Device and method of emitting an estimated value

The present invention relates to encoders for encoding a signal comprising audio and / or video information, and more particularly to estimating a need for information units to encode that signal.

The known coder is shown below. At an input 1000, an audio signal to be coded is fed. This is first supplied to a scaling stage 1002 in which a so-called AAC gain control is performed to set the level of the audio signal. Scaling page information is provided to a bitstream formatter 1004, as indicated by the arrow between block 1002 and block 1004. The scaled audio signal is then applied to an MDCT filter bank 1006. In the AAC encoder, the filter bank implements a modified discrete cosine transform with 50% overlapping windows, the window length being determined by a block 1008.

Generally speaking, block 1008 is for windowing transient signals with shorter windows, and for windowing stationary signals with longer windows. This serves to achieve a higher time resolution (at the expense of frequency resolution) due to the shorter transient signal windows, while higher steady state signals achieve higher frequency resolution (at the expense of time resolution) through longer windows, with longer windows tend to be preferred because they promise a larger coding gain. At the output of the filter bank 1006, temporally successive blocks of spectral values ​​are present which, depending on the embodiment of the filter bank, may be MDCT coefficients, Fourier coefficients or even subband signals.

The following is an example of the case in which the filter bank outputs temporally successive blocks of MDCT spectral coefficients, which generally represent successive short-term spectra of the audio signal to be encoded at input 1000. A block of MDCT spectral values ​​is then fed to a TNS processing block 1010 where temporal noise shaping (TNS) takes place. The TNS technique is used to shape the temporal shape of the quantization noise within each window of the transform. This is achieved by applying a filtering process to parts of the spectral data of each channel. The coding is performed on a window basis. In particular, the following steps are carried out

First, a frequency range is selected for the TNS tool. A suitable choice is to cover a frequency range of 1.5 kHz up to the highest possible scale factor band with a filter. It should be noted that this frequency range depends on the sampling rate as specified in the AAC standard (ISO / IEC 14496-3: 2001 (E)).

Subsequently, LPC (LPC = linear predictive coding) calculation is performed with the spectral MDCT coefficients lying within the selected target frequency range. For increased stability, coefficients corresponding to frequencies below 2.5 kHz are excluded from this process. Conventional LPC procedures, as known from speech processing, can be used for the LPC calculation, for example the known Levinson-Durbin algorithm. The calculation is performed for the maximum allowable order of the noise shaping filter.

As a result of the LPC calculation, the expected prediction gain PG is obtained. Further, the reflection coefficients or Parcor coefficients are obtained.

If the prediction gain does not exceed a certain threshold, the TNS tool is not applied. In this case, control information is written in the bit stream for a decoder to know that no TNS processing has been performed.

However, if the prediction gain exceeds a threshold, TNS processing is applied.

In a next step, the reflection coefficients are quantized. The order of the noise shaping filter used is determined by removing all the reflection coefficients having an absolute value less than a threshold from the "tail" of the reflection coefficient array.

The number of remaining reflection coefficients is on the order of the noise shaping filter. A suitable threshold is 0.1.

The remaining reflection coefficients are typically converted to linear prediction coefficients, which technique is also known as a "step-up" procedure.

The calculated LPC coefficients are then used as coder noise shaping filter coefficients, ie as prediction filter coefficients. This FIR filter is routed over the specified target frequency range. The decoding uses an autoregressive filter, while the coding uses a so-called moving average filter. Finally, the page information for the TNS tool is also supplied to the bit stream formatter, as shown by the arrow between the block TNS processing 1010 and the bit stream formatter 1004 in FIG<figref idref="f0003">Fig. 3</figref> is shown.

This will be several in <figref idref="f0003">Fig. 3</figref> not shown optional tools such as a long-term prediction tool, an intensity / coupling tool, a prediction tool, a noise substitution tool, until finally reaching to a middle / side encoder 1012. The center / side encoder 1012 is active when the audio signal to be encoded is a multi-channel signal, that is, a stereo signal having a left channel and a right channel. So far, ie in the processing direction before block 1012 in FIG<figref idref="f0003">Fig. 3</figref> For example, the left and right stereo channels were processed separately, that is, scaled, transformed by the filter bank, or not subjected to TNS processing, etc.

