Copyright (c) 1996 Institute of Electrical and Electronics Engineers. Reprinted, with permission, from the
`IEEE Multimedia Journal, Summer 1995 issue.
`
`This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any
`way imply IEEE endorsement of any of Motorola's or Digital's products or services. Internal or personal
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`from the IEEE by sending a blank email message to info.pub.permissions@ieee.org.
`
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`
`This article was published when the author was with Motorola, Inc. As of October 7, 1996, Davis Pan will
`be working at the Cambridge Research Laboratory of Digital Equipment Corporation in Cambridge,
`Massachusetts.
`
`A Tutorial on MPEG/Audio Compression
`
`Davis Pan
`
`Motorola Inc.
`1301 East Algonquin Road,
`Schaumburg, IL 60196
`
`ABSTRACT
`
`This tutorial covers the theory behind MPEG/audio compression. This algorithm was developed by the Motion Picture Experts
`Group (MPEG), as an International Organization for Standardization (ISO) standard for the high fidelity compression of digital
`audio. The MPEG/audio compression standard is one part of a multiple part standard that addresses the compression of video
`(11172-2), the compression of audio (11172-3), and the synchronization of the audio, video, and related data streams (11172-1)
`to an aggregate bit rate of about 1.5 Mbits/sec. The MPEG/audio standard also can be used for audio-only applications to
`compress high fidelity audio data at much lower bit rates.
`
`While the MPEG/audio compression algorithm is lossy, often it can provide "transparent", perceptually lossless, compression
`even with compression factors of 6-to-1 or more. The algorithm works by exploiting the perceptual properties of the human
`auditory system. This paper also will cover the basics of psychoacoustic modeling and the methods used by the MPEG/audio
`algorithm to compress audio data with least perceptible degradation.
`
`1. INTRODUCTION
`
`This tutorial covers the theory behind MPEG/audio compression. It is written for people with a modest background in digital
`signal processing and does not assume prior experience in audio compression or psychoacoustics. Here the goal is to give a
`broad, preliminary understanding of MPEG/audio compression; many details have been omitted. Wherever possible, this tutorial
`uses figures and illustrative examples to present the intricacies of the algorithm.
`
`The MPEG/audio compression algorithm is the first international standard[1,2] for the digital compression of high-fidelity audio.
`Other audio compression algorithms address speech-only applications or provide only medium-fidelity audio compression
`performance. For example, Code Excited Linear Prediction (CELP)[3] is a speech coding algorithm, while m -law and Adaptive
`Differential Pulse Code Modulation (ADPCM) are relatively simple compression algorithms that can provide medium fidelity
`audio compression. To contrast the complexity of the MPEG/audio algorithm with that of some simpler, generic audio
`compression algorithms, the annex of this paper presents the details of m -law and the ADPCM algorithm adopted by the
`Interactive Multimedia Association.
`
`The MPEG/audio standard is the result of over 3 years of collaborative work by an international committee of high-fidelity audio
`compression experts known as the Motion Picture Experts Group (MPEG/audio). The International Organization for Standards
`and the International Electrotechnical Commission (ISO/IEC) adopted this standard at the end of 1992.
`
`1
`
`Micro Motion 1014
`
`

`
`Although MPEG/audio compression is perfectly suitable for audio-only applications, it is actually one part of a three part
`compression standard. Combined with the other two parts, video and systems, the MPEG standard addresses the compression of
`synchronized video and audio at a total bit rate of about 1.5 Megabits/sec.
`
`The MPEG standard is rigid only where necessary to ensure inter-operability. It mandates the syntax of the coded bitstream,
`defines the decoding process, and provides compliance tests for assessing the accuracy of the decoder[4]. This guarantees that,
`regardless of origin, any fully compliant MPEG/audio decoder will be able to decode any MPEG/audio bitstream with a
`predictable result. A wide acceptance of this standard will permit manufacturers to produce and sell, at reasonable cost, large
`numbers of MPEG/audio codecs.
`
`Where possible, the standard is open to future innovative improvements. Designers are free to try new and different
`implementations of the encoder or decoder within the bounds of the standard. There is especially good potential for diversity in
`the encoder.
