阅读说明：本技术 一种无快门的非均匀校正的方法 (Shutter-free non-uniform correction method ) 是由 彭玲 公志强 汪利庆 刘仁军 于 2021-04-13 设计创作，主要内容包括：本发明提供了一种无快门的非均匀校正的方法,在探测器工作范围内,分别采集四个不同温度黑体的本底,连续十张本底求均值,去除时域噪声,保存本底。根据机芯的温度分别拟合四个不同温度黑体的本底,同时计算其均值,根据均值和像素值构建多段函数,原始图像经过导向滤波计算其均值,根据这个均值计算新本底,同时根据这个均值,判断计算增益所需的高温本底和低温本底。实时计算本底和增益,考虑了探测器温度和环境温度对红外图像非均匀校正的影响。本发明无需快门,不仅不会中断探测器工作过程,还能节约成本,同时能解决温漂问题,能够自适应的校正红外图像。(The invention provides a shutter-free non-uniform correction method, which is characterized in that in the working range of a detector, the background of four black bodies with different temperatures is respectively collected, ten continuous backgrounds are averaged, time domain noise is removed, and the background is stored. The method comprises the steps of respectively fitting the backgrounds of four black bodies with different temperatures according to the temperature of a machine core, simultaneously calculating the average value of the black bodies, constructing a multi-section function according to the average value and a pixel value, calculating the average value of an original image through guide filtering, calculating a new background according to the average value, and judging a high-temperature background and a low-temperature background required by gain calculation according to the average value. And calculating background and gain in real time, and considering the influence of the temperature of the detector and the ambient temperature on the non-uniform correction of the infrared image. The invention does not need a shutter, does not interrupt the working process of the detector, can save cost, can solve the temperature drift problem and can adaptively correct the infrared image.)

1. A method of shutter-less non-uniformity correction, comprising:

s100, placing an infrared core in a high-low temperature box, respectively placing black bodies with the temperature of T1, T2, T3 and T4 degrees outside the high-low temperature box, electrifying to continuously collect the background after the temperature of the core is stable, averaging ten continuous backgrounds, removing time domain noise, continuously storing the background, and naming the stored background by the FPA value of the core;

s200, fitting the background of a black body with the degrees of T1, T2, T3 and T4 respectively by a least square method according to the temperature of the movement;

s300, calculating the mean values of the fitted blackbody background of T1, T2, T3 and T4 degrees respectively;

s400, respectively forming a function coordinate system by the mean value of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree and the pixel values of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree, and obtaining a corresponding function relation formula by a quadratic spline method;

s500, carrying out guided filtering on the original image to obtain an average value, and substituting the average value into S400 to obtain a functional relation formula, so as to obtain a new background;

s600, conducting guided filtering on an original image to obtain an average value, and judging a real-time high-temperature background and a real-time low-temperature background required by calculating a gain K according to the average value;

and S700, subtracting the calculated background from the original image, and multiplying the background by a gain K to finish the non-uniform correction of the infrared image.

2. The method of shutter-less non-uniformity correction according to claim 1, wherein in S200, the method of fitting the background of the T1, T2, T3 and T4 degree black bodies by the least square method according to the temperature of the movement, respectively, is: obtaining a core FPA value FPA1(n) and a first pixel point B of a collected T1 temperature black body sample_(1,1)(T1(n)) isPair of FPA1(n) and B with a unitary cubic function_(1,1)(T1(n)) fitting, determining a coefficient corresponding to the unitary cubic function through the minimum value of the sum of squares of the difference between the actual first pixel value of the nth sample of the collected T1 temperature black body and the fitted pixel value, obtaining the best fit curve which accords with the T1-degree black body, and respectively obtaining the best fit curves of the T2, the T3 and the T4-degree black body by adopting the same method.

3. The method of shutter-less non-uniformity correction according to claim 1, wherein in S300, the formula for calculating the mean of the fitted T1, T2, T3 and T4 degrees blackbody background is:

wherein Tn is T1, T2, T3 and T4, meanTn represents the average value of T1, T2, T3 and T4 degrees of blackbody background, B_(i,j)(Tn) represents the (i, j) th pixel point of the T1, T2, T3, and T4 degree blackbody, m represents the length of the image, and n represents the width of the image.

