Context-Based Entropy Coding of Spectral Envelope Sample Values

Abstract

To provide an improved concept of spectral envelope encoded sample values for a parametric decoder.SOLUTION: The concept is obtained by combining spectral temporal prediction on the one hand, and context-based entropy coding of prediction residuals on the other hand, while specifically determining a context for the current sample value that depends on the amount of deviation between an already coded / decoded sample value pair of a spectral envelope of a spectral temporal neighborhood of the current sample value. The combination of spectral temporal prediction on the one hand, and context-based entropy coding of prediction residuals by selecting a context depending on the amount of deviation on the other hand, is consistent with the nature of the spectral envelope.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to context-based entropy coding of sample values ​​of the spectral envelope and its use in audio coding / compression. [Background technology]

[0002] For example, as described in [1] and [2], many contemporary state-of-the-art lossy audio coders are based on the MDCT transform and use irrelevance reduction and redundancy reduction to minimize the required bit rate for a given perceptual quality. Irrelevance reduction generally exploits the perceptual limitations of the human auditory system to reduce the representation precision or reduce perceptually irrelevant frequency information. Redundancy reduction is applied to exploit statistical structures or correlations to achieve the smallest compact representation of the remaining data, generally using statistical models associated with entropy coding.

[0003] In particular, parametric coding concepts are used to efficiently code audio content. Using parametric coding, parts of an audio signal, e.g. parts of its spectrogram, are described using parameters, rather than using actual time-domain audio samples etc. For example, parts of the spectrogram of an audio signal can be synthesized at the decoder side with a data stream consisting of parameters, e.g. the spectral envelope, and optionally further parameters that control the synthesis, in order to make the synthesized spectrogram parts fit the transmitted spectral envelope. A novel technique of this kind is Spectral Band Replication (SBR), where a core codec is used to code and transmit the low-frequency components of the audio signal, but the transmitted spectral envelope is used at the decoder side to spectrally shape / form a spectral replica of the reproduction of the low-frequency components of the audio signal in order to synthesize the high-frequency components of the audio signal at the decoder side.

[0004] As outlined above, the spectral envelope within the framework of the coding technique is transmitted in the data stream with some suitable spectro-temporal resolution. In a manner similar to the transmission of the sample values ​​of the spectral envelope, the scaling coefficients for scaling the spectral line coefficients or frequency domain coefficients such as MDCT coefficients are likewise transmitted at some suitable spectro-temporal resolution that is coarser than the original spectral line resolution and coarser for the embodiment in the spectral sense.

[0005] A fixed Huffman coding table can be used to convey information about samples describing the spectral envelope or scaling coefficients or frequency domain coefficients. An improved method is to use context coding, e.g., as described in [2] and [3], where the context used to select the probability distribution for coding the values ​​spans both time and frequency. Individual spectral lines, such as MDCT coefficient values, are actual projections of a composite spectral line, and even when the magnitude of the composite spectral line is constant over time, it can appear somewhat random in nature, but the phase varies from one frame to the next. This requires a very complex scheme of context selection, quantization and mapping for good results, as described in [3].

[0006] In image coding, the contexts used are usually two-dimensional across the x and y axes of the image, as described for example in [4]. In image coding, the values ​​are in the linear or power domain, for example by using gamma adjustment. In addition, a single fixed linear prediction may be used in each context as a plane approximation and a basic edge detection mechanism, and the prediction error may be coded. Parametric Golomb or Golomb-Rice coding may be used to code the prediction error. Run-length coding is additionally used to compensate for the difficulty of directly coding very low entropy signals, for example at less than one bit per sample, using a bit-based coder.

[0007] However, despite the improvements associated with encoding scaling factors and / or the spectral envelope, there remains a need for improved concepts for encoding sample values ​​of the spectral envelope. It is therefore an object of the present invention to provide a concept for encoding spectral values ​​of the spectral envelope.

[0008] This object is achieved by the subject matter of the pending independent claims.

[0009] The embodiment described herein is based on the discovery that an improved concept for coding sample values ​​of the spectral envelope can be obtained by combining, on the one hand, spectro-temporal prediction and, on the other hand, context-based entropy coding of the residuals, in particular determining a context for the current sample value that is dependent on an amount for the deviation between pairs of already coded / decoded sample values ​​of the spectral envelope in the spectro-temporal neighborhood of the current sample value. The combination of the spectro-temporal prediction on the one hand and the context-based entropy coding of the prediction residuals with the selection of the context dependent on the amount of deviation on the other is in harmony with the nature of the spectral envelope. The smoothness of the spectral envelope occurs in a compact prediction residual distribution, so that the spectro-temporal cross-correlation is almost completely removed after the prediction and can be ignored in the context selection for the entropy coding of the prediction result. This in turn reduces the overhead for managing the context. The use of the amount of deviation between already coded / decoded sample values ​​in the spectro-temporal neighborhood of the current sample value, however, still allows the provision of a context adaptability that improves the entropy coding efficiency in a manner that justifies the additional overhead caused by this.

[0010] According to the embodiment described below, linear prediction is combined with the use of difference values ​​as deviation measures, thereby keeping the coding overhead low.

[0011] According to an embodiment, the positions of already coded / decoded sample values ​​used to determine the difference value finally used to select / determine a context are selected such that they are adjacent to each other, spectrally or temporally in line with the current sample value, i.e., they lie along a line parallel to the time or spectral axis, and the signs of the difference values ​​are further taken into account when determining / selecting a context. By this measure, a kind of "trend" in the prediction residual can be taken into account when determining / selecting a context for the current sample value while merely increasing the overhead of managing the context considerably.

