Self-adaptive discrete sampling method for curve data

Abstract

The invention provides a curve data adaptive discrete sampling method. The method comprises the following steps: acquiring an input bit width of a curve to be sampled; setting a curve input end point as an initial actual sampling binding point, and setting an initial discrete sampling step length; calculating a next estimated sampling binding point according to the current actual sampling binding point and the current discrete sampling step length; counting interpolation errors corresponding to the input data belonging to the current actual sampling binding point and the next estimated sampling binding point; continuously updating a current actual sampling binding point and a current discrete sampling step length to obtain a curve initial sampling lookup table and a to-be-adjusted interval set; updating the initial sampling lookup table based on the interval set to be adjusted; counting interpolation errors corresponding to the input data belonging to each interval in the adjusted interval set, and updating the interval set to be adjusted again; and outputting the current sampling lookup table as a curve final sampling lookup table until interpolation errors corresponding to all input data of the curve to be sampled do not exceed a preset error threshold value.

Description

Technical Field

[0001] The present invention belongs to the field of image display technology, and in particular relates to a curve data adaptive discrete sampling method. Background Art

[0002] In the field of image display technology, many algorithms, such as contrast enhancement and color gamut mapping, often require linear or nonlinear curve mapping of image data. To facilitate hardware implementation and reduce the computational complexity of curve mapping, image chips typically store this curve data in hardware memory in the form of lookup tables.

[0003] Discrete sampling of curve data is essentially a form of information compression. It essentially extracts a subset from the data set that makes up the curve, ensuring that its content closely reflects the original set within a specified accuracy range. Discrete sampling of curve data reduces the number of lookup table binding points, thereby reducing data storage and hardware storage costs. In actual image processing, table lookup interpolation can replace the original curve mapping calculation, saving hardware resources and improving interpolation accuracy.

[0004] The present invention proposes a curve data adaptive discrete sampling method, which can adaptively adjust the curve discrete sampling step size according to the actual distribution characteristics of curve data of different shapes, thereby ensuring interpolation accuracy, saving storage space, and being easy to implement in hardware. Summary of the Invention

[0005] The present invention provides a curve data adaptive discrete sampling method, comprising the following steps: Step 1: Obtain the input bit width of the curve to be sampled; Step 2: Set the curve input end point as the initial actual sampling binding point and set the initial discrete sampling step size; Step 3: Calculate the next estimated sampling binding point based on the current actual sampling binding point and the current discrete sampling step size; Step 4: Calculate the interpolation error of the input data within the range between the current actual sampling binding point and the next estimated sampling binding point; Step 5: Repeat steps 3 to 4 until the starting point of the curve input is sampled to obtain the curve initial sampling lookup table; Step 6: Based on the interval set to be adjusted Update the initial sampling lookup table; Step 7: Statistics belong to the adjusted interval set The interpolation error corresponding to the input data within each interval; Step 8. Repeat steps 6 to 7 until the interpolation error corresponding to all input data of the curve does not exceed the error threshold. , output the current sampling lookup table as the final sampling lookup table of the curve.

[0006] In step 1, it is assumed that the input bit width of the curve to be sampled is bit, then the input data value range of the curve is expressed as .

[0007] In step 2, the end point of the curve input to be sampled, that is, the maximum value of the curve input, is set as the initial actual sampling binding point input , and according to the curve mapping function Calculate the corresponding initial actual sampling binding point output : ; Set the initial discrete sampling step size ,make .

[0008] In step 3, it is assumed that the actual sampling binding point input is Indicates that the current actual sampling binding point output is: ; The current discrete sampling step size is , then the next estimated sampling binding point Should be: ; Estimated sampling binding point Corresponding sampling binding point output Should be: .

[0009] In step 4, the current actual sampling binding point Tie point to the next estimated sample The error checking interval can be expressed as , the corresponding sampling binding point output interval is expressed as , assuming that it belongs to The input data set of the curve to be sampled within the error checking interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : ; The interpolation error corresponding to each input data should be: ; in, ; like The interpolation error of some input data in the set is greater than the preset error threshold If the current error checking interval is Recorded in the interval set to be adjusted middle; Estimated sampling points As the new current actual sampling binding point, the new current discrete sampling step size is updated to .

[0010] In step 5, the initial sampling lookup table of the assumed curve contains Group binding points are as follows: .