`
`1.1 MPEG/audio Features and Applications
`
`MPEG/audio is a generic audio compression standard. Unlike vocal-tract-model coders specially tuned for speech signals, the
`MPEG/audio coder gets its compression without making assumptions about the nature of the audio source. Instead, the coder
`exploits the perceptual limitations of the human auditory system. Much of the compression results from the removal of
`perceptually irrelevant parts of the audio signal. Removal of such parts results in inaudible distortions, thus MPEG/audio can
`compress any signal meant to be heard by the human ear. In keeping with its generic nature, MPEG/audio offers a diverse
`assortment of compression modes:
`
`• The audio sampling rate can be 32, 44.1, or 48 kHz.
`
`• The compressed bitstream can support one or two audio channels in one of 4 possible modes:
`
`1. a monophonic mode for a single audio channel,
`
`2. a dual-monophonic mode for two independent audio channels (this is functionally identical to the stereo mode),
`
`3. a stereo mode for stereo channels with a sharing of bits between the channels, but no joint-stereo coding, and
`
`4. a joint-stereo mode that either takes advantage of the correlations between the stereo channels or the irrelevancy of
`the phase difference between channels, or both.
`
`• The compressed bitstream can have one of several predefined fixed bit rates ranging from 32 to 224 kbits/sec per channel.
`Depending on the audio sampling rate, this translates to compression factors ranging from 2.7 to 24. In addition, the
`standard provides a "free" bit rate mode to support fixed bit rates other than the predefined rates.
`
`• MPEG/audio offers a choice of three independent layers of compression. This provides a wide range of tradeoffs between
`codec complexity and compressed audio quality:
`
`Layer I is the simplest and is best suited for bit rates above 128 kbits/sec per channel. For example, Philips' Digital
`Compact Cassette (DCC)[5] uses Layer I compression at 192 kbits/s per channel.
`
`Layer II has an intermediate complexity and is targeted for bit rates around 128 kbits/s per channel. Possible
`applications for this layer include the coding of audio for Digital Audio Broadcasting (DAB®)[6] , for the storage of
`synchronized video-and-audio sequences on CD-ROM, and the full motion extension of CD-interactive, Video CD.
`
`Layer III is the most complex but offers the best audio quality, particularly for bit rates around 64 kbits/s per channel.
`This layer is well suited for audio transmission over ISDN.
`
`All three layers are simple enough to allow single-chip, real-time decoder implementations.
`
`2
`
`

`
`• The coded bitstream supports an optional Cyclic Redundancy Check (CRC) error detection code.
`
`• MPEG/audio provides a means of including ancillary data within the bitstream.
`
`In addition, the MPEG/audio bitstream makes features such as random access, audio fast forwarding, and audio reverse possible.
`
`2. OVERVIEW
`
`The key to MPEG/audio compression is quantization. Although quantization is lossy, this algorithm can give "transparent",
`perceptually lossless, compression. The MPEG/audio committee conducted extensive subjective listening tests during the
`development of the standard. The tests showed that even with a 6-to-1 compression ratio (stereo, 16 bits/sample, audio sampled
`at 48 kHz compressed to 256 kbits/sec) and under optimal listening conditions, expert listeners were unable to distinguish
`between coded and original audio clips with statistical significance. Furthermore, these clips were specially chosen because they
`are difficult to compress. Reference 7 gives the details of the set up, procedures and results of these tests.
`
`Figure 1 shows block diagrams of the MPEG/audio encoder and decoder. The input audio stream passes through a filter bank
`that divides the input into multiple subbands of frequency. The input audio stream simultaneously passes through a
`psychoacoustic model that determines the ratio of the signal energy to the masking threshold for each subband. The bit or noise
`allocation block uses the signal-to-mask ratios to decide how to apportion the total number of code bits available for the
`quantization of the subband signals to minimize the audibility of the quantization noise. Finally, the last block takes the
`representation of the quantized subband samples and formats this data and side information into a coded bitstream. Ancillary
`data not necessarily related to the audio stream can be inserted within the coded bitstream. The decoder deciphers this bitstream,
`restores the quantized subband values, and reconstructs the audio signal from the subband values.