4. The method for shutter-less non-uniformity correction according to claim 1, wherein in S400, the piecewise function expression of the correspondence between the mean value of the fitted black body and the first pixel point of the fitted black body is:

wherein mean represents the mean of the original image after guide filtering, and the fitted mean means mean 1, mean 2, mean 3 and mean 4 and the first pixel value B of the corresponding fitted background pair_(1,1)(T1)、B_(1,1)(T2)，、B_(1,1)(T3)，、B_(1,1)(T4) substituting the above function to obtain coefficients a1, b1, c1, a2, b2, c2, a3, b3, and c3 of the function; substituting the coefficients of the calculated function into B_(1,1)(T) in the expression, the mean value and the secondThe function of one pixel is used for sequentially calculating the pixel values and the mean value of other points by the method.

5. A method of shutter-less non-uniformity correction as claimed in claim 1, characterized in that in S500 the original image is subjected to a guided filtering, the mean is found, and the mean is substituted into the function obtained in S500 to obtain the new background B (i, j).

6. The method of shutter-less non-uniformity correction according to claim 1, wherein in S600, the mean after guided filtering is compared with T2 and T3 degree blackbody means mean T2, mean T3, and when the mean is less than mean T2, the gain K takes the value K1(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value of K2(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value K3(i, j).

7. The method of shutter-less non-uniformity correction according to claim 6, wherein the gain K1(i, j), gain K2(i, j), gain K3(i, j) are calculated by the formula:

where meanT1 represents the mean of a fitted T1 temperature bold body, meanT2 represents the mean of a fitted T2 temperature bold body, meanT3 represents the mean of a fitted T3 temperature bold body, meanT4 represents the mean of a fitted T4 temperature bold body, B_(i,j)(T1) represents the pixel value of a fitted T1 temperature black body, B_(i,j)(T2) represents the pixel values of a fitted T2 temperature black body，B_(i,j)(T3) represents the pixel value of a fitted T3 temperature black body, B_(i,j)(T4) represents the pixel values of the fitted T4 temperature bold, i and j represent coordinate points of the fitted background pixels.

8. The method of shutter-less non-uniformity correction according to claim 1, wherein in S700, the infrared image is non-uniformly corrected based on the above-determined fitting backgrounds B (i, j) and K (i, j), by:

NUC(i,j)＝(SRC(i,j)-B(i,j))*K(i,j)

where NUC (i, j) represents the corrected infrared image, SRC (i, j) represents the input raw image, B (i, j) represents the newly calculated background, and K (i, j) represents the newly calculated gain.

Technical Field

The invention relates to the field of infrared correction, in particular to a shutter-free non-uniform correction method.

Background

Under the influence of manufacturing materials, manufacturing processes and other reasons, the response rates of all detection units of the infrared detector are inconsistent, so that the infrared focal plane array has common non-uniformity, and the imaging quality of the infrared detector is seriously influenced. At present, two-point correction is generally adopted to carry out non-uniform correction on an infrared image. The infrared detector is greatly influenced by the ambient temperature, and the correction coefficient needs to be continuously updated periodically, namely, the infrared detector continuously opens a shutter, so that the working process of the infrared detector is suspended, and the application range of the infrared detector is severely restricted. Meanwhile, the periodic shutter opening can not only increase the cost of the product, but also increase the process of the structural design of the product.

In addition, the gain K of the existing infrared detector needs to be calculated in advance and led into the movement each time, but in practical application, the ambient temperature may change, which affects the temperature of the detector and thus the imaging, and the gain K calculated in advance may not be used or function well when being used, even if the gain K is changed along with the time, the temperature change cannot achieve the correction effect or the correction effect is not good. Based on this, it is highly desirable to find a method for shutter-free and adaptive non-uniformity correction.

Disclosure of Invention

In view of the above, the present invention has been developed to provide a method of shutter-less non-uniformity correction that overcomes, or at least partially solves, the above-mentioned problems.

In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:

a method of shutter-less non-uniformity correction, comprising:

s100, placing an infrared core in a high-low temperature box, respectively placing black bodies with the temperature of T1, T2, T3 and T4 degrees outside the high-low temperature box, electrifying to continuously collect the background after the temperature of the core is stable, averaging ten continuous backgrounds, removing time domain noise, continuously storing the background, and naming the stored background by the FPA value of the core;

s200, fitting the background of a black body with the degrees of T1, T2, T3 and T4 respectively by a least square method according to the temperature of the movement;

s300, calculating the mean values of the fitted blackbody background of T1, T2, T3 and T4 degrees respectively;

s400, respectively forming a function coordinate system by the mean value of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree and the pixel values of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree, and obtaining a corresponding function relation formula by a quadratic spline method;

s500, carrying out guided filtering on the original image to obtain an average value, and substituting the average value into S400 to obtain a functional relation formula, so as to obtain a new background;

s600, conducting guided filtering on an original image to obtain an average value, and judging a real-time high-temperature background and a real-time low-temperature background required by calculating a gain K according to the average value;

and S700, subtracting the calculated background from the original image, and multiplying the background by a gain K to finish the non-uniform correction of the infrared image.

