtains the calculation result of the minimum operator included in the undetermined Lagrange polynomial based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point. This is specifically used for:

[0113] Based on the number of interpolation observation points, determine the undetermined Lagrange polynomial corresponding to the number of interpolation observation points, wherein the order of the undetermined Lagrange polynomial is equal to the number of interpolation observation points minus one;

[0114] The observation point to be determined and the interpolation observation point are loaded using the Single Instruction Stream Multiple Data Stream (SIMD) instruction.

[0115] Using the SIMD instructions, the calculation results of the minimum operators included in the undetermined Lagrange polynomial are calculated based on the observation point to be determined and the interpolation observation point.

[0116] In one embodiment, the undetermined Lagrange polynomial includes the ratio of the sum of a plurality of second operators to a first operator; both the first operator and the second operators include the product of a plurality of the smallest operators corresponding to the undetermined Lagrange polynomial, and the smallest operator includes a subtraction operation;

[0117] The instruction response module determines the target Lagrange polynomial based on the undetermined Lagrange polynomial and the calculation result of the minimum operator, specifically for:

[0118] Using SIMD instructions, based on the expressions of the first and second operators included in the undetermined Lagrange polynomial, the calculation results of the minimum operator are combined into the first and second operators to obtain the target Lagrange polynomial.

[0119] In one implementation, the undetermined Lagrange polynomial corresponds to a plurality of minimal operators, and the plurality of minimal operators corresponding to the undetermined Lagrange polynomial are all different.

[0120] In one implementation, the process of determining the undetermined Lagrange polynomial includes:

[0121] The original Lagrange polynomial is expressed as the ratio of the sum of multiple second operators to the first operator;

[0122] Extract the smallest operator from the first operator and the second operator, so that the first operator and the second operator are represented as a product of the plurality of said smallest operators.

[0123] For specific limitations regarding the data processing device, please refer to the limitations regarding the data processing method above, which will not be repeated here. Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0124] Exemplary processors and computing devices

[0125] One embodiment of this specification provides a processor, such as Figure 4 As shown, the processor 1001 includes:

[0126] Decoder 1002 is used to decode computation instructions into decoded instructions;

[0127] The execution unit 1003 is used to execute the decoded instructions to implement the data processing method described in any of the above embodiments or the data processing-based method described in any of the above embodiments.

[0128] In addition to the above structure, the processor 1001 may also include multiple registers 1004 to cooperate with the execution unit 1003 in performing tasks. Both the registers 1004 and the decoder 1002 are connected to the execution unit 1003. Another embodiment of this application also proposes a computing device, see [link to relevant documentation]. Figure 5 As shown, an exemplary embodiment of this specification also provides a computing device, including: a memory and a processor, the memory storing a computer program, the processor executing the computer program and performing the steps in the classification method according to various embodiments of this specification described above.

[0129] The internal structure of the computing device can be as follows: Figure 5As shown, the computing device includes a processor, memory, network interface, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps of the classification methods according to various embodiments of this specification as described in the above embodiments.

[0130] The processor may include the main processor, as well as baseband chips, modems, etc.

[0131] The memory stores a program that executes the technical solution of this invention, and may also store an operating system and other critical business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0132] The processor can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0133] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0134] Output devices may include devices that allow information to be output to a user, such as displays, printers, speakers, etc.

[0135] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0136] The processor executes programs stored in memory and calls other devices, which can be used to implement the various steps of any of the classification methods provided in the above embodiments of this application.

[0137] The computing device may also include a display component and a voice component. The display component may be a liquid crystal display screen or an e-ink display screen. The input device of the computing device may be a touch layer covering the display component, or a button, trackball or touchpad set on the casing of the computing device, or an external keyboard, touchpad or mouse, etc.