`
`The following sections explore various aspects of MPEG/audio compression in more detail. The first section covers the time to
`frequency mapping of the polyphase filter bank. The next section covers implementations of the psychoacoustic model followed
`by a more detailed descriptions of the 3 Layers of MPEG/audio compression. This gives enough background to cover a brief
`summary of the different bit (or noise) allocation processes used by the three layers and the joint stereo coding methods. The
`paper finishes with a short description of current MPEG/audio standards work.
`
`2.1 The Polyphase Filter Bank
`
`This section will give some insight into the behavior of the MPEG/audio polyphase filter bank by presenting a detailed
`examination of the encoder's analysis filter bank. A similar analysis applies to the decoder's synthesis filter bank.
`
`The polyphase filter bank is the key component common to all layers of MPEG/audio compression. This filter bank divides the
`audio signal into 32 equal-width frequency subbands. The filters are relatively simple and provide good time resolution with
`reasonable frequency resolution. The design is a good compromise with three notable concessions. First, the equal widths of
`the subbands do not accurately reflect the human auditory system's frequency dependent behavior. The width of a "critical band"
`as a function of frequency is a good indicator of this behavior. Many psychoacoustic effects are consistent with a critical band
`frequency scaling. For example, both the perceived loudness of a signal and its audibility in the presence of a masking signal is
`different for signals within one critical band than for signals that extend over more than one critical band. Figure 2 compares the
`polyphase filter bandwidths with the width of these critical bands. At lower frequencies a single subband covers several critical
`bands. In this circumstance the number of quantizer bits cannot be specifically tuned for the noise masking available for the
`individual critical bands. Instead, the critical band with the least noise masking dictates the number of quantization bits needed
`for the entire subband. Second, the filter bank and its inverse are not lossless transformations. Even without quantization, the
`inverse transformation cannot perfectly recover the original signal. However, by design the error introduced by the filter bank is
`small and inaudible. Finally, adjacent filter bands have a major frequency overlap. A signal at a single frequency can affect two
`adjacent filter bank outputs. Other parts of this paper will cover these issues in more detail.
`
`To understand the polyphase filter bank it is useful to examine its origin. The ISO MPEG/audio standard describes a procedure
`for computing the analysis polyphase filter outputs that is very similar to a method described by Rothweiler[8]. Figure 3 shows a
`structure for a MPEG-encoder-filter bank based on Rothweiler's proposal. For comparison, figure 4 shows the flow diagram
`from the ISO MPEG/audio standard for the same filter bank.
`
`By combining the equations and steps shown by flow diagram, one can derive the following equation for the filter bank outputs:
`
`3
`
`

`
`7
`
`63
`(cid:202) (cid:229)
`st[i] = (cid:229)
`(cid:202) M[i][k] * (C[k+64j] * x[k+64j])
`k=0
`j=0
`
`where:
`
` (1)
`
`i is the subband index and ranges from 0 to 31,
`st[i] is the filter output sample for subband i at time t, where t is an integer multiple of 32 audio sample intervals,
`C[n] is one of 512 coefficients of the analysis window defined in the standard,
`x[n] is an audio input sample read from a 512 sample buffer, and
`(2*i+1)*(k-16)*p
`M[i][k] = cos[
`64
`
`] are the analysis matrix coefficients.
`
`The above equation is partially optimized to reduce the number of computations. Because the function within the parenthesis is
`independent of the value of i, and M[i][k] is independent of j, the 32 filter outputs need only 512 + 32*64 = 2,560 multiplies and
`64*7+32*63 = 2,464 additions, or roughly 80 multiplies and additions per output. Substantially further reductions in multiplies
`and adds are possible with, for example, a fast Discrete Cosine Transform[9,10] or a fast Fourier Transform implementation[11].
`
`Note this filter bank implementation is critically sampled: for every 32 input samples, the filter bank produces 32 output samples.
`In effect, each of the 32 subband filters subsamples its output by 32 to produce only one output sample for every 32 new audio
`samples.