[0012] Preferred embodiments of the present application are described below with reference to the drawings in which: [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 shows an overview of the spectral envelope and illustrates possible spectro-temporal neighborhoods for a currently encoded / decoded sample value of the spectral envelope as well as its components from the sample value and possible decoding orders defined between them. [Diagram 2] FIG. 2 shows a block diagram of a context-based entropy encoder for encoding sample values ​​of a spectral envelope according to an embodiment. [Diagram 3] FIG. 3 is a block diagram illustrating a quantization function that may be used in quantizing the deviation amount. [Figure 4] FIG. 4 shows a block diagram of a context-based entropy decoder compatible with the encoder of FIG. [Diagram 5] FIG. 5 shows a block diagram of a context-based entropy coder for encoding sample values ​​of a spectral envelope according to a further embodiment. [Figure 6]FIG. 6 shows a circuit diagram illustrating the arrangement of an interval of entropy coded possible values ​​of the prediction residual in relation to the total interval of possible values ​​of the prediction residual according to an embodiment using escape coding. [Figure 7] FIG. 7 shows a block diagram of a context-based entropy decoder compatible with the encoder of FIG. [Figure 8] FIG. 8 illustrates a possible definition of a spectro-temporal neighborhood using a particular notation. [Figure 9] FIG. 9 shows a block diagram of a parametric audio decoder according to an embodiment. [Figure 10] FIG. 10 illustrates a schematic diagram of a possible implementation variant of the parametric decoder of FIG. 9 by showing the relationship between the frequency interval covered by the spectral envelope on the one hand and the fine structures covering other intervals of the frequency range of the entire audio signal on the other hand. [Figure 11] FIG. 11 shows a block diagram of a speech coder adapted to the parametric speech decoder of FIG. 9 according to the variant of FIG. [Figure 12] FIG. 12 is a block diagram illustrating a modification of the parametric audio decoder of FIG. 9 when supporting IGF (Intelligent Gap Filling). [Figure 13] FIG. 13 shows a schematic diagram illustrating a fine structure spectrogram, i.e. a spectrum from a spectral slice, an IGF filling of the spectrum and its shaping by a spectral envelope according to an embodiment. [Figure 14] FIG. 14 shows a block diagram of a speech coder supporting IGF, which is adapted to the modification of the parametric decoder of FIG. 9 according to FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] As a kind of motivation for the embodiment outlined in the present specification below, which can be applied to the coding of the spectral envelope in general, some ideas leading to the advantageous embodiment outlined below are now presented using Intelligent Gap Filling (IGF) as an example. IGF is a novel method that significantly improves the quality of the coded signal even at very low bit rates. References are referred to below for details. In any case, IGF deals with the fact that important parts of the spectrum in the high frequency region are typically quantized to zero due to insufficient bit allocation. In order to preserve as much as possible in the IGF information in the low frequency region, the fine structure of the higher frequency region is used as a source for adaptively replacing the target regions in the high frequency region that are mostly quantized to zero. An important requirement for achieving good perceptual quality is the matching of the decoded energy envelope of the spectral coefficients with that of the original signal. To achieve this, an average spectral energy is calculated from the spectral coefficients from one or more consecutive AAC scaling coefficient bands. Calculating the average energy using boundaries defined by the scaling factor bands is motivated by the already existing careful alignment of those boundaries to some of the important bands, which are characteristic of human hearing. The average energy is converted to a dB-scale representation using a formula similar to the one for the AAC scaling factors, and is then uniformly quantized. In the IGF, different quantization precisions can optionally be used depending on the required overall bitrate. Since the average energy constitutes an important part of the information generated by the IGF, its efficient representation is of high importance for the overall performance of the IGF.

[0015] Thus, in the IGF, the scaling factor energies describe the spectral envelope. The scaling factor energies (SFE) represent that the spectral values ​​describe the spectral envelope. When decoding the same, the special properties of the SFE can be exploited. In particular, in contrast to [2] and [3], it has been realized that the SFE represents the average value of the MDCT spectral lines, and therefore their values ​​are much more "smooth" and linearly correlated to the average magnitude of the corresponding composite spectral lines. Taking advantage of this situation, the following embodiment uses a combination of the spectral envelope sample value prediction on the one hand and the context-based entropy coding of the prediction residual using a context depending on the amount of deviation of adjacent already coded / decoded sample value pairs of the spectral envelope on the other hand. The use of this combination is particularly suitable for this type of data to be coded, i.e. the spectral envelope.

[0016] To facilitate understanding of the embodiments outlined further below, FIG. 1 illustrates a spectral envelope 10 and its components from sample values ​​12 that sample the spectral envelope 10 of an audio signal at a particular spectrotemporal resolution. In FIG. 1, the sample values ​​12 are exemplarily arranged along a time axis 14 and a spectral axis 16. Each sample value 12 describes or defines the height of the spectral envelope 10 within a corresponding spatiotemporal tile, e.g. covering a particular rectangle, of the spatiotemporal domain of the spectrogram of the audio signal. A sample value is thus an integral value obtained by integrating the spectrogram over its associated spectrotemporal tile. The sample values ​​12 may measure the height or strength of the spectral envelope 10 in terms of energy or some other physical quantity and may be defined in a non-logarithmic or linear domain or in a logarithmic domain, the latter of which may provide an additional effect due to its feature of additionally smoothing the sample values ​​along the axes 14 and 16, respectively.

[0017] As far as the following description is concerned, it should be noted that for the sake of explanation it is only assumed that the sample values ​​12 are regularly arranged spectrally and temporally, i.e. that the corresponding space-time tiles corresponding to the sample values ​​12 cover the frequency bands 18 regularly from the spectrogram of the audio signal, but this regularity is not obligatory. Rather, an irregular sampling of the spectral envelope 10 by the sample values ​​12 can also be used, with each sample value 12 then representing the average height of the spectral envelope 10 within its corresponding space-time tile. The neighborhood definitions outlined further below can nevertheless be transferred to other embodiments of this kind of irregular sampling of the spectral envelope 10. A short statement on this kind of possibility is provided below.

[0018] It should be noted, however, that the above-mentioned spectral envelope may be the subject of coding and decoding for transmission from the coder to the decoder for various reasons. For example, the spectral envelope may be used for scalability purposes, to extend the core coding of the low-frequency band of the audio signal, i.e. to extend the low-frequency band towards higher frequencies, i.e. towards the high-frequency band with respect to the spectral envelope. In that case, for example, the context-based entropy decoder / encoder described below could be part of, for example, an SBR decoder / encoder. Alternatively, the same could be part of an audio coder / decoder using IGF, as already described above. In IGF, the high-frequency part of the audio signal spectrogram is additionally described using spectral values ​​describing the spectral envelope of the high-frequency part of the spectrogram in order to fill the zero-quantized areas of the spectrogram within the high-frequency part using the spectral envelope. More details on this point are described further below.

[0019] FIG. 2 shows a context-based entropy coder for encoding sample values ​​12 of a spectral envelope 10 of an audio signal according to an embodiment of the present application.

[0020] The context-based entropy coder of Fig. 2 is generally indicated with the reference numeral 20 and includes a predictor 22, a context determiner 24, an entropy coder 26 and a residual determiner 28. The context determiner 24 and the predictor 22 have inputs which access the sample values ​​12 of the spectral envelope (Fig. 1). The entropy coder 26 has a control input connected to the output of the context determiner 24 and a data input connected to the output of the residual determiner 28. The residual determiner 28 has two inputs, one of which is connected to the output of the predictor 22 and the other of which provides the residual determiner 28 with access to the sample values ​​12 of the spectral envelope 10. In particular, the residual determiner 28 receives at its input the sample value x currently to be coded, while the context determiner 24 and the predictor 22 receive at their inputs the sample values ​​12 which have already been coded and which are in the spectro-temporal neighbourhood of the current sample value x.