In the middle / side encoder is then first checked whether a middle / side encoding makes sense, that brings a coding gain at all. A middle / side encoding will then bring a coding gain if the left and the right channel are more similar, because then the center channel, that is the sum of the left and the right channel is almost equal to the left or the right channel, apart from the scaling by the factor 1/2, while the page channel has only very small values, since it is equal to the difference between the left and the right channel. Thus, it can be seen that when the left and right channels are approximately equal, the difference is approximately zero or includes only very small values,

The quantizer 1014 is given a allowed perturbation per scale factor band by a psycho-acoustic model 1020. The quantizer operates iteratively, ie it first calls an outer iteration loop, which then calls an inner iteration loop. Generally speaking, first, based on quantizer step size start values, a quantization of a block of values ​​is made at the input of the quantizer 1014. In particular, the inner loop quantizes the MDCT coefficients, consuming a certain number of bits. The outer loop calculates the distortion and modified energy of the coefficients using the scale factor to again invoke an inner loop. This process is iterated until a certain conditional set is met. For each iteration in the outer iteration loop, the signal is reconstructed to compute the perturbation introduced by the quantization and to compare it with the allowable perturbation provided by the psycho-acoustic model 1020. Furthermore, the scale factors are increased from iteration to iteration by one step, for each iteration of the outer iteration loop.

Then, when a situation is reached where the quantization disturbance introduced by the quantization is below the allowed disturbance determined by the psycho-acoustic model, and at the same time bit requirements are met, namely that a maximum bitrate is not exceeded, the iteration, ie the analysis-by-synthesis method is terminated, and the resulting scale factors are encoded as set forth in block 1014 and supplied in coded form to the bitstream formatter 1004 as indicated by the arrow between block 1014 and the block Block 1004 is drawn. The quantized values ​​are then supplied to entropy coder 1016, which typically performs entropy coding using multiple Huffman code tables for different scale factor bands, to transfer the quantized values ​​to a binary format. As is well known, entropy coding in the form of Huffman coding relies on code tables that are created on the basis of expected signal statistics and in which frequently occurring values ​​get shorter code words than more rarely occurring values. The entropy-coded values ​​are then also supplied as actual main information to the bit stream formatter 1004, which then outputs the coded audio signal on the output side in accordance with a specific bit stream syntax. and where common values ​​get shorter code words than less common ones. The entropy-coded values ​​are then also supplied as actual main information to the bit stream formatter 1004, which then outputs the coded audio signal on the output side in accordance with a specific bit stream syntax. and where common values ​​get shorter code words than less common ones. The entropy-coded values ​​are then also supplied as actual main information to the bit stream formatter 1004, which then outputs the coded audio signal on the output side in accordance with a specific bit stream syntax.

The data reduction of audio signals is now a well-known technique that is the subject of a number of international standards (eg ISO / MPEG-1, MPEG-2 AAC, MPEG-4).

Common to the above method is that the input signal by means of a so-called encoder taking advantage of perceptual effects (psychoacoustics, psycho-optics) is brought into a compact, data-reduced representation. For this purpose, a spectral analysis of the signal is usually carried out and the corresponding signal components are quantized taking into account a perceptual model and then coded in a compact manner as so-called bitstream.

In order to estimate, before the actual quantization, how many bits a particular section of the signal to be coded will require, so-called perceptual entropy (PE) can be used. The PE also provides a measure of how difficult it is for the encoder to encode a particular signal or portions thereof.

Decisive for the quality of the estimation is the deviation of the PE from the number of actually required bits.

Further, the perceptual entropy or demand estimate of information units for encoding a signal may be used to estimate whether the signal is transient or stationary, since transient signals also require more bits to encode than more stationary signals. The estimation of a transient property of a signal is used, for example, to determine a window length decision, such as at block 1008 in FIG<figref idref="f0003">Fig. 3</figref> is suggested to perform.

In <figref idref="f0005">Fig. 6</figref> is the Perceptual Entropy calculated according to ISO / IEC IS 13818-7 (MPEG-2 advanced audio coding (AAC)). To calculate this perceptual entropy, ie a bandwise perceptual entropy, the in<figref idref="f0005">Fig. 6</figref> illustrated equation used. In this equation, the parameter pe stands for the perceptual entropy. Furthermore, width (b) stands for the number of spectral coefficients in the respective band b. Further, e (b) is the energy of the signal in this band. Finally, nb (b) is the appropriate masking threshold, or more generally, the allowable disturbance that can be introduced into the signal, for example, by quantization, so that a human listener still hears no or only a negligible disturbance.