Further, in S200, the method of fitting the background of the T1, T2, T3, and T4 degree blackbodies by the least square method, respectively, according to the temperature of the movement is: obtaining a core FPA value FPA1(n) and a first pixel point B of a collected T1 temperature black body sample_(1,1)(T1(n)), applying a one-dimensional cubic function to FPA1(n) and B_(1,1)(T1(n)) fitting, determining a coefficient corresponding to the unitary cubic function through the minimum value of the sum of squares of the difference between the actual first pixel value of the nth sample of the collected T1 temperature black body and the fitted pixel value, obtaining the best fit curve which accords with the T1-degree black body, and respectively obtaining the best fit curves of the T2, the T3 and the T4-degree black body by adopting the same method.

Further, in S300, the formula for calculating the mean of the fitted T1, T2, T3 and T4 degree blackbody background is:

wherein Tn is T1, T2, T3 and T4, meanTn represents the average value of T1, T2, T3 and T4 degrees of blackbody background, B_(i,j)(Tn) represents the (i, j) th pixel point of the T1, T2, T3, and T4 degree blackbody, m represents the length of the image, and n represents the width of the image.

Further, in S400, the piecewise function expression of the corresponding relationship between the mean value of the fitted black body and the first pixel point of the fitted black body is:

wherein mean represents the mean of the original image after guide filtering, and the fitted mean means mean 1, mean 2, mean 3 and mean 4 and the first pixel value B of the corresponding fitted background pair_(1,1)(T1)、B_(1,1)(T2)，、B_(1,1)(T3)，、B_(1,1)(T4) substituting the above function to obtain coefficients a1, b1, c1, a2, b2, c2, a3, b3, and c3 of the function; substituting the coefficients of the calculated function into B_(1,1)In the expression of (T), the mean value and the function of the first pixel are obtained, and the pixel values of other points and the function of the mean value are calculated in sequence by using the above method.

Further, in S500, the original image is subjected to guided filtering, an average mean is calculated, and the mean is substituted into the function obtained in S500 to obtain a new background B (i, j).

Further, in S600, the mean after the guiding filtering is compared with T2 and T3 degree blackbody means mean T2 and mean T3, and when the mean is less than mean T2, the gain K takes the value of K1(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value of K2(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value K3(i, j).

Further, the formula for calculating the gain K1(i, j), the gain K2(i, j), and the gain K3(i, j) is:

where meanT1 represents the mean of a fitted T1 temperature bold body, meanT2 represents the mean of a fitted T2 temperature bold body, meanT3 represents the mean of a fitted T3 temperature bold body, meanT4 represents the mean of a fitted T4 temperature bold body, B_(i,j)(T1) represents the pixel value of a fitted T1 temperature black body, B_(i,j)(T2) represents the pixel value of a fitted T2 temperature black body, B_(i,j)(T3) represents the pixel value of a fitted T3 temperature black body, B_(i,j)(T4) represents the pixel values of the fitted T4 temperature bold, i and j represent coordinate points of the fitted background pixels.

Further, in S700, according to the obtained fitting backgrounds B (i, j) and K (i, j), non-uniform correction is performed on the infrared image, which specifically includes:

NUC(i,j)＝(SRC(i,j)-B(i,j))*K(i,j)

where NUC (i, j) represents the corrected infrared image, SRC (i, j) represents the input raw image, B (i, j) represents the newly calculated background, and K (i, j) represents the newly calculated gain.