[0021] TIFF0007667199000001.tif115170

[0022] Although, as already outlined above, the sample values ​​12 are assumed to be regularly spaced along the time and spectral axes 14 and 16, this regularity is not mandatory and the definition of neighborhood and the identification of adjacent sample values ​​can be extended to such irregular cases. For example, neighboring sample value "a" can be defined as the neighbor to the upper left corner of the spectro-temporal tile of the current sample along the time axis that temporally precedes the upper left corner. Similar definitions can be used to define other neighbors, e.g. neighbor b to e.

[0023] As will be outlined in more detail below, the predictor 22 may use different subsets of all sample values ​​within the spectro-temporal neighborhood, i.e. subsets of {a, b, c, d, e}, depending on the spectro-temporal position of the current sample value x. Which subset is actually used may depend, for example, on the availability of adjacent sample values ​​within the spectro-temporal neighborhood defined by the set {a, b, c, d, e}. The adjacent sample values ​​a, d and c may for example not be available for the current sample value x that is directly following a random access point, i.e. a point in time that allows the decoder to start decoding, such that reliance on previous parts of the spectral envelope 10 is forbidden. Alternatively, the adjacent sample values ​​b, c and e may not be available for the current sample value x that represents the low-frequency end of the interval 18, such that the position of the respective adjacent sample value falls within the outer interval 18. In any case, the predictor 22 may spectro-temporally predict the current sample value x by linearly combining the sample values ​​already coded within the spectral neighborhood.

[0024] TIFF0007667199000002.tif65170

[0025] TIFF0007667199000003.tif84169

[0026] As an intermediate remark, it should be mentioned that the definition of the spectro-temporal neighborhood may be adapted to the encoding / decoding order in which the context-based entropy coder 20 sequentially codes the sample values ​​12. As shown in FIG. 1, for example, the context-based entropy coder may be configured to sequentially code the sample values ​​12 using a decoding order 30 that traverses the sample values ​​12 at each time instant, time by time, proceeding from the lowest frequency to the highest frequency. In the following, the "time instants" are indicated as "frames", but the time instants may alternatively be called time slots, time units, etc. In any case, when using this type of spectral traversal before a temporal feedforward, the definition of the spectro-temporal neighborhood that extends to the preceding time instant and towards the lower frequencies provides the greatest feasibility that the corresponding sample values ​​have already been encoded / decoded and can be utilized. In this case, the values ​​in the neighborhood are always already encoded / decoded, if they exist, but this may be different for other neighborhood and decoding order pairs. Naturally, the decoder uses the same decoding order 30.

[0027] The sample values ​​12 may represent the spectral envelope 10 in the logarithmic domain, as already indicated above. In particular, the spectral values ​​12 may already have been quantized up to integer values ​​using a logarithmic quantization function. Due to the quantization, the deviation amount determined in the context determiner 24 may therefore already be an integer in nature. This is the case, for example, when using a difference as the deviation amount. Regardless of the inherent integer nature of the deviation amount measured in the context determiner 24, the context determiner 24 may subject the deviation amount to a quantization and determine the context using the quantized amount. In particular, as outlined below, the quantization function used by the context determiner 24 may be constant for values ​​of the deviation amount outside a predetermined interval, for example a predetermined interval including zero.

[0028] 3 exemplarily shows such a quantization function 32 which maps the unquantized deviation magnitude to a quantized deviation magnitude in the just-mentioned predetermined interval 34 which in this example extends from -2.5 to 2.5, with unquantized deviation magnitude values ​​larger than the interval always being mapped to a quantized deviation magnitude value of 3 and unquantized deviation magnitude values ​​smaller than the interval 34 always being mapped to a quantized deviation magnitude value of -3. Thus, only seven contexts should be distinguished and supported in the context-based entropy coder. In the embodiment outlined below, as just illustrated, the length of the interval 34 is 5 and the cardinality of the set of possible values ​​of the spectral envelope sample values ​​is 2. n (e.g. = 128), i.e. greater than 16 times the length of the interval. As will be explained later, for the escape coding used, the range of possible values ​​of the spectral envelope sample values ​​is [0;2 n [where n is 2 n+1 is an integer selected to be smaller than the base of the codable values ​​of the prediction residual values, which is 311, according to a particular embodiment described below.

[0029] TIFF0007667199000004.tif109169

[0030] For completeness, FIG. 2 shows that a quantizer 36 may be connected before the input of the residual determiner 28 from which the current sample value x arrives to obtain the current sample value x, e.g. as outlined above, with a logarithmic quantization function already applied to the unquantized sample value x.

[0031] FIG. 4 shows a context-based entropy decoder according to an embodiment, which matches the context-based entropy encoder of FIG.

[0032] TIFF0007667199000005.tif115169

[0033] The entropy decoder 46 reverses the entropy coding performed by the entropy encoder 26. That is, the entropy decoder also manages a number of contexts, and for the current sample value x, uses the context selected by the context determiner 44, each context having an associated corresponding probability distribution that assigns to each possible value of r the same particular probability r as that selected by the context determiner 24 for the entropy encoder 26.

[0034] When using arithmetic coding, the entropy decoder 46, for example, reverses the interval subdivision sequence of the entropy encoder 26. The internal state of the entropy decoder 46 is, for example, defined by the probability interval width of the current interval, and the offset value indicates the subinterval from the current probability interval to which the actual value of r for the current sample value x corresponds. The entropy decoder 46 uses the arriving arithmetic coded bit stream output by the entropy encoder 26 to update the probability interval and the offset value, for example by a renormalization process, and obtains the actual value of r by checking the offset value to see which subinterval the same falls into.

[0035] As already mentioned above, it may be useful to restrict the entropy coding of the residual values ​​onto some small subinterval of possible values ​​of the prediction residual r. Figure 5 shows a modification of the context-based entropy coder of Figure 2 to achieve this. In addition to the elements shown in Figure 2, the context entropy coder of Figure 5 consists of a control connected between the residual determiner 28 and the entropy coder 26, i.e., a control 60, as well as an escape coding handler 62 controlled via the control 60.

[0036] TIFF0007667199000006.tif130170

[0037] In the case of an initial prediction residual r lying within the interval 68, the control 60 causes the entropy coder 26 to directly entropy code this initial prediction residual r. No special measures are to be taken. However, if r, as provided by the residual determiner 28, lies outside the interval 68, an escape coding procedure is initiated by the control 60. In particular, the immediately neighboring values ​​immediately adjacent to the interval boundaries 70 and 72 of the interval 68 may, according to one embodiment, belong to the symbol alphabet of the entropy coder 26 and function as the escape code itself. That is, as indicated by brace 74, the symbol alphabet of the entropy encoder 26 includes all values ​​in the interval 68 and its immediately adjacent values ​​below and above the interval 68, and for an initial prediction residual r that is less than the lower limit 70 of the interval 68, the control 60 simply decreases the value to be entropy coded up to the largest alphabet value 76 that is immediately adjacent to the upper limit 72 of the interval 68 for residual values ​​r that are greater than the upper limit 72 of the interval 68, and sends to the entropy encoder 26 the smallest alphabet value 78 that is immediately adjacent to the lower limit 70 of the interval 68 if the initial prediction residual r is less than the lower limit of the interval 68.