[0138] Those skilled in the art will understand that Figure 5 The structures shown are merely block diagrams of some structures related to the solutions in this specification and do not constitute a limitation on the computing devices on which the solutions in this specification are applied. Specific computing devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0139] Exemplary computer program products and storage media

[0140] In addition to the methods and devices described above, the classification methods provided in the embodiments of this specification can also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the classification methods according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0141] The computer program product described herein can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0142] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the classification methods according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0143] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The embodiments described above are merely illustrative of several implementation methods outlined in this specification. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this specification. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this specification, and these all fall within the scope of protection of this specification. Therefore, the scope of protection for this patent should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, include: In response to a data processing instruction carrying the observation points to be observed and the interpolation observation points, a Lagrange interpolation fitting process is performed; the interpolation observation points include key-value pairs of pixel positions and pixel parameters; The Lagrange interpolation fitting process includes: Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial; Based on the undetermined Lagrange polynomial and the calculation results of the minimum operator, the target Lagrange polynomial is determined, and the undetermined observation point is solved using the target Lagrange polynomial to obtain the pixel position and pixel parameters of the undetermined observation point. The undetermined Lagrange polynomial includes the ratio of the sum of multiple second operators to the first operator; both the first operator and the second operator include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes subtraction operations.

2. The method according to claim 1, characterized in that, The number of interpolation observation points is multiple, and the calculation result of obtaining the minimum operator included in the undetermined Lagrange polynomial based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point includes: Based on the number of interpolation observation points, determine the undetermined Lagrange polynomial corresponding to the number of interpolation observation points, wherein the order of the undetermined Lagrange polynomial is equal to the number of interpolation observation points minus one; The observation point to be determined and the interpolation observation point are loaded using the Single Instruction Stream Multiple Data Stream (SIMD) instruction. Using the SIMD instructions, the calculation results of the minimum operators included in the undetermined Lagrange polynomial are calculated based on the observation point to be determined and the interpolation observation point.

3. The method according to claim 2, characterized in that, The undetermined Lagrange polynomial includes the ratio of the sum of multiple second operators to the first operator; both the first operator and the second operator include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes a subtraction operation; The step of determining the target Lagrange polynomial based on the calculation results of the undetermined Lagrange polynomial and the minimum operator includes: Using SIMD instructions, based on the expressions of the first and second operators included in the undetermined Lagrange polynomial, the calculation results of the minimum operator are combined into the first and second operators to obtain the target Lagrange polynomial.

4. The method according to claim 1, characterized in that, The undetermined Lagrange polynomial corresponds to multiple minimal operators, and the multiple minimal operators corresponding to the undetermined Lagrange polynomial are all different.

5. The method according to claim 1, characterized in that, The process of determining the undetermined Lagrange polynomial includes: The original Lagrange polynomial is expressed as the ratio of the sum of multiple second operators to the first operator; Extract the smallest operator from the first operator and the second operator, so that the first operator and the second operator are represented as a product of the plurality of said smallest operators.

6. A data processing apparatus, characterized in that, include: The instruction response module is used to respond to data processing instructions carrying observation points to be observed and interpolation observation points, and to perform the Lagrange interpolation fitting process; the interpolation observation points include key-value pairs of pixel positions and pixel parameters; The Lagrange interpolation fitting process includes: Based on the observation point to be determined, the interpolation observation point, and the undetermined Lagrange polynomial corresponding to the interpolation observation point, obtain the calculation results of the minimum operator included in the undetermined Lagrange polynomial; Based on the undetermined Lagrange polynomial and the calculation results of the minimum operator, the target Lagrange polynomial is determined, and the undetermined observation point is solved using the target Lagrange polynomial to obtain the pixel position and pixel parameters of the undetermined observation point. The undetermined Lagrange polynomial includes the ratio of the sum of multiple second operators to the first operator; both the first operator and the second operator include the product of multiple minimum operators corresponding to the undetermined Lagrange polynomial, and the minimum operator includes subtraction operations.

7. A processor, characterized in that, include: A decoder is used to decode data processing instructions into decoded instructions; An execution unit is configured to execute the decoded instructions to implement the data processing method according to any one of claims 1 to 5.

8. A computing device, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the data processing method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the data processing method according to any one of claims 1 to 5.

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