Real-time data compression method for focal plane array performance test

Abstract

The invention discloses a real-time data compression method for a focal plane array performance test, and the method comprises the following steps: S1, connecting a test instrument with a to-be-tested focal plane array, and carrying out the calibration of the test instrument; s2, determining the distance between the laser light source and the focal plane array to be measured, setting a data compression template according to the characteristics of the focal plane array, and performing non-uniform interval division on output data; s3, estimating the occurrence probability of each interval data, and generating an output data interval mapping table; s4, on the basis of the interval division and the mapping table, Huffman coding is carried out on each output data interval, and an original output data coding mapping table is generated; and S5, in a data acquisition process, performing real-time compression on the acquired data by utilizing the generated original output data coding mapping table. According to the invention, the storage space of the test data is saved, the transmission time of the test data is shortened, and high-efficiency processing of the test data in the later period is facilitated.

Description

Technical Field

[0001] The invention belongs to the technical field of electronic product testing and relates to a real-time data compression method for focal plane array performance testing. Background Art

[0002] In order to evaluate the performance of the focal plane array, it is necessary to test it after it is produced. However, as the size of the array increases, a huge amount of test data will be obtained during the test process. Even if high-performance computers and data transmission systems are used, storing, transmitting and processing these massive amounts of data will take up a lot of resources, which is not conducive to the real-time processing of the focal plane array data. Therefore, it is necessary to compress the test data of the focal plane array in real time. Data compression algorithms have been widely used in other fields, and good results have been achieved in compressing text, images, sounds, videos and other information. In addition, a combination of software and hardware has emerged to achieve efficient real-time compression. Referring to data compression algorithms in other fields, the statistical distribution characteristics of the output data of the focal plane array are fully considered, and a combination of software and hardware is used to design a real-time data compression method for focal plane array performance testing. Summary of the invention

[0003] (I) Purpose of the invention

[0004] The purpose of the present invention is to design a real-time data compression method for focal plane array performance testing to solve the problems of efficient storage, transmission and processing of massive data during focal plane array testing.

[0005] (II) Technical solution

[0006] In order to solve the above technical problems, the present invention provides a real-time data compression method for focal plane array performance testing, which comprises the following steps:

[0007] S1. Connect the test instrument and the focal plane array to be tested, and calibrate the test instrument;

[0008] The test equipment includes: program-controlled computer, power supply, laser light source, signal generator, data acquisition device;

[0009] S2, determining the distance between the laser light source and the focal plane array to be measured, setting a data compression template according to the characteristics of the focal plane array, and dividing the output data into non-uniform intervals;

[0010] S3, estimate the probability of occurrence of data in each interval, and generate an output data interval mapping table;

[0011] S4. Based on the above interval division and mapping table, each output data interval is Huffman coded to generate an original output data coding mapping table;

[0012] S5. During the data collection process, the generated original output data encoding mapping table is used to compress the collected data in real time.

[0013] In step S1, the program-controlled computer is connected to the signal generator and the data acquisition device using a USB, LAN or GPIB interface, and SCPI is used for standardized instrument control.

[0014] In step S2, the program-controlled computer calls the tree instruction structure of SCPI to implement the application of the test signal and the collection of the focal plane array output data.

[0015] In step S5, a method combining software and hardware is used to quickly search the original output data encoding mapping table to achieve real-time compression of the test data.

[0016] In step S1, during the calibration of the test instrument, the distance between the laser light source window and the focal plane array is set to d, and the delay between the start test signal and the laser pulse is set to t delay , then the flight time of the photons emitted by the laser light source to the focal plane array is estimated to be:

[0017]

[0018] Where c is the speed of light.

[0019] In step S2, the compression template uses a graph to represent the relationship between the weight and the focal plane array output data, which is used to estimate the probability distribution in the subsequent steps.