[0038] TIFF0007667199000007.tif134169

[0039] Clearly, escape coding is less complicated than coding of normal prediction residuals lying within the interval 68. Context adaptation is not used, for example. Rather, coding of the coded values ​​in the escape case can be performed simply by writing the binary representation for values ​​such as |r| and even x directly. However, the interval 68 is preferably chosen such that the escape procedure is statistically unlikely to occur and merely represents a statistical "outlier" of the sample value x.

[0040] Figure 7 shows a variant of the context-based entropy decoder of Figure 4, which corresponds or matches the entropy encoder of Figure 5. As with the entropy encoder of Figure 5, the context-based entropy decoder of Figure 7 differs from the one shown in Figure 4 in that a control 71 is connected between the entropy decoder 46 on the one hand and the combiner 48 on the other hand, and the entropy decoder of Figure 7 further includes an escape code handler 73. As with Figure 5, the control 71 performs a check 74 whether the entropy decoded value r output by the entropy decoder 46 lies within the interval 68 or corresponds to some escape code. If the latter situation applies, the escape code handler 73 is triggered by the control 71 to extract from the data stream also carrying the entropy-coded data stream entropy-decoded by the entropy decoder 46, said code being inserted by the escape code handler 62, for example, a binary representation of sufficient bit length to represent the real prediction residual r in a self-sufficient manner independent of the escape code indicated by the entropy-decoded value r, or in a manner dependent on the real escape code that the entropy-decoded value r assumes as already explained in connection with Fig. 6. For example, the escape code handler 73, upon reading a binary representation of a value from the data stream, adds the same to the absolute value of the escape code, i.e. the absolute value of the upper or lower bound, respectively, and uses the sign of the respective bound, i.e. a plus sign for the upper bound and a minus sign for the lower bound, as the sign of the read value. Conditional coding may be used. That is, if the entropy decoded value r output by the entropy decoder 46 falls outside the interval 68, the escape code handler 73 may first read, for example, a p-bit absolute value from the data stream, p-1. Otherwise, the entropy decoded value r is updated by adding the p-bit absolute value to the entropy decoded value r if the escape code is upper bound 72, and by subtracting the p-bit absolute value from the entropy decoded value r if the escape code is lower bound 70. However, if the p-bit absolute value is less than 2 p If it is -1, then another q-bit magnitude is read from the bitstream, and if the escape code has an upper bound of 72, then the entropy decoded value r is the q-bit magnitude +2. p The entropy is updated by adding -1 to the entropy decoded value r, and if the escape code is lower bound 70, the p-bit absolute value +2 is added to the entropy decoded value r. p It is updated by subtracting -1.

[0041] However, Figure 7 also shows another variant. According to this variant, in the case of escape codes, the escape code procedure implemented by the escape code handlers 62 and 72 directly encodes the complete sample value x, so that the estimate is more than necessary. For example, n A bit representation may then be sufficient to indicate the value of x.

[0042] Note that, as a precaution only, other ways of implementing escape coding are possible with these alternative embodiments as well, by not entropy decoding anything for spectral values ​​whose prediction residuals exceed or lie outside interval 68. For example, for each syntax element, a flag may be transmitted indicating whether the same is coded using entropy coding or whether escape coding is used. Then, for each sample value, the flag indicates the selected method of coding.

[0043] In the following, specific embodiments for realizing said embodiment are described. In particular, the specific embodiments presented below illustrate how to deal with the above-mentioned difficulty in obtaining certain previously encoded / decoded sample values ​​in the spectro-temporal neighborhood. Furthermore, specific examples are given for setting the range 66 of possible values, the interval 68, the quantization function 32, the range 34, etc. Later, it is described that specific embodiments can be used in conjunction with IGFs. However, it should be noted that the description presented below can easily be transferred to other cases in which the temporal grid on which the sample values ​​of the spectral envelope are arranged is defined by other time units than frames, such as, for example, groups of QMF slots, and the spectral resolution is similarly defined by a subgrouping of the subbands into spectro-temporal tiles.

[0044] Let us denote by t (time) the frame number across time and by f (frequency) the position of each sample value in the spectral envelope across the scale factor (or scale factors). The sample values ​​are called SFE values ​​in the following. We want to encode the values ​​of x using the information already available from already decoded frames at positions (t-1), (t-2), …, and from the current frame at position (t), at frequencies (f-1), (f-2), …. The situation is again represented in FIG. 8.

[0045] For independent frames we set t=0. An independent frame is a frame that is suitable as a random access point for the decoding entity. It thus represents the time instant at which random access to the decoding is possible at the decoding side. As far as the spectral axis 16 is concerned, the first SFE 12 associated with the lowest frequency has f=0. In FIG. 8, the neighborhood in time and frequency used to calculate the context (available at both the encoder and the decoder) is as in the case of a, b, c, d and e in FIG. 1.

[0046] TIFF0007667199000008.tif15169

[0047] TIFF0007667199000009.tif103130

[0048] TIFF0007667199000010.tif98170

[0049] TIFF0007667199000011.tif67169

[0050] TIFF0007667199000012.tif54169

[0051] With respect to the following figures, various possibilities are described as to how the above mentioned context-based entropy coder / decoder can be incorporated into the respective audio decoder / encoder. Fig. 9 shows a parametric decoder 80 which can be advantageously incorporated, for example, into a context-based entropy decoder 40 according to any of the above outlined embodiments. The parametric decoder 80 comprises, besides the context-based entropy decoder 40, a fine structure determiner 82 and a spectral shaper 84. Optionally, the parametric decoder 80 comprises an inverse transformer 86. The context-based entropy decoder 40 receives an entropy-coded data stream 88, which has been coded by any of the above outlined embodiments of a context-based entropy coder, as outlined above. The data stream 88 therefore has a spectral envelope coded therein. The context-based entropy decoder 40 decodes, in the manner outlined above, sample values ​​of the spectral envelope of the audio signal which the parametric decoder 80 is to reproduce. The fine structure determiner 82 is configured to determine the fine structure of the spectrogram of this audio signal. For this purpose, the fine structure determiner 82 may receive information from outside, for example from other parts of the data stream also consisting of the data stream 88. Further variants are described below. In other variants, however, the fine structure determiner 82 may determine the fine structure alone, using a probability or pseudo-stochastic process. The spectral shaper 84 is then configured to shape the fine structure by the spectral envelope, as defined by the spectral values ​​decoded by the context-based entropy decoder 40. In other words, the inputs of the spectral shaper 84 are connected to the outputs of the context-based entropy decoder 40 and the fine structure determiner 82, respectively, on the one hand to receive the spectral envelope from the same and on the other hand to receive the fine structure of the spectrogram of the audio signal, and the spectral shaper 84 outputs at its output the fine structure of the spectrogram shaped by the spectral envelope.The inverse transformer 86 may perform an inverse transform on the shaped fine structure to output at its output a reconstruction of the audio signal.