The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:

the shutter-free non-uniform correction method provided by the invention is characterized in that the background of four black bodies with different temperatures is respectively collected in the working range of a detector, ten continuous background averages are obtained, time domain noise is removed, and the background is stored. The method comprises the steps of respectively fitting the backgrounds of four black bodies with different temperatures according to the temperature of a machine core, calculating the mean value of the black bodies, constructing a multi-section function according to the mean value and a pixel value, calculating the mean value of an original image through guide filtering, calculating a new background according to the mean value, and judging a high-temperature background and a low-temperature background required by calculating the gain K according to the mean value. And calculating the background and the gain K in real time, and considering the influence of the temperature of the detector and the ambient temperature on the non-uniform correction of the infrared image. The invention does not need to periodically open the shutter, does not interrupt the working process of the detector, can save the cost and can adaptively correct the infrared image. According to the mean value of the original image after guiding filtering, the gain K, the required high-temperature background and the low-temperature background are calculated in real time, the influence of the environment temperature on the detector is considered, the infrared image can be corrected in a self-adaptive mode, and the non-uniformity correction of the infrared image is completed.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is a flowchart of a method for shutter-less non-uniformity correction in accordance with embodiment 1 of the present invention;

FIG. 2 is a logic diagram of a method for shutter-less non-uniformity correction in accordance with embodiment 1 of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

In order to solve the problems in the prior art, embodiments of the present invention provide a method for shutter-free non-uniformity correction.

Example 1

The embodiment discloses a method for shutter-free non-uniformity correction, which comprises the following steps of:

s100, placing an infrared core in a high-low temperature box, respectively placing black bodies with the temperature of T1, T2, T3 and T4 degrees outside the high-low temperature box, electrifying to continuously collect the background after the temperature of the core is stable, averaging ten continuous backgrounds, removing time domain noise, continuously storing the background, and naming the stored background by the FPA value of the core.

S200, fitting the background of the black bodies of T1, T2, T3 and T4 degrees by a least square method according to the temperature of the movement.

In this embodiment S200, according to the temperature of the movement, the background of the T1 degree black body, the background of the T2 degree black body, the background of the T3 degree black body, and the background of the T4 degree black body are respectively fitted by a least square method, and it is found through actual simulation that the pixel values of the FPA and the background of the movement resemble a unitary cubic function curve, where a unitary cubic function is used, and the data points are data points

Obtaining a core FPA1(n) value and a first pixel point B of the collected T1 temperature black body sample through least square fitting_(1,1)(T1(n)) as a function of the correspondence between: y ═ a × x³+b*x²+ c x + d. The following relationship is satisfied:

where n represents the number of samples collected, B (T1(n)) represents the sample of the collected T1 temperature black body, FPA1(n) represents the FPA value corresponding to the nth sample in the T1 temperature black body sample, and B_(1,1)(T1(n)) represents the first pixel value in the nth sample of the T1 temperature blackbody sample, R_(1,1)(T1(n)) represents the first pixel value in the nth sample of the fitted T1 temperature black body, and p (n) represents the sum of the squares of the differences between the actual first pixel value and the fitted pixel value for the nth sample of the acquired T1 temperature black body.

The corresponding coefficients a, B, c and d are obtained by calculating the partial derivatives, so that the T1 temperature black body, the function of the FPA value and the pixel value can be obtained, and the first pixel value B of the fitting can be further calculated_(1,1)(T1). Analogically calculating all pixel values B of T1 temperature black body_(i,j)(T1)。

The same method is used for obtaining the pixel value B of the T2 temperature black body_(i,j)(T2), the same method is used to determine the pixel value B of the T3 temperature black body_(i,j)(T3), the same method is used to determine the pixel value B of the T4 temperature black body_(i,j)(T4)。

S300, calculating the mean values of the fitted blackbody background of T1, T2, T3 and T4 degrees respectively; in this example S300, the formula for calculating the mean of the fit T1, T2, T3 and T4 degree blackbody background is:

wherein Tn is T1, T2, T3 and T4, meanTn represents the average value of T1, T2, T3 and T4 degrees of blackbody background, B_(i,j)(Tn) represents the (i, j) th pixel point of the T1, T2, T3, and T4 degree blackbody, m represents the length of the image, and n represents the width of the image.

S400, respectively forming a function coordinate system by the mean value of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree and the pixel values of the background of the black body with the fitted T1 degree, T2 degree, T3 degree and T4 degree, and obtaining a corresponding function relation formula by a quadratic spline method.