`
`One can manipulate equation (1) into a familiar filter convolution equation:
`
`511
`st[i] = (cid:229)
`(cid:202) x [t-n]*H i[n]
`n=0
`
`where:
`x [t ] is an audio sample at time t, and
`(2*i+1)*(n-16)*p
`] with
`Hi[n] = h[n]*cos[
`64
`h[n]= -C[n], if the integer part of (n/64) is odd,
` = C[n] otherwise, for n = 0 to 511.
`
` (2)
`
`In this form, each subband of the filter bank has its own band-pass filter response, Hi[n]. Although this form is more convenient
`for analysis, it is clearly not an efficient solution: A direct implementation of this equation requires 32*512 = 16,384 multiplies
`and 32*511 = 16,352 additions to compute the 32 filter outputs.
`
`The coefficients, h[n], correspond to the prototype low-pass filter response for the polyphase filter bank. Figure 5 compares a
`plot of h[n] with C[n]. The C[n] used in the partially optimized equation (1) has every odd numbered group of 64 coefficients of
`h[n] negated to compensate for M[i][k]. The cosine term of M[i][k] only ranges from k = 0 to 63 and covers an odd number of
`half cycles whereas the cosine terms of Hi[n] range from n=0 to 511 and cover 8 times the number of half cycles.
`
`The equation for Hi[n] clearly shows that each is a modulation of the prototype response with a cosine term to shift the low pass
`response to the appropriate frequency band, hence these are called polyphase filters. These filters have center frequencies at
`odd multiples of p /(64T) where T is the audio sampling period and each has a nominal bandwidth of p /(32T). As figure 6 shows,
`the prototype filter response does not have a sharp cutoff at its nominal bandwidth. So when the filter outputs are subsampled by
`32, there is a considerable amount of aliasing. The design of the prototype filter, and the inclusion of appropriate phase shifts in
`the cosine terms, results in a complete alias cancellation at the output of the decoder's synthesis filter bank[8,12]. Another
`consequence of using a filter with a wider-than-nominal bandwidth is an overlap in the frequency coverage of adjacent polyphase
`filters. This effect can be detrimental to efficient audio compression because signal energy near nominal subband edges will
`appear in two adjacent polyphase filter outputs. Figure 7 shows how a pure sinusoid tone, which has energy at only one
`frequency, appears at the output of two polyphase filters.
`
`4
`
`

`
`Although the polyphase filter bank is not lossless, any consequent errors are small. Figures 8 and 9 show the composite
`frequency response combining response of the encoder's analysis filter bank with that of the decoder's synthesis filter bank.
`Without quantization of the subband samples, the composite response has a ripple of less than .07 dB.
`
`2.2 Psychoacoustics
`
`The MPEG/audio algorithm compresses the audio data in large part by removing the acoustically irrelevant parts of the audio
`signal. That is, it takes advantage of the human auditory system's inability to hear quantization noise under conditions of
`auditory masking. This masking is a perceptual property of the human auditory system that occurs whenever the presence of a
`strong audio signal makes a temporal or spectral neighborhood of weaker audio signals imperceptible. A variety of
`psychoacoustic experiments corroborate this masking phenomenon[13].
`
`Empirical results also show that the human auditory system has a limited, frequency dependent, resolution. This frequency
`dependency can be expressed in terms of critical band widths which are less than 100 Hz for the lowest audible frequencies and
`more than 4 kHz at the highest. The human auditory system blurs the various signal components within a critical band although
`this system's frequency selectivity is much finer than a critical band.
`
`Because of the human auditory system's frequency-dependent resolving power, the noise masking threshold at any given
`frequency is solely dependent on the signal energy within a limited bandwidth neighborhood of that frequency. Figure 10
`illustrates this property. MPEG/audio works by dividing the audio signal into frequency subbands that approximate critical
`bands, then quantizing each subband according to the audibility of quantization noise within that band. For the most efficient
`compression, each band should be quantized with no more levels than necessary to make the quantization noise inaudible.