[0052] In particular, the fine determiner 82 may be configured to determine the fine structure of the spectrogram using at least one of the following: artificial random noise generation using spectral prediction and / or spectral entropy context derivation, spectral reconstruction and spectral line-by-line decoding. The first two possibilities are described with reference to Fig. 10. Fig. 10 illustrates the possibility that the spectral envelope 10 decoded by the context-based entropy decoder 40 is associated with a frequency interval 18 forming a higher frequency extension of a low-frequency interval 90, i.e. the interval 18 extends the lower-frequency interval 90 to higher frequencies, i.e. the interval 18 borders the interval 19 on the higher frequency side of the latter. Fig. 10 thus illustrates the possibility that the audio signal to be reproduced by the parametric decoder 80 in fact covers a frequency interval 92 in which the interval 18 merely represents the higher frequency part of the entire frequency interval 92. As shown in Fig. 9, the parametric decoder 80 may additionally include a low-frequency decoder 94 configured to decode, for example, a low-frequency data stream 96 accompanying the data stream 88 to obtain at its output a low-frequency band version of the audio signal. The spectrogram of this low-frequency version is represented in Fig. 10 using the reference sign 98. Taken together, this frequency version 98 of the audio signal and the fine structure shaped within the interval 18 results in the audio signal reproduction of the complete frequency interval 92, i.e., its spectrogram throughout the complete frequency interval 92. As indicated by the dotted line in Fig. 9, the inverse transformer 86 may perform an inverse transform on the complete interval 92. In this framework, the fine structure determiner 82 may receive the low-frequency version 98 from the decoder 94 in the time domain or in the frequency domain. In the first case, the fine structure determiner 82 may perform a transformation of the received low-frequency version into the spectral domain to obtain a spectrogram 98 and to obtain the fine structure to be shaped by the spectral shaper 84 with the spectral envelope provided by the context-based entropy decoder 40 using spectral reconstruction, as illustrated with arrow 100.However, as already outlined above, the fine structure determiner 82 cannot even receive a low-frequency version of the audio signal from the LF decoder 94 and generate the fine structure merely using a stochastic or pseudo-stochastic process.

[0053] A corresponding parametric coder, which is adapted to the parametric decoder according to figures 9 and 10, is represented in figure 11. The parametric coder of figure 11 comprises a frequency crossover 110 receiving an audio signal 112 to be encoded, a high frequency band coder 114 and a low frequency band coder 116. The frequency crossover 110 decomposes the inbound audio signal 112 into two components, a first signal 118 corresponding to a high pass filtered version of the inbound audio signal 112 and a low frequency signal 120 corresponding to a low pass filtered version of the inbound audio signal 112, the frequency bands covered by the high frequency signal 118 and the low frequency signal 120 being adjacent to each other at some crossover frequencies (compare 122 in figure 10). The low frequency band coder 116 receives the low frequency signal 120 and codes it into a low frequency data stream, i.e. 96, and the high frequency band coder 114 calculates sample values ​​describing the spectral envelope of the high frequency signal 118 within the high frequency section 18. The high frequency band coder 114 is also equipped with the above-mentioned context-based entropy coder for coding these sample values ​​of the spectral envelope. The low frequency band coder 116 may for example be a transform coder, and the spectrotemporal resolution with which it codes the transform or spectrogram of the low frequency signal 120 may be greater than the spectrotemporal resolution with which the sample values ​​12 resolve the spectral envelope of the high frequency signal 118. The high frequency band coder 114 therefore outputs, inter alia, a data stream 88. As indicated by dotted line 124 in FIG. 11, the low frequency band encoder 116 may output information to the high frequency band encoder 114, for example to control the high frequency band encoder 114 regarding this generation of sample values ​​describing the spectral envelope, or at least regarding the selection of the spectro-temporal resolution at which the sample values ​​sample the spectral envelope.

[0054] Fig. 12 shows another possibility of realizing the parametric decoder 80 of Fig. 9 and in particular the fine structure determiner 82. In particular, according to the embodiment of Fig. 12, the fine structure determiner 82 itself receives the data stream and determines on its basis the fine structure of the audio signal spectrogram using a spectral line-by-spectral decoding using spectral prediction and / or spectral entropy-context derivation. That is to say, the fine structure determiner 82 itself recovers from the data stream a fine structure, for example in the form of a spectrogram consisting of a time sequence of the spectrum of the lapped transform. However, in the case of Fig. 12, the fine structure thus determined by the fine structure determiner 82 relates to the first frequency interval 130 and coincides with a complete frequency interval of the audio signal, i.e. 92.

[0055] In the embodiment of Fig. 12, the frequency interval 18 to which the spectral envelope 10 pertains completely overlaps with the interval 130. In particular, the interval 18 forms the high-frequency part of the interval 130. For example, many of the spectral lines within the spectrogram 132 recovered by the fine structure determiner 82 and covering the frequency interval 130 are quantized to zero, especially within the high-frequency part 18. Nevertheless, in order to reproduce the audio signal with high quality, even within the high-frequency part 18, at a reasonable bit rate, the parametric decoder 80 exploits the spectral envelope 10. The spectral values ​​12 of the spectral envelope 10 describe the spectral envelope of the audio signal within the high-frequency part 18 with a spectrotemporal resolution coarser than the spectrotemporal resolution of the spectrogram 132 decoded by the fine structure determiner 82. For example, the spectrotemporal resolution of the spectral envelope 10 is coarser in spectral terms, i.e. its spectral resolution is coarser than the spectral line precision of the fine structure 132. As mentioned above, spectrally, the sample values ​​12 of the spectral envelope 10 may describe the spectral envelope 10, for example, into frequency bands 134 into which the spectral lines of the spectrogram 132 are grouped due to bandwise scaling of the scaling factors of the spectral line coefficients.