[0011] In step 6, if the interval set to be adjusted An empty set means that the interpolation error corresponding to all input data of the curve does not exceed the error threshold , directly output the current initial sampling lookup table as the final sampling lookup table of the curve; If the interval set to be adjusted Not empty, assuming the collection Contains Group sampling binding point interval: ;According to the collection Each interval in the initial sampling lookup table updates the sampling binding points in turn: S6.1, order ; S6.2, Assumptions The sampling binding point interval represented by ;exist and Add a new sampling binding point between two sampling binding points : ; Added sampling binding point The corresponding sample output is: ; The sampling point interval and Recorded in the adjusted interval set ; Initial sampling lookup table Updates as follows: ; S6.3, order , go to step S6.2 and continue until , the loop ends and the set of intervals to be adjusted is cleared , output the current sampling lookup table .

[0012] In step 7, assume that the adjusted interval set Contains Group sampling binding point interval: , calculate the interpolation error of the input data belonging to each sampling binding point interval in turn: S7.1, order ; S7.2, Assumptions The sampling binding point interval represented by , the corresponding sampling binding point output interval is ; Assumed to belong to The input data set of the curve to be sampled in the interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : , The interpolation error of all input data to be tested within the current interval should be: , in, ; like There are some interpolation errors in the set that are greater than the preset error threshold In the case of Recorded in the interval set to be adjusted ; S7.3, order , go to step S7.2 and continue until , the loop ends and the adjusted interval set is cleared . BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 Shown is a data processing flow chart of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0015] See Figure 1 The present invention provides a curve data adaptive discrete sampling method, comprising the following steps: Step 1: Obtain the input bit width of the curve to be sampled; Step 2: Set the curve input end point as the initial actual sampling binding point and set the initial discrete sampling step size; Step 3: Calculate the next estimated sampling binding point based on the current actual sampling binding point and the current discrete sampling step size; Step 4: Calculate the interpolation error of the input data within the range between the current actual sampling binding point and the next estimated sampling binding point; Step 5: Repeat steps 3 to 4 until the starting point of the curve input is sampled to obtain the curve initial sampling lookup table; Step 6: Based on the interval set to be adjusted Update the initial sampling lookup table; Step 7: Statistics belong to the adjusted interval set The interpolation error corresponding to the input data within each interval; Step 8. Repeat steps 6 to 7 until the interpolation error corresponding to all input data of the curve does not exceed the error threshold. , output the current sampling lookup table as the final sampling lookup table of the curve.

[0016] In step 1, it is assumed that the input bit width of the curve to be sampled is bit, then the input data value range of the curve is expressed as .

[0017] In step 2, the end point of the curve input to be sampled, that is, the maximum value of the curve input, is set as the initial actual sampling binding point input , and according to the curve mapping function Calculate the corresponding initial actual sampling binding point output : ; Set the initial discrete sampling step size ,make .

[0018] In step 3, it is assumed that the actual sampling binding point input is Indicates that the current actual sampling binding point output is: ; The current discrete sampling step size is , then the next estimated sampling binding point Should be: ; Estimated sampling binding point Corresponding sampling binding point output Should be: .

[0019] In step 4, the current actual sampling binding point Tie point to the next estimated sample The error checking interval can be expressed as , the corresponding sampling binding point output interval is expressed as , assuming that it belongs to The input data set of the curve to be sampled within the error checking interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : ; The interpolation error corresponding to each input data should be: ; in, ; like The interpolation error of some input data in the set is greater than the preset error threshold If the current error checking interval is Recorded in the interval set to be adjusted middle; Estimated sampling points As the new current actual sampling binding point, the new current discrete sampling step size is updated to .

[0020] In step 5, the initial sampling lookup table of the assumed curve contains Group binding points are as follows: .

[0021] In step 6, if the interval set to be adjusted An empty set means that the interpolation error corresponding to all input data of the curve does not exceed the error threshold , directly output the current initial sampling lookup table as the final sampling lookup table of the curve; If the interval set to be adjusted Not empty, assuming the collection Contains Group sampling binding point interval: ;According to the collection Each interval in the initial sampling lookup table updates the sampling binding points in turn: S6.1, order ; S6.2, Assumptions The sampling binding point interval represented by ;exist and Add a new sampling binding point between two sampling binding points : ; Added sampling binding point The corresponding sample output is: ; The sampling point interval and Recorded in the adjusted interval set ; Initial sampling lookup table Updates as follows: ; S6.3, order , go to step S6.2 and continue until , the loop ends and the set of intervals to be adjusted is cleared , output the current sampling lookup table .

[0022] In step 7, assume that the adjusted interval set Contains Group sampling binding point interval: , calculate the interpolation error of the input data belonging to each sampling binding point interval in turn: S7.1, order ; S7.2, Assumptions The sampling binding point interval represented by , the corresponding sampling binding point output interval is ; Assumed to belong to The input data set of the curve to be sampled in the interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : , The interpolation error of all input data to be tested within the current interval should be: , in, ; like There are some interpolation errors in the set that are greater than the preset error threshold In the case of Recorded in the interval set to be adjusted ; S7.3, order , go to step S7.2 and continue until , the loop ends and the adjusted interval set is cleared .