`
`2.2.1 The Psychoacoustic Model
`
`The psychoacoustic model analyzes the audio signal and computes the amount of noise masking available as a function of
`frequency [,14,15,16,17]. The masking ability of a given signal component depends on its frequency position and its loudness. The
`encoder uses this information to decide how best to represent the input audio signal with its limited number of code bits. The
`MPEG/audio standard provides two example implementations of the psychoacoustic model. Psychoacoustic model 1 is less
`complex than psychoacoustic model 2 and has more compromises to simplify the calculations. Either model works for any of the
`layers of compression. However, only model 2 includes specific modifications to accommodate Layer III.
`
`There is considerable freedom in the implementation of the psychoacoustic model. The required accuracy of the model is
`dependent on the target compression factor and the intended application. For low levels of compression, where there is a
`generous supply of code bits, a complete bypass of the psychoacoustic model may be adequate for consumer use. In this case,
`the bit allocation process can iteratively assign bits to the subband with the lowest signal-to-noise ratio. For the archiving of
`music, the psychoacoustic model can be made much more stringent[18].
`
`Below is a general outline of the basic steps involved in the psychoacoustic calculations for either model. Differences between
`the two models will be highlighted.
`
`• Time align audio data. There is one psychoacoustic evaluation per frame. The audio data sent to the psychoacoustic
`model must be concurrent with the audio data to be coded. The psychoacoustic model must account for both the delay of
`the audio data through the filter bank and a data offset so that the relevant data is centered within the psychoacoustic
`analysis window. For example, when using psychoacoustic model 1 for Layer I, the delay through the filter bank is 256
`samples and the offset required to center the 384 samples of a Layer I frame in the 512 point analysis window is (512-
`384)/2 = 64 points. The net offset is 320 points to time align the psychoacoustic model data with the filter bank outputs.
`
`• Convert audio to a frequency domain representation. The psychoacoustic model should use a separate, independent, time-
`to-frequency mapping instead of the polyphase filter bank because it needs finer frequency resolution for an accurate
`calculation of the masking thresholds. Both psychoacoustic models use a Fourier transform for this mapping. A standard
`Hann weighting, applied to the audio data before Fourier transformation, conditions the data to reduce the edge effects of
`the transform window.
`
`Psychoacoustic model 1 uses a 512 sample analysis window for Layer I and a 1024 sample window for Layers II and
`III. Because there are only 384 samples in a Layer I frame, a 512 sample window provides adequate coverage. Here
`the smaller window size reduces the computational load. Layer II and III use a 1,152 sample frame size so the 1,024
`
`5
`
`

`
`sample window does not provide complete coverage. While ideally the analysis window should completely cover the
`samples to be coded, a 1,024 sample window is a reasonable compromise. Samples falling outside the analysis window
`generally will not have a major impact on the psychoacoustic evaluation.
`
`Psychoacoustic model 2 uses a 1,024 sample window for all layers. For Layer I, the model centers a frame's 384 audio
`samples in the psychoacoustic window as previously discussed. For Layers II and III, the model computes two 1,024
`point psychoacoustic calculations for each frame. The first calculation centers the first half of the 1,152 samples in the
`analysis window and the second calculation centers the second half. The model combines the results of the two
`calculations by using the higher of the two signal-to-mask ratios for each subband. This in effect selects the lower of
`the two noise masking thresholds for each subband.
`
`• Process spectral values in groupings related to critical band widths. To simplify the psychoacoustic calculations, both
`models process the frequency values in perceptual quanta.
`
`• Separate spectral values into tonal and non-tonal components. Both models identify and separate the tonal and noise-like
`components of the audio signal because the masking abilities of the two types of signal are different.
`
`Psychoacoustic model 1 identifies tonal components based on the local peaks of the audio power spectrum. After
`processing the tonal components, model 1 sums the remaining spectral values into a single non-tonal component per
`critical band. The frequency index of each of these concentrated non-tonal components is the value closest to the
`geometric mean of the enclosing critical band.