[0056] The spectral shaper 84 may then use the sample values ​​12 to fill in the spectral lines within the spectral lines or spectro-time tiles corresponding to each sample value 12 using mechanisms such as spectral regrowth or artificial noise generation to adjust the fine structure level or energy occurring within each spectro-time tile / scaling factor set according to the corresponding sample values ​​describing the spectral envelope. See, for example, FIG. 13. FIG. 13 illustrates a spectrum from a spectrogram 132 corresponding to one frame or time, e.g., time 136 in FIG. 12. The spectrum is illustrated using reference numeral 140. As illustrated in FIG. 13, some portions 142 thereof are quantized to zero. FIG. 13 illustrates the subdivision of the spectral lines of the spectrum 140 into high frequency portions 18 and scaling factor bands indicated by braces. Using "x" and "b" and "e", FIG. 13 illustrates that three sample values ​​12 describe the spectral envelope within the high frequency portions 18 of time 136 - one for each scaling factor band. Within each scaling factor band corresponding to these sample values ​​e, b and x, the fine structure determiner 82 generates a fine structure within at least the zero quantized portion 142 of the spectrum 140, as indicated by the hatched area 144, for example by spectral reconstruction from the lower frequency portion 146 of the complete frequency interval 130, and adjusts the energy resulting from the spectrum by scaling the artificial fine structure 144 in response to or by using the sample values ​​e, b and x.Interestingly, there are non-zero quantized portions 148 of spectrum 140 within the scaling factor band of the mid or high frequency portion 18, and therefore, using intelligent gap filling according to FIG. 12, it is possible to place peaks within spectrum 140 even in the high frequency portion 18 of the complete frequency interval 130 with spectral line resolution and at any spectral line position, and nevertheless, there is an opportunity to fill the zero quantized portions 142 with sample values ​​x, b and e to shape the fine structure inserted within these zero quantized portions 142.

[0057] Finally, Fig. 14 shows a possible parametric coder for feeding the parametric decoder of Fig. 9, when implemented according to the description of Figs. 12 and 13. In particular, in that case, the parametric coder may include a transformer 150 configured to spectrally decompose the inbound audio signal 152 into a complete spectrogram covering the complete frequency interval 130. A lapped transform with a variable transform length may be used. A spectral line coder 154 codes this spectrogram with a spectral line resolution. To this end, the spectral line coder 154 receives both the high frequency part 18 and the remaining low frequency part from the transformer 150, such that both parts cover the complete frequency interval 130 without gaps and without overlap. A parametric high frequency coder 156 simply receives the high frequency part 18 of the spectrogram 132 from the transformer 150 and generates at least the data stream 88, i.e. sample values ​​describing the spectral envelope within the high frequency part 18.

[0058] That is, according to the embodiment of Fig. 12-14, the spectrogram 132 of the audio signal is coded by the spectral line coder 154 into the data stream 158. The spectral line coder 154 may thus code one spectral line value per spectral line of the complete interval 130 per time or frame 136. Small boxes 160 in Fig. 12 indicate these spectral line values. Along the spectral axis 16, the spectral lines may be classified into scaling factor bands. In other words, the frequency interval 16 may be subdivided into scaling factor bands consisting of groups of spectral lines. The spectral line coder 154 may select a scaling factor for each scaling factor band within each time in order to scale the quantized spectral line values ​​160 coded via the data stream 158. With a spectrotemporal resolution that is at least coarser than the spectrotemporal grid defined by the times and spectral lines on which the spectral line values ​​160 are regularly located and that may coincide with the raster defined by the scaling factor resolution, the parametric high-frequency coder 156 describes the spectral envelope within the high-frequency portion 18. Interestingly, the non-zero quantized spectral line values ​​160 are scaled by the scaling factor of the scaling factor band they fall into and can be scattered anywhere within the high frequency portion 18, in the spectral line resolution, and therefore they cause a high frequency synthesis at the decoding side within the spectral shaper 84 using sample values ​​describing the spectral envelope within the high frequency portion, such that the fine structure determiner 82 and the spectral shaper 84 for example restrict their fine structure synthesis and shaping within the high frequency portion 18 of the spectrogram 132 to the zero quantized portion 142. In the end, a very effective compromise is made between the bit rate spent on the one hand and the obtainable quality on the other hand.

[0059] As indicated by the dashed arrow in FIG. 14 , shown at 164, the spectral line coder 154 may inform the parametric high-frequency coder 156 regarding, for example, a reconstructable version of the spectrogram 132 as reconstructable from the data stream 158, and the parametric high-frequency coder 156 uses this information to control the generation of the sample values ​​12 and / or the spectrotemporal resolution of the representation of the spectral envelope 10, for example.

[0060] To summarise the above, the above embodiments exploit the special properties of the sample values ​​of the spectral envelope. Here, in contrast to [2] and [3], such sample values ​​represent the average values ​​of the spectral lines. In all the embodiments outlined above, the transformation may use the MDCT, and therefore the inverse MDCT may be used for all inverse transformations. In any case, such sample values ​​of the spectral envelope are much more "smooth" and linearly related to the average values ​​of the corresponding composite spectral lines. In addition, according to at least some of the above embodiments, the sample values ​​of the spectral envelope, hereafter called SFE values, are in fact in the dB domain or more generally in the logarithmic domain, which is a logarithmic representation. This further improves the "smoothness" compared to linear or power domain values ​​for the spectral lines. For example, in AAC, the power exponent is 0.75. In contrast to [4], in at least some embodiments, the spectral envelope sample values ​​are in the logarithmic domain, and the characteristics and structure of the coding distribution are significantly different (depending on its magnitude, one logarithmic domain value generally maps to an exponentially increasing number of linear domain values). Thus, at least some of the above-mentioned embodiments utilize a logarithmic representation in quantizing the context (a smaller number of contexts generally exists) and in coding the tail of the distribution in each context (the tail of each distribution is wider). In contrast to [2], based on the same data as used in calculating the quantized context, some of the above-mentioned embodiments further use a fixed or adaptive linear prediction in each context. While still obtaining optimal performance, this method helps to significantly reduce the number of contexts. In contrast to, for example, [4], in at least some of the embodiments, linear prediction in the logarithmic domain has a significantly different use and importance. For example, it is possible to perfectly predict both the constant energy spectral region and also the fade-in and fade-out spectral regions of the signal.In contrast to [4], some of the above-mentioned embodiments use arithmetic coding, which allows optimal coding of any distribution using information extracted from a representative training data set. In contrast to [2], which also uses arithmetic coding, according to said embodiments, prediction error values ​​are coded rather than original values. Furthermore, in said embodiments, bit-plane coding does not need to be used. Bit-plane coding, however, requires several arithmetic coding steps per integer value. In comparison, according to said embodiments, each sample value of the spectral envelope can be coded / decoded within a range including one step, including the optional use of escape coding values ​​outside the center of the total sample value distribution, as described above, which is very fast.