[0023] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0024] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the appended claims and their equivalents.

Claims

1. A curve data adaptive discrete sampling method, characterized in that: The following steps are involved: Step 1: Obtain the input bit width of the curve to be sampled; Step 2: Set the curve input end point as the initial actual sampling binding point and set the initial discrete sampling step size; Step 3: Calculate the next estimated sampling binding point based on the current actual sampling binding point and the current discrete sampling step size; Step 4: Calculate the interpolation error of the input data within the range between the current actual sampling binding point and the next estimated sampling binding point; Step 5: Repeat steps 3 to 4 until the starting point of the curve input is sampled to obtain the curve initial sampling lookup table; Step 6: Based on the set of intervals to be adjusted Update the initial sampling lookup table; Step 7: Statistics belong to the adjusted interval set The interpolation error corresponding to the input data within each interval; Step 8. Repeat steps 6 to 7 until the interpolation error corresponding to all input data of the curve does not exceed the error threshold. , output the current sampling lookup table as the final sampling lookup table of the curve.

2. The method for adaptive discrete sampling of curve data according to claim 1, characterized in that: In step 1, it is assumed that the input bit width of the curve to be sampled is bit, then the input data value range of the curve is expressed as .

3. The method for adaptive discrete sampling of curve data according to claim 2, characterized in that: In step 2, the end point of the curve input to be sampled, that is, the maximum value of the curve input, is set as the initial actual sampling binding point input , and according to the curve mapping function Calculate the corresponding initial actual sampling binding point output : ； Set the initial discrete sampling step size ,make .

4. The method for adaptive discrete sampling of curve data according to claim 3, characterized in that: In step 3, it is assumed that the actual sampling binding point input is Indicates that the current actual sampling binding point output is: ； The current discrete sampling step size is , then the next estimated sampling binding point Should be: ； Estimated sampling binding point Corresponding sampling binding point output Should be: .

5. The method for adaptive discrete sampling of curve data according to claim 4, characterized in that: In step 4, the current actual sampling binding point Tie point to the next estimated sample The error checking interval can be expressed as , the corresponding sampling binding point output interval is expressed as , assuming that it belongs to The input data set of the curve to be sampled within the error checking interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : ； The interpolation error corresponding to each input data should be: ; in, ; like The interpolation error of some input data in the set is greater than the preset error threshold If the current error checking interval is Recorded in the interval set to be adjusted middle; Estimated sampling points As the new current actual sampling binding point, the new current discrete sampling step size is updated to .

6. The method for adaptive discrete sampling of curve data according to claim 5, characterized in that: In step 5, the initial sampling lookup table of the assumed curve contains Group binding points are as follows: 。 7. The method for adaptive discrete sampling of curve data according to claim 6, characterized in that: In step six, If the interval set to be adjusted An empty set means that the interpolation error corresponding to all input data of the curve does not exceed the error threshold , directly output the current initial sampling lookup table as the final sampling lookup table of the curve; If the interval set to be adjusted Not empty, assuming the collection Contains Group sampling binding point interval: ;According to the collection Each interval in the initial sampling lookup table updates the sampling binding points in turn: S6.1, order ; S6.2, Assumptions The sampling binding point interval represented by ;exist and Add a new sampling binding point between two sampling binding points : ; Added sampling binding point The corresponding sample output is: ; The sampling point interval and Recorded in the adjusted interval set ; Initial sampling lookup table Updates as follows: ； S6.3, order , go to step S6.2 and continue until , the loop ends and the set of intervals to be adjusted is cleared , output the current sampling lookup table .

8. The method for adaptive discrete sampling of curve data according to claim 7, characterized in that: In step seven, Assuming the adjusted interval set Contains Group sampling binding point interval: , calculate the interpolation error of the input data belonging to each sampling binding point interval in turn: S7.1, order ; S7.2, Assumptions The sampling binding point interval represented by , the corresponding sampling binding point output interval is ; Assumed to belong to The input data set of the curve to be sampled in the interval is expressed as , including Data: , calculate the corresponding mapping output value of each input data : ， The interpolation error of all input data to be tested within the current interval should be: ， in, ; like There are some interpolation errors in the set that are greater than the preset error threshold In the case of Recorded in the interval set to be adjusted ; S7.3, order , go to step S7.2 and continue until , the loop ends and the adjusted interval set is cleared .

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