`
`Psychoacoustic model 2 never actually separates tonal and non-tonal components. Instead, it computes a tonality index
`as a function of frequency. This index gives a measure of whether the component is more tone-like or noise-like. Model
`2 uses this index to interpolate between pure tone-masking-noise and noise-masking-tone values. The tonality index is
`based on a measure of predictability. Model 2 uses data from the previous two analysis windows to predict, via linear
`extrapolation, the component values for the current window. Tonal components are more predictable and thus will have
`higher tonality indices. Because this process relies on more data, it is more likely to better discriminate between tonal
`and non-tonal components than the model 1 method.
`
`• Apply a spreading function. The masking ability of a given signal spreads across its surrounding critical band. The model
`determines the noise masking thresholds by first applying an empirically determined masking (model 1) or spreading
`function (model 2) to the signal components.
`
`• Set a lower bound for the threshold values. Both models include an empirically determined absolute masking threshold,
`the threshold in quiet. This threshold is the lower bound on the audibility of sound.
`
`• Find the masking threshold for each subband. Both psychoacoustic models calculate the masking thresholds with a higher
`frequency resolution than that provided by the polyphase filter bank. Both models must derive a subband threshold value
`from possibly a multitude of masking thresholds computed for frequencies within that subband.
`
`Model 1 selects the minimum masking threshold within each subband. While this approach is good for the lower
`frequency subbands where the subband is narrow relative to a critical band, it may be inaccurate for the higher
`frequency subbands because critical bands for that frequency range span several subbands. These inaccuracies arise
`because model 1 concentrates all the non-tonal components within each critical band into a single value at a single
`frequency. In effect model 1 converts non-tonal components into a form of tonal component. A subband within a wide
`critical band but far from the concentrated non-tonal component will not get an accurate non-tonal masking assessment.
`This approach is a compromise to reduce the computational loads.
`
`Model 2 selects the minimum of the masking thresholds covered by the subband only where the band is wide relative to
`the critical band in that frequency region. It uses the average of the masking thresholds covered by the subband where
`the band is narrow relative to the critical band. Model 2 is not less accurate for the higher frequency subbands because
`it does not concentrate the non-tonal components.
`
`6
`
`

`
`• Calculate the signal-to-mask ratio. The psychoacoustic model computes the signal-to-mask ratio as the ratio of the signal
`energy within the subband (or, for Layer III , a group of bands) to the minimum masking threshold for that subband. The
`model passes this value to the bit (or noise) allocation section of the encoder.
`
`2.2.1.1 An Example of Psychoacoustic Model Analysis
`
`This section gives an illustrative example of the analysis used by psychoacoustic model 1 and model 2. Figure 11 is a spectral
`plot of the example audio signal to be psychoacoustically analyzed and compressed. This signal consists of a combination of a
`strong, 11,250 Hz, sinusoidal tone with lowpass noise.
`
`2.2.1.1.1 Example for Psychoacoustic Model 2
`
`The processes used by psychoacoustic model 2 are somewhat easier to visualize, so this model will be covered first. Figure 12a
`shows the result, according to psychoacoustic model 2, of transforming the audio signal to the perceptual domain (63, one-third
`critical band, partitions) and then applying the spreading function. Note the shift of the sinusoid peak and the expansion of the
`lowpass noise distribution. The perceptual transformation expands the low frequency region and compresses the higher
`frequency region. Because the spreading function is applied in a perceptual domain, the shape of the spreading function is
`relatively uniform as a function of partition. Figure 13 shows a plot of the spreading functions. Figure 12b shows the tonality
`index for the audio signal as computed by psychoacoustic model 2. Figure 14a shows a plot of the masking threshold as
`computed by the model based on the spread energy and the tonality index. This figure has plots of the masking threshold both
`before and after the incorporation of the threshold in quiet to illustrate its impact. Note the threshold in quiet significantly
`increases the noise masking threshold for the higher frequencies. The human auditory system is much less sensitive in this
`region. Also note how the sinusoid signal increases the masking threshold for the neighboring frequencies. The masking
`threshold is computed in the uniform frequency domain instead of the perceptual domain in preparation for the final step of the
`psychoacoustic model, the calculation of the signal-to-mask ratios (SMR) for each subband. Figure 14b is a plot of these results
`and figure 14c is a