[0061] To briefly summarize an embodiment of a parameter decoder supporting IGF again as described above with respect to Figures 9, 12 and 13, according to this embodiment the fine structure determiner 82 is configured to use a spectral line-by-line decoding using spectral prediction and / or spectral entropy context derivation to derive the fine structure 132 of the spectrogram of the audio signal in the first frequency interval 130, i.e. the complete frequency interval. The frequency-line-by-line decoding refers to the fact that the fine structure determiner 82 receives spectral line values ​​160 from the data stream that are spectrally arranged at the spectral line pitch, thereby forming a spectrum 136 for each time instant corresponding to the respective time portion. The use of spectral prediction may for example include a differential encoding of these spectral line values ​​along the spectral axis 16, i.e. only the difference with respect to the immediately spectrally preceding spectral line value is decoded from the data stream and added to this preceding value. Spectral entropy-context derivation may refer to the fact that the context for entropy decoding of each spectral line value 160 may depend on already decoded spectral line values ​​in the spectro-temporal neighborhood or at least in the spectral neighborhood of the currently decoded spectral line value 160, i.e. may be additively selected based on already decoded spectral line values. To fill the zero-quantized portion 142 of the fine structure, the fine structure determiner 82 may use artificial random noise generation and / or spectral regrowth. The fine structure determiner 82 performs this simply in the second frequency interval 18, which may for example be limited to the high-frequency part of the total frequency interval 130. The spectrally regrowth part may for example be obtained from the remaining frequency portion 146. The spectral shaper then performs a shaping of the fine structure thus obtained according to the spectral envelope described by the sample values ​​12 in the zero-quantized portion. In particular, the contribution of the non-zero-quantized part of the fine structure in the interval 18 to the result of the fine structure after shaping is independent of the actual spectral envelope 10.This means that either the artificial random noise generation and / or spectral regrowth, i.e. filling, is entirely restricted to the zero quantized portions 142, so that in the final fine structure spectrum only the portions 142 are filled by the artificial random noise generation and / or spectral regrowth using the spectral envelope shaping, and their remaining non-zero contributions 148 are interspersed between the portions 142, or all the artificial random noise generation and / or spectral regrowth are alternated, i.e. by forming the resulting synthesized fine structure by the spectral envelope 10, each synthesized fine structure being placed on the portion 148 in an additional manner. However, even in that case, the contribution of the original decoded fine structure as the non-zero quantized portions 148 is maintained.

[0062] With respect to the embodiment of Figs. 12-14, it should finally be noted that the IGF (Intelligent Gap Filling) procedure or concept described with respect to these figures significantly improves the quality of the encoded signal even at very low bit rates, where significant parts of the spectrum in the high frequency region 18 are typically quantized to zero due to insufficient bit allocation. In order to preserve as much as possible the fine structure of the higher frequency region 18, the IGF information, the low frequency region is used as a source to adaptively replace the target region of the high frequency region, i.e. the region 142, which is mostly quantized to zero. A key requirement to achieve good perceptual quality is the matching of the decoded energy envelope of the spectral coefficients with that of the original signal. To achieve this, an average spectral energy is calculated over the spectral coefficients from one or more consecutive AAC scaling coefficient bands. The resulting values ​​are sample values ​​12 describing the spectral envelope. Calculating the average using the boundaries defined by the scaling coefficient bands is motivated by the existing careful tuning of those boundaries to some of the critical bands, which are characteristic of human hearing. As mentioned above, the average energy can be converted to a logarithmic, e.g. dB scale, representation, e.g. using a formula that can be similar to the one already known for the AAC scaling factors and uniformly quantized. In the IGF, different quantization precisions can be optionally used depending on the total bit rate required. The average energy constitutes an important part of the information generated by the IGF, so its efficient representation in the data stream 88 is crucial for the overall performance of the IGF concept.

[0063] Although some aspects have been described in the context of an apparatus, it will be apparent that these aspects also represent a description of a corresponding method, where a block or apparatus corresponds to a method step or feature of a method step. Similarly, aspects that are also described in the context of a method step represent a description of a corresponding block or item or feature of a corresponding apparatus. Some or all of the method steps may be performed by (or using) a hardware apparatus, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such an apparatus.

[0064] Depending on specific implementation requirements, the embodiments of the present invention can be implemented in hardware or in software. The embodiments can be implemented using a digital storage medium having electronically readable control signals stored thereon, such as a floppy disk, hard disk, DVD, Blu-Ray, CD, ROM, PROM, EPROM, EEPROM or FLASH memory, which cooperates (or can cooperate) with a programmable computer system so that the respective methods are executed. Thus, the digital storage medium may be computer readable.

[0065] Some embodiments according to the present invention include a data carrier having electronically readable control signals that can cooperate with a programmable computer system to carry out one of the methods described herein.

[0066] Typically, embodiments of the present invention may be implemented as a computer program product having program code which operates to perform one of the methods when the computer program product runs on a computer. The program code may for example be stored on a machine readable carrier.

[0067] Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.

[0068] In other words, an embodiment of the inventive method is, therefore, a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

[0069] A further embodiment of the inventive method is therefore a data carrier (or digital storage medium or computer readable medium) comprising the computer program recorded thereon for performing one of the methods described herein. The data carrier, digital storage medium or recording medium is typically tangible and / or non-transitory.

[0070] A further embodiment of the inventive method is, therefore, a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the sequence of signals may for example be adapted to be transferred via a data communication connection, for example the Internet.

[0071] A further embodiment comprises a processing means, for example a computer, or a programmable logic device, configured to or adapted to perform one of the methods described herein.

[0072] A further embodiment comprises a computer having installed thereon the computer program for performing one of the methods described herein.

[0073] Further embodiments according to the present invention include an apparatus or system configured to transfer (e.g. electronically or optically) to a receiver a computer program for performing one of the methods described herein. The receiver may be, for example, a computer, a mobile device, a memory device, etc. The apparatus or system may include, for example, a file server for transferring the computer program to the receiver.

[0074] In some embodiments, a programmable logic device (e.g., a field programmable gate array) may be used to perform some or all of the functions of the methods described herein. In some embodiments, a field programmable gate array may cooperate with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by any hardware device.

[0075] The above described embodiments are merely illustrative for the principles of the present invention. It is understood that modifications and variations of the apparatus and details described herein will be apparent to those skilled in the art. It is therefore intended to be limited only by the scope of the appended claims and not by the specific details shown in the specification and the description of the embodiments.