[0020] In step S2, according to the relationship between the photon flight time set in the readout circuit and the focal plane array output value, the position of the maximum weight point P is determined according to the flight time estimated t value. The figure also shows the non-uniform interval division of the focal plane array output data: the area near the P point is set as the key area, and the details of the key area are retained; other areas are non-key areas, and the details of the non-key areas are ignored to achieve data compression; among them, the interval division of the key area is dense, and the interval division of the non-key area is sparse.

[0021] In step S3, the process of generating the output data interval mapping table is as follows: suppose the original data set is X = {x1, x2, ..., x n}, n is the maximum output data of the focal plane array, and the corresponding weight set W(X) = {w(x1),w(x2),…,w(x n )}, interval symbol set Y = {y1,y2,…,y m}, m is the number of intervals divided, then any interval y k The weight is the original data {x r ,x r+1 ,…,x sThe sum of weights:

[0022]

[0023] Normalize the weights of each interval to get:

[0024]

[0025] In the formula, all p constitute the interval probability set P, together with the interval symbol set Y as the input set of Huffman coding, and Huffman coding is used to generate the original output data coding mapping table.

[0026] In step S4, the process of generating the original output data encoding mapping table using Huffman coding is as follows: assuming that the initial Huffman tree T is an empty set, when the number of elements in the interval probability set P is greater than 1, the following steps are executed cyclically: selecting the two smallest elements in P, corresponding to y in the interval symbol set Y i ,y j ; If node y does not exist in T i or j , then add the corresponding node to T; let y be y i With y j The parent node of i )+p(y j ); remove p(y from P i ) and p(y j ), and add p(y); finally generate the Huffman tree T, then the binary code of each interval is constructed from the root node to the corresponding leaf node, thereby generating the original data coding mapping table.

[0027] (III) Beneficial effects

[0028] The real-time data compression method for focal plane array performance testing provided by the above technical solution is based on the characteristics of the focal plane array, and the statistical distribution of the focal plane output data is estimated after the test device is calibrated, and the data compression method is set; the output data interval mapping table is generated by dividing the focal plane output data into non-uniform intervals, and the probability of occurrence of each interval data is estimated, and each output data interval is Huffman encoded to generate an original output data encoding mapping table; SCPI is used for standardized instrument control, and the design concept of combining software and hardware is used to realize the rapid search of the original output data encoding mapping table during the focal plane array test process, thereby realizing real-time data compression. The present invention saves the storage space of the test data, shortens the time of test data transmission, and is conducive to the efficient processing of the test data in the later stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is an overall block diagram of the focal plane array test data compression method adopted by the present invention;

[0030] Figure 2 A schematic diagram of the data compression template and non-uniform interval division used in the present invention;

[0031] Figure 3 This is a flow chart of generating an original data encoding mapping table using a Huffman encoding algorithm according to an interval symbol set and a corresponding probability set in the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the drawings and examples.

[0033] The data compression technology proposed in the present invention sets a compression template according to the calibration result of the test instrument, establishes an output data interval mapping table, and then uses the Huffman coding algorithm to generate the original output data coding mapping table. The software and hardware are combined to perform fast table lookup and realize real-time compression of test data during the focal plane array test process.

[0034] Reference Figure 1 As shown, the real-time data compression method for focal plane array performance testing in this embodiment includes the following steps:

[0035] S1. Connect the test instrument and the focal plane array to be tested, and calibrate the test instrument;

[0036] The test equipment includes: program-controlled computer, power supply, laser light source, signal generator, data acquisition device;

[0037] S2, determining the distance between the laser light source and the focal plane array to be measured, setting a data compression template according to the characteristics of the focal plane array, and dividing the output data into non-uniform intervals;

[0038] S3, estimate the probability of occurrence of data in each interval, and generate an output data interval mapping table;

[0039] S4. Based on the above interval division and mapping table, each output data interval is Huffman coded to generate an original output data coding mapping table;

[0040] S5. During the data collection process, the generated original output data encoding mapping table is used to compress the collected data in real time.

[0041] In step S1, the program-controlled computer is connected to the signal generator and the data acquisition device using a USB, LAN or GPIB interface, and SCPI is used for standardized instrument control.

[0042] In step S5, a method combining software and hardware is used to quickly search the original output data encoding mapping table to achieve real-time compression of the test data.

[0043] In order to standardize the control process of the instrument and facilitate the compression of real-time test data, the programmable instrument standard command SCPI is used to control the above instruments with instructions in a unified format. In the software design of the programmable computer, the tree command structure of SCPI is called to realize the application of test signals and the collection of focal plane array output data.

[0044] During the test, the distance between the laser light source and the focal plane array to be tested remains unchanged. Ideally, each pixel of the focal plane array produces the same output value. However, under weak light, the output values ​​generated by each pixel have a certain statistical distribution. For the focal plane array data obtained by the program-controlled computer using SCPI, a compression algorithm is proposed to compress the data. The basic idea of ​​the compression algorithm is to retain the details of the output data interval with a higher probability of occurrence based on the statistical distribution of the focal plane array output data, and ignore the details of other data intervals, so as to reduce the amount of data generated during the test without significantly affecting the performance evaluation of the focal plane array.

[0045] In step S1, during the calibration of the test instrument, the distance between the laser light source window and the focal plane array is set to d, and the delay between the start test signal and the laser pulse is set to t delay , then the flight time of the photons emitted by the laser light source to the focal plane array can be estimated as:

[0046]

[0047] Where c is the speed of light.

[0048] The compression template based on the prior statistical distribution is as follows Figure 2 As shown in the figure, the solid line represents the relationship between the weight and the focal plane array output data, which is used to estimate the probability distribution in the subsequent steps. According to the relationship between the photon flight time set in the readout circuit and the focal plane array output value, the position of point P in the figure can be determined according to the above t value. The dotted line in the figure represents the non-uniform interval division of the focal plane array output data: the area near point P is the key area, and the details of this area should be retained as much as possible; while other areas are non-key areas, and their details should be ignored to achieve data compression. Therefore, near point P with a larger weight, the interval division is dense; while in other areas with smaller weights, the interval division is relatively sparse.

[0049] according to Figure 2 , we can build an output data interval mapping table. Let the original data set be X = {x1, x2, …, x n} (n is the maximum output data of the focal plane array), the corresponding weight set W(X) = {w(x1),w(x2),…,w(x n )}, interval symbol set Y = {y1,y2,…,ym}(m is the number of intervals divided), then any interval y k The weight is the original data {x r ,x r+1 ,…,x s The sum of weights:

[0050]

[0051] Normalize the weights of each interval to get:

[0052]

[0053] In the formula, m represents the number of divided intervals. All p constitute the interval probability set P, together with the interval symbol set Y as the input set of Huffman coding.

[0054] The process of generating the original data encoding mapping table using Huffman coding is as follows: Figure 3 As shown. Assume that the initial Huffman tree T is an empty set. When the number of elements in the interval probability set P is greater than 1, the following steps are executed cyclically: Select the two smallest elements in P, corresponding to y in the interval symbol set Y i ,y j ; If node y does not exist in T i or j , then add the corresponding node to T; let y be y i With y j The parent node of i )+p(y j ); remove p(y from P i ) and p(y j ), and add p(y). Finally, the Huffman tree T is generated, and the binary code of each interval can be constructed from the root node to the corresponding leaf node, thereby generating the original data coding mapping table.

[0055] In the original data encoding mapping table, the data with a higher probability of occurrence has a shorter encoding, and the data with a lower probability of occurrence has a longer encoding. Therefore, in the focal plane array test process, the original data encoding mapping table can be used to effectively achieve data compression. The original data encoding mapping table is quickly searched by combining software and hardware, thereby achieving a fast and efficient data compression process in the focal plane array test process.