The bands may differ from the band division of the psychoacoustic model (block 1020 in <figref idref="f0003">Fig. 3</figref>), or it is the so-called scale factor bands (scfb) used in the quantization. The psychoacoustic masking threshold is the energy value that the quantization error should not exceed.

In the <figref idref="f0005">Fig. 6</figref> The figure shows how well such a Perceptual Entropy works as an estimate of the number of bits needed for encoding. For this purpose, the respective perceptual entropy was plotted as a function of the consumed bits using the example of an AAC coder at different bit rates for each individual block. The test piece used contains a typical mix of music, language and individual instruments.

Ideally, the points would gather along a straight line through the zero point. The extension of the point sequence with the deviations from the ideal line illustrates the inaccurate estimate.

A disadvantage of the in <figref idref="f0005">Fig. 6</figref> The concept shown here is therefore the deviation that manifests itself as resulting, for example, in too great a value for the perceptual entropy, which in turn means that the quantizer is signaled that more bits than actually required are needed. This results in the quantizer being too finely quantized that it does not exploit the amount of allowed disturbance, resulting in a reduced coding gain. On the other hand, if the value for the Perceptual Entropy is determined to be too small, then the quantizer is signaled that fewer bits than actually required are needed to encode the signal. This, in turn, causes the quantizer to be coarsely quantized, which would immediately result in an audible disturbance in the signal unless countermeasures are taken.

To improve the calculation of Perceptual Entropy you could, as in <figref idref="f0006">Fig. 7</figref> is shown, introduce a constant term, such as 1.5, in the logarithmic expression. Then there is already a better result, ie a smaller deviation upwards or downwards, although it can still be seen that the consideration of a constant term in the logarithmic expression reduces the case that the perceptual entropy is too optimistic Bits signaled. On the other hand is off<figref idref="f0006">Fig. 7</figref> however, it can be clearly seen that significantly too many bits are signaled, which leads to the quantizer always becoming too finely quantized, ie that the bit requirement is assumed to be greater than it actually is, which in turn results in a reduced coding gain. The constant in the logarithmic expression is a rough estimate of the bits needed for the page information.

The insertion of a term into the logarithm expression does indeed improve the bandwise perceptual entropy, as in <figref idref="f0005">Fig. 6</figref> is shown, since the bands with a very small distance between the energy and the masking threshold are taken into account, since even for the transmission of zero-quantized spectral coefficients a certain number of bits is necessary.

Another, but very time-consuming computation of Perceptual Entropy is in <figref idref="f0007">Fig. 8</figref> shown. In<figref idref="f0007">Fig. 8</figref> the case is shown in which the perceptual entropy is calculated line by line. The disadvantage, however, lies in the higher computational complexity of the line-by-line calculation. Here, instead of the energy, spectral coefficients X (k) are used, where kOffset (b) designates the first index of band b. If<figref idref="f0007">Fig. 8</figref> With <figref idref="f0006">Fig. 7</figref> is compared, it can be seen clearly in the range between 2000 and 3000 bits, a reduction of the "rashes" upwards. The PE estimate will therefore be more accurate, so not too pessimistic, but rather at the optimum, so that the coding gain in comparison to the in<figref idref="f0005">Fig. 6</figref> and <figref idref="f0006">7</figref> or the number of iterations in the quantizer is reduced.

The disadvantage of the line-by-line calculation of perceptual entropy, however, is the computation time required to calculate the in <figref idref="f0007">Fig. 8</figref> evaluate the equation shown.

While such computational drawbacks do not necessarily matter if the encoder is running on a high-performance PC or a high-performance workstation. On the other hand, it looks quite different when the encoder is housed in a portable device, such as a UMTS mobile phone, which on the one hand needs to be small and cheap, which, on the other hand, has a low power requirement and which, in addition, has to work very fast to handle the Encoding a transmitted via the UMTS connection audio signal or video signal to allow.

The <patcit id="pcit0001" dnum="US2002103637A1"><text>US 2002/103637 A1</text></patcit> discloses a concept for improving the performance of encoding systems employing high frequency reconstruction techniques. For this purpose, an encoding difficulty or a measure of the workload of an encoder is calculated on the encoder side in order to control the crossover frequency which determines up to what frequency a signal is coded with a source coder, the proportion of the signal being above the crossover frequency is encoded by a high frequency reconstruction method. As a measure of the difficulty of encoding a signal, Perceptual Entropy is calculated based on squealing a spectral value and then weighting it with a number equal to the number of lines in the current band divided by the psychoacoustic threshold for it Band is, to then form a logarithm of the result. A summation of all such logarithms in a band then gives the perceptual entropy in this band. Alternatively, a distortion energy at the end of the source coding process can also be calculated by summing the distortion energy in each band and weighting it with a loudness curve.

The object of the present invention is to provide an efficient yet accurate concept for determining an estimate of a need for information units to encode a signal.

This object is achieved by a device according to claim 1, a method according to claim 5 or a computer program according to claim 6.

The present invention is based on the finding that a frequency band-wise calculation of the estimate for a need for information units must be recorded for computing time reasons, however, in order to obtain an accurate determination of the estimated value, the distribution of the energy in the frequency band should be calculated band-by-band is, must be taken into account.

Thus, to a certain extent, the entropy coder following the quantizer is implicitly "involved" in determining the estimate of the demand for information units. The entropy coding makes it possible that a smaller number of bits is required to transmit smaller spectral values ​​than to transmit larger spectral values. The entropy coder is particularly efficient when it is possible to transmit to-zero-quantized spectral values. Since these will typically occur most frequently, the codeword for transmitting a zero-quantized spectral line is the shortest codeword, and the codeword for transmitting an increasingly larger quantized spectral line becomes longer and longer.

It has been found that the band-wise perceptual entropy calculation used in the prior art for determining the estimate of the demand for information units completely ignores the operation of the downstream entropy coder when the distribution of energy in the frequency band deviates from a completely uniform distribution ,

Thus, according to the invention, to reduce the inaccuracies of the band-wise calculation, it is considered how the energy is distributed within a band.

Depending on the implementation, the measure of the distribution of energy in the frequency band can be determined based on the actual amplitudes, or by estimating the frequency lines that are not quantized to zero by the quantizer. This measure, which is also referred to as "nl", where nl stands for "number of active lines", ie for the number of active lines, is preferred for computing efficiency reasons. However, the number of spectral lines quantized to zero or a finer subdivision can also be taken into account, and this estimate becomes more and more accurate as more information from the downstream entropy coder is taken into account. If the entropy coder is based on Huffman codetables,

Depending on the calculation time constraints, however, in the case of a particularly efficient calculation, the measure of the distribution of the energy in the frequency band is carried out by determining the lines still surviving after the quantization, ie the number of active lines.

The present invention is advantageous in that an estimate of a need for information content is determined which is more accurate and more efficient than the prior art.

In addition, the present invention is scalable to various applications because, depending on the desired accuracy of the estimate, more and more characteristics of the entropy coder, but at the cost of increased computation time, can be included in the estimation of the bit demand.

Preferred embodiments of the present invention will be explained below in detail with reference to the attached times. Show it:<dl id="dl0001"><dt>Fig. 1</dt><dd>a block diagram of the device according to the invention for determining an estimated value;</dd><dt>Fig. 2a</dt><dd>a preferred embodiment of the means for calculating a measure of the distribution of energy in the frequency band;</dd><dt>Fig. 2b</dt><dd>a preferred embodiment of the means for calculating the estimate of the need for bits;</dd><dt>Fig. 3</dt><dd>a block diagram of a known audio encoder;</dd><dt>Fig. 4</dt><dd>a schematic diagram for explaining the influence of the energy distribution within a band on the determination of the estimated value;</dd><dt>Fig. 5</dt><dd>a diagram for estimated value calculation according to the present invention;</dd><dt>Fig. 6</dt><dd>a diagram for estimating according to ISO / IEC IS 13818-7 (AAC);</dd><dt>Fig. 7</dt><dd>a diagram for estimated value calculation with constant term;</dd><dt>Fig. 8</dt><dd>a diagram for linear estimation calculation with constant term.</dd></dl>

Hereinafter, referring to <figref idref="f0001">Fig. 1</figref> the device according to the invention for determining an estimate for a need of information units for coding a signal is shown. The signal, which may be an audio and / or a video signal, is input via an input 100.

Preferably, the signal is already present as a spectral representation with spectral values. However, this is not absolutely necessary since some calculations with a time signal can be carried out by appropriate eg bandpass filtering.

The signal is provided to a device 102 for providing a measure of allowable interference to a frequency band of the signal. The allowed disturbance can, for example, by means of a psycho-acoustic model, as shown by<figref idref="f0003">Fig. 3</figref> (Block 1020) has been explained. The device 102 is also operative to also provide a measure of the energy of the signal in the frequency band. The prerequisite for a band-wise calculation is that a frequency band for which an allowable disturbance or a signal energy is specified contains at least two or more spectral lines of the spectral representation of the signal. In typical standardized audio coders, the frequency band will preferably be a scale factor band, since the bit-demand estimate is needed directly by the quantizer to determine whether or not a done quantization satisfies a bit-criterion.

The device 102 is designed to supply both the allowed disturbance nb (b) and the signal energy e (b) of the signal in the band to a device 104 for calculating the demand for bits.

According to the invention, the means 104 for calculating the demand for bits is designed to take into account, in addition to the allowed disturbance and the signal energy, a measure nl (b) for a distribution of the energy in the frequency band, the distribution of the energy in the frequency band of deviates from a completely uniform distribution. The measure for the distribution of the energy is calculated in a device 106, wherein the device 106 requires at least one band, namely the considered frequency band of the audio or video signal either as a bandpass signal or directly as a sequence of spectral lines, for example a spectral analysis of the Bandes to be able to get the measure of the distribution of energies in the frequency band.

Of course, the audio or video signal may be supplied to the device 106 as a time signal, the device 106 then performing band filtering as well as analysis in the band. Alternatively, the audio or video signal supplied to the device 106 may already be in the frequency domain, such as MDCT coefficients, or as a bandpass signal in the filter bank with a smaller bandpass compared to an MDCT filterbank -Filter.

In a preferred embodiment, means 106 for calculating is adapted to take into account current amounts of spectral values ​​in the frequency band to calculate the estimate.

Furthermore, the means for calculating the measure of the distribution of the energy can be designed to determine as a measure of the distribution of energy a number of spectral values ​​whose magnitude is greater than or equal to a predetermined magnitude threshold, or whose magnitude is less than or equal to the magnitude threshold wherein the magnitude threshold is preferably an estimated quantizer level that causes a quantizer to quantize values ​​less than or equal to the quantizer level to zero. In this case, the measure of the energy is the number of active lines, that is, the number of lines that survive after quantization or not equal to zero.

<figref idref="f0002">Fig. 2a</figref> shows a preferred embodiment of the means 106 for calculating the measure of the distribution of energy in the frequency band. The measure of the distribution of energy in the frequency band is in<figref idref="f0002">Fig. 2a</figref> denoted by nl (b). The form factor ffac (b) is already a measure of the distribution of the energy e (b) or eb or en in the frequency band b. As can be seen from block 106, the measure of the spectral distribution nl from the form factor ffac (b) is weighted by the 4th root of the signal energy e (b) divided by the bandwidth width (b) and number of lines, respectively determined in the scale factor band b. In this connection, it should be noted that the shape factor is also an example of a quantity indicating a measure of the distribution of the energies, while nl (b) is an example of a quantity containing an estimate of the number of energies represents lines relevant to quantization.

The form factor ffac (b) is calculated by absolute value formation of a spectral line and subsequent rooting of this spectral line and subsequent summation of the "rooted" amounts of the spectral lines in the band.

<figref idref="f0002">Fig. 2b</figref> shows a preferred embodiment of the means 104 for calculating the estimated value pe, wherein in <figref idref="f0002">Fig. 2b</figref> another case distinction is introduced, namely, when the base 2 logarithm of the energy to allowed disturbance ratio is greater than a constant factor c1 or equal to the constant factor. In this case, the alternative above in block 104 is taken, ie the measure of the spectral distribution n1 is multiplied by the logarithm expression.

If, on the other hand, it is found that the base 2 logarithm from the ratio of the signal energies en and eb to the allowed disturbance is less than the value c1, the lower alternative in block 104 of FIG <figref idref="f0002">Fig. 2b</figref> is used, which additionally has an additive constant c2 and a multiplicative constant c3, which is calculated from the constants c2 and c1.

The following is based on <figref idref="f0004">Fig. 4a and Fig. 4b</figref> the concept of the invention shown. So shows<figref idref="f0004">Fig. 4a</figref> a band with four spectral lines, all of the same size. The energy in this band is thus distributed evenly across the band. On the other hand shows<figref idref="f0004">Fig. 4b</figref> a situation where the energy in the band resides in one spectral line while the other three spectral lines are zero. This in<figref idref="f0004">Fig. 4b</figref> For example, the band shown could be before quantization, or could be obtained after quantization, if the in <figref idref="f0004">Fig. 4b</figref>zeros set spectral lines before quantization are smaller than the first quantizer are erstufe and thus set by the quantizer to zero, so not "survive".

The number of active lines in <figref idref="f0004">Fig. 4b</figref> is thus equal to 1, with the parameter n1 in <figref idref="f0004">Fig. 4b</figref> is calculated to the square root of 2. In contrast, the value nl, ie the measure of the spectral distribution of energy in<figref idref="f0004">Fig. 4a</figref> calculated to 4. This means that the spectral distribution of the energy is more uniform when the measure of the distribution of the spectral energy is greater.

It should be noted that the band-wise calculation of Perceptual Entropy according to the prior art does not detect any difference between the two cases. In particular, no difference is noted when in the two bands that are in<figref idref="f0004">Fig. 4a and 4b</figref> are shown, the same energy is present.

Obviously, however, the in <figref idref="f0004">Fig. 4b</figref> case coded with only one relevant line with fewer bits, since the three zero-set spectral lines can be transmitted very efficiently. Generally speaking, the simpler quantisability of in<figref idref="f0004">Fig. 4b</figref> If so, on the fact that after quantization and lossless coding, smaller values, and in particular values ​​quantized to zero, require fewer bits for transmission.

The invention thus takes into account how the energy is distributed within the band. This is done, as has been done, by replacing the number of lines per band in the known equation (<figref idref="f0005">Fig. 6</figref>) by estimating the number of lines that are nonzero after quantization. This estimate is in<figref idref="f0002">Fig. 2a</figref> shown.

It should also be noted that the in <figref idref="f0002">Fig. 2a</figref> is also required elsewhere in the encoder, for example within the quantization block 1014 to determine the quantization step size. Then, if the form factor is already computed elsewhere, it need not be recalculated for bit estimation, so that the inventive concept of improved estimation of the measure of the required bits requires a minimum of additional computational overhead.

As has already been stated, X (k) is the spectral coefficient to be quantized later, while the variable kOffset (b) designates the first index in band b.

Like it out <figref idref="f0004">Fig. 4a and 4b</figref> is apparent, the spectrum results in <figref idref="f0004">Fig. 4a</figref> a value nl = 4, while the spectrum in <figref idref="f0004">Fig. 4b</figref> gives a value of 1.41. With the help of the form factor, a measure is thus available for the characterization of the spectral field structure within the band.

The new formula for calculating improved band-wise perceptual entropy is thus based on multiplying the measure of the spectral distribution of energy and the logarithmic expression by giving the signal energy e (b) in the numerator and the allowed error in the denominator, as needed a term within the logarithm can be used, as it is already in <figref idref="f0006">Fig. 7</figref> is shown. For example, this term may also be 1.5, but may also be zero, as in FIG<figref idref="f0002">Fig. 2b</figref> shown case, this z. B. can be determined empirically.

At this point be on again <figref idref="f0004">Fig. 5</figref> from which the calculated according to the invention perceptual entropy is apparent, and plotted on the required bits. A higher accuracy of the estimation over the comparison examples in the<figref idref="f0005">Fig. 6</figref>. <figref idref="f0006">7</figref> and <figref idref="f0007">8th</figref> is clearly visible. Also compared to the line-wise calculation, the modified band-wise calculation according to the invention performs at least equally.

Depending on the circumstances, the method according to the invention can be implemented in hardware or in software. The implementation may be on a digital storage medium, in particular a floppy disk or CD with electronically readable control signals, which may interact with a programmable computer system such that the method is performed. In general, the invention thus also consists in a computer program product with a program code stored on a machine-readable carrier for carrying out the method according to the invention, when the computer program product runs on a computer. In other words, the invention can thus be realized as a computer program with a program code for carrying out the method when the computer program runs on a computer.