[0076] References [1] International Standard ISO / IEC 14496-3:2005, Information technology - Coding of audio-visual objects - Part 3: Audio, 2005. [2] International Standard ISO / IEC 23003-3:2012, Information technology - MPEG audio technologies - Part 3: Unified Speech and Audio Coding, 2012. [3] B. Edler and N. Meine: Improved Quantization and Lossless Coding for Subband Audio Coding, AES 118th Convention, May 2005. [4] M.J. Weinberger and G. Seroussi: The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS, 1999. Available online at http: / / www.hpl.hp.com / research / info_theory / loco / HPL-98-193R1.pdf

Claims

1. a context-based entropy decoder for decoding sample values ​​of a spectral envelope of an audio signal; a fine structure determiner configured to receive spectral line values ​​from a spectrally arranged data stream at the spectral line pitch and to determine a fine structure of a spectrogram of the audio signal; a spectral shaper configured to shape the microstructure according to the spectral envelope; A parametric decoder comprising: the fine structure determiner is configured to derive the fine structure of the spectrogram of the audio signal in a first frequency interval using spectral line-by-spectral decoding with spectral prediction and / or spectral entropy context derivation, to find zero-quantized parts of the fine structure in a second frequency interval overlapping the first frequency interval, and to apply artificial random noise generation and / or spectral regrowth to the zero-quantized parts, The context-based entropy decoder comprises: spectro-temporal predicting a current sample value of the spectral envelope to obtain an estimate of the current sample value; determining a context for the current sample value depending on a magnitude of deviation between a pair of already decoded sample values ​​of the spectral envelope in a spectro-temporal neighborhood of the current sample value; entropy decoding a prediction residual value of the current sample value using the determined context; Combining the estimate and the prediction residual value to obtain the current sample value The parametric decoder is configured as follows:

2. The parametric decoder of claim 1 , wherein the context-based entropy decoder is further configured to perform spectro-temporal prediction by linear prediction.

3. 2. The parametric decoder of claim 1, wherein the context-based entropy decoder is further configured to use a signed difference between the pair of already decoded sample values ​​of the spectral envelope in the spectro-temporal neighborhood of the current sample value to measure the deviation.

4. the context-based entropy decoder is further configured to determine the context for the current sample value in dependence on a first magnitude of deviation between a first pair of already decoded sample values ​​of the spectral envelope in the spectro-temporal neighborhood of the current sample value and a second magnitude of deviation between a second pair of already decoded sample values ​​of the spectral envelope in the spectro-temporal neighborhood of the current sample value; 2. The parametric decoder of claim 1, wherein the first pair are spectrally adjacent to one another and the second pair are temporally adjacent to one another.

5. 5. The parametric decoder of claim 4, wherein the context-based entropy decoder is further configured to spectro-temporally predict the current sample value of the spectral envelope by linearly combining the already decoded sample values ​​of the first and second pairs.

6. 6. The parametric decoder of claim 5, wherein the context-based entropy decoder is further configured to set coefficients of the linear combinations such that the coefficients are the same for different contexts if a bit rate at which the audio signal is encoded is greater than a predefined threshold, and such that the coefficients are set individually for the different contexts if the bit rate is less than the predefined threshold.

7. 2. The parametric decoder of claim 1, wherein the context-based entropy decoder is further configured to, when decoding the sample values ​​of the spectral envelope, sequentially decode the sample values ​​using a decoding order that goes through the sample values ​​at each instant in time in a direction from lowest frequency to highest frequency at each instant in time.

8. 2. The parametric decoder of claim 1, wherein the context-based entropy decoder is further configured to, in determining the context, quantize the magnitude of the deviation and use the quantized magnitude to determine the context.

9. 9. The parametric decoder of claim 8, wherein the context-based entropy decoder is further configured to use a quantization function during the quantization of the magnitude of the deviation that is constant for values ​​of the magnitude of the deviation outside a predefined interval, the predefined interval including zero.

10. 10. The parametric decoder of claim 9, wherein the values ​​of the spectral envelope are expressed as integers and the length of the predetermined interval is less than or equal to 1 / 16 of the number of representable states of the integer representation of the values ​​of the spectral envelope.

11. The parametric decoder of claim 1 , wherein the context-based entropy decoder is further configured to transition the current sample value derived from the combination from a logarithmic domain to a linear domain.

12. 2. The parametric decoder of claim 1, wherein the context-based entropy decoder manages a number of contexts, each associated with a probability distribution that assigns a respective probability to each possible value of the prediction residual value, and wherein the context-based entropy decoder is further configured to use a set of context-specific probability distributions when entropy decoding the prediction residual values, the set of probability distributions being constant during sequential decoding of the sample values ​​along a decoding order and sequential decoding of the sample values ​​of a spectral envelope.

13. 2. The parametric decoder of claim 1, wherein the context-based entropy decoder is further configured to use an escape encoding mechanism when entropy decoding the prediction residual values ​​if the prediction residual values ​​are outside a predetermined range of values.

14. 14. The parametric decoder of claim 13, wherein the sample values ​​of the spectral envelope are expressed as integers, the prediction residual values ​​are expressed as integers, and the absolute values ​​of the interval boundaries of the predefined value range are less than or equal to 1 / 8 of the number of representable states of the prediction residual values.

15. - decoding sample values ​​of a spectral envelope of the audio signal using context-based entropy decoding; receiving spectral line values ​​from a data stream spectrally arranged within a spectral row pitch to determine a fine structure of a spectrogram of the audio signal; shaping the microstructure according to the spectral envelope; - deriving the fine structure of the spectrogram of the audio signal in a first frequency interval using spectral line-by-spectral decoding with spectral prediction and / or spectral entropy context derivation; finding zero-quantized portions of the fine structure in a second frequency interval overlapping the first frequency interval and applying artificial random noise generation and / or spectral regrowth to the zero-quantized portions; A parametric decoding method comprising: The step of decoding sample values ​​of the spectral envelope of the audio signal comprises: - spectro-temporal predicting a current sample value of said spectral envelope to obtain an estimate of said current sample value; determining a context for the current sample value depending on the magnitude of deviation between a pair of already decoded sample values ​​of the spectral envelope in a spectro-temporal neighborhood of the current sample value; entropy decoding a prediction residual value of the current sample value using the determined context; combining the estimate and the prediction residual value to obtain the current sample value; A parametric decoding method comprising:

16. The microstructure determiner includes: Artificial random noise generation, Spectral regrowth, and Spectral line-by-line decoding using spectral prediction and / or spectral entropy context derivation The parametric decoder of claim 1 , configured to determine the fine structure of the spectrogram using at least one of:

17. 2. The parametric decoder of claim 1, further comprising a low-frequency section decoder configured to decode lower frequency sections of a spectrogram of the audio signal, the context-based entropy decoder, the fine structure determiner and the spectral shaper being configured such that the shaping of the fine structure according to the spectral envelope is performed within a spectral high-frequency extension of the low-frequency section.

18. The low frequency section decoder comprises: Line-by-line decoding using spectral prediction and / or spectral entropy context derivation, or Spectral decomposition of decoded time-domain low-frequency speech signals 20. The parametric decoder of claim 17, configured to determine the fine structure of the spectrogram using

19. 2. The parametric decoder of claim 1, wherein the fine structure determiner is configured to derive the fine structure of the spectrogram of the audio signal using spectral line-by-spectral decoding with spectral prediction and / or spectral entropy context derivation.

20. A non-transitory computer readable medium storing a computer program having a program code for performing the method according to claim 15 when the computer program is run on a computer.

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