In this embodiment S400, a coordinate point is formed by the mean value of the background of the fitted T1 degree black body mean T1 and the pixel value of the background of the fitted T1 degree black body, and by analogy, a corresponding functional relation is obtained by a quadratic spline method for the T2 degree black body, the T3 degree black body, and the T4 degree black body, and the piecewise function expression of the correspondence between the mean value of the fitted black body and the first pixel point of the fitted black body is:

where mean represents the mean of the raw image after guided filtering, the fitted means mean values mean 1, mean 2, mean 3, mean 4 and the first pixel value B of the corresponding fitted background pair_(1,1)(T1)、B_(1,1)(T2)，、B_(1,1)(T3)，、B_(1,1)(T4), substituting the function above yields:

a1＝0

a1*meanT1²+b1*meanT1+c1＝B_(1,1)(T1)

a1*meanT2²+b1*meanT2+c1＝B_(1,1)(T2)

a2*meanT2²+b2*meanT2+c2＝B_(1,1)(T2)

a2*meanT3²+b2*meanT3+c2＝B_(1,1)(T3)

a1*meanT2+b1＝a2*meanT2+b2

a2*meanT3+b2＝a3*meanT3+b3

a3*meanT3²+b3*meanT3+c3＝B_(1,1)(T3)

a3*meanT4²+b3*meanT4+c3＝B_(1,1)(T4)

the coefficients a1, b1, c1, a2, b2, c2, a3, b3, c3 of the function are calculated by the following formula:

substituting the coefficients of the calculated function into B_(1,1)And (T) obtaining the average value and the function of the first pixel in the expression of (T), and sequentially calculating the pixel values of other points and the function of the average value by using the method.

S500, carrying out guided filtering on the original image to obtain an average value, and substituting the average value into S400 to obtain a functional relation formula, so as to obtain a new background; in this embodiment S500, the original image is subjected to guided filtering, mean is obtained, and the mean is substituted into the function obtained in S500 to obtain a new background B (i, j).

S600, conducting guided filtering on an original image to obtain an average value, and judging a real-time high-temperature background and a real-time low-temperature background required by calculating a gain K according to the average value; in this embodiment S600, as shown in fig. 2, the mean after the guiding filtering is compared with T2 and T3 degree blackbody means mean T2 and mean T3, and when the mean is smaller than mean T2, the gain K takes the value K1(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value of K2(i, j); when mean is greater than mean T2 and less than T3, the gain K takes the value K3(i, j).

Specifically, the formula for calculating the gain K1(i, j), the gain K2(i, j), and the gain K3(i, j) is:

where meanT1 represents the mean of a fitted T1 temperature bold body, meanT2 represents the mean of a fitted T2 temperature bold body, meanT3 represents the mean of a fitted T3 temperature bold body, meanT4 represents the mean of a fitted T4 temperature bold body, B_(i,j)(T1) represents the pixel value of a fitted T1 temperature black body, B_(i,j)(T2) represents the pixel value of a fitted T2 temperature black body, B_(i,j)(T3) represents the pixel value of a fitted T3 temperature black body, B_(i,j)(T4) represents the pixel values of the fitted T4 temperature bold, i and j represent coordinate points of the fitted background pixels.

And S700, subtracting the calculated background from the original image, and multiplying the background by a gain K to finish the non-uniform correction of the infrared image.

In this embodiment S700, according to the above-obtained fitting backgrounds B (i, j) and K (i, j), the infrared image is non-uniformly corrected, which includes the following specific processes:

NUC(i,j)＝(SRC(i,j)-B(i,j))*K(i,j)

where NUC (i, j) represents the corrected infrared image, SRC (i, j) represents the input raw image, B (i, j) represents the newly calculated background, and K (i, j) represents the newly calculated gain.

In the shutter-free non-uniform correction method provided by the embodiment, in the working range of the detector, the backgrounds of four black bodies with different temperatures are respectively collected, ten continuous backgrounds are averaged, time domain noise is removed, and the backgrounds are stored. The method comprises the steps of respectively fitting the backgrounds of four black bodies with different temperatures according to the temperature of a machine core, calculating the mean value of the black bodies, constructing a multi-section function according to the mean value and a pixel value, calculating the mean value of an original image through guide filtering, calculating a new background according to the mean value, and judging a high-temperature background and a low-temperature background required by calculating the gain K according to the mean value. And calculating the background and the gain K in real time, and considering the influence of the temperature of the detector and the ambient temperature on the non-uniform correction of the infrared image. The invention does not need to periodically open the shutter, does not interrupt the working process of the detector, can save the cost and can adaptively correct the infrared image. According to the mean value of the original image after guiding filtering, the gain K, the required high-temperature background and the low-temperature background are calculated in real time, the influence of the environment temperature on the detector is considered, the infrared image can be corrected in a self-adaptive mode, and the non-uniformity correction of the infrared image is completed.

It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy.

In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.

For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions of the present application. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".