[0056] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A real-time data compression method for focal plane array performance testing, characterized in that: The following steps are involved: S1. Connect the test instrument and the focal plane array to be tested, and calibrate the test instrument; The test equipment includes: program-controlled computer, power supply, laser light source, signal generator, data acquisition device; S2, determining the distance between the laser light source and the focal plane array to be measured, setting a data compression template according to the characteristics of the focal plane array, and dividing the output data into non-uniform intervals; S3, estimate the probability of occurrence of data in each interval, and generate an output data interval mapping table; S4. Based on the above interval division and mapping table, each output data interval is Huffman coded to generate an original output data coding mapping table; S5. During the data collection process, the generated original output data encoding mapping table is used to compress the collected data in real time.

2. The real-time data compression method for focal plane array performance testing according to claim 1, characterized in that: In step S1, the program-controlled computer is connected to the signal generator and the data acquisition device using a USB, LAN or GPIB interface, and SCPI is used for standardized instrument control.

3. The real-time data compression method for focal plane array performance testing according to claim 2, characterized in that: In step S2, the program-controlled computer calls the tree instruction structure of SCPI to implement the application of the test signal and the collection of the focal plane array output data.

4. The real-time data compression method for focal plane array performance testing according to claim 1, characterized in that: In step S5, a method combining software and hardware is used to quickly search the original output data encoding mapping table to achieve real-time compression of the test data.

5. The real-time data compression method for focal plane array performance testing according to claim 1, characterized in that: In step S1, during the calibration of the test instrument, the distance between the laser light source window and the focal plane array is set to d, and the delay between the start test signal and the laser pulse is set to t delay , then the flight time of the photons emitted by the laser light source to the focal plane array is estimated to be: Where c is the speed of light.

6. The real-time data compression method for focal plane array performance testing according to claim 1, characterized in that: In step S2, the compression template uses a graph to represent the relationship between the weight and the focal plane array output data, which is used to estimate the probability distribution in the subsequent steps.

7. The real-time data compression method for focal plane array performance testing according to claim 6, characterized in that: In step S2, according to the relationship between the photon flight time set in the readout circuit and the focal plane array output value, the position of the maximum weight point P is determined according to the flight time estimated t value. The figure also shows the non-uniform interval division of the focal plane array output data: the area near the P point is set as the key area, and the details of the key area are retained; other areas are non-key areas, and the details of the non-key areas are ignored to achieve data compression; among them, the interval division of the key area is dense, and the interval division of the non-key area is sparse.

8. The real-time data compression method for focal plane array performance testing according to claim 7, characterized in that: In step S3, the process of generating the output data interval mapping table is as follows: suppose the original data set is X = {x1, x2, ..., x n }, n is the maximum output data of the focal plane array, and the corresponding weight set W(X) = {w(x1),w(x2),…,w(x n )}, interval symbol set Y = {y1,y2,…,y m }, m is the number of intervals divided, then any interval y k The weight is the original data {x r ,x r+1 ,…,x s }The sum of weights: Normalize the weights of each interval to get: In the formula, all p constitute the interval probability set P, together with the interval symbol set Y as the input set of Huffman coding, and Huffman coding is used to generate the original output data coding mapping table.

9. The real-time data compression method for focal plane array performance testing according to claim 8, characterized in that: In step S4, the process of generating the original output data encoding mapping table using Huffman coding is as follows: assuming that the initial Huffman tree T is an empty set, when the number of elements in the interval probability set P is greater than 1, the following steps are executed cyclically: selecting the two smallest elements in P, corresponding to y in the interval symbol set Y i ,y j ; If node y does not exist in T i or j , then add the corresponding node to T; let y be y i With y j The parent node of i )+p(y j ); remove p(y from P i ) and p(y j ), and add p(y); finally generate the Huffman tree T, then the binary code of each interval is constructed from the root node to the corresponding leaf node, thereby generating the original data coding mapping table.

10. Application of the real-time data compression method for focal plane array performance testing according to any one of claims 1 to 9 in the field of electronic product testing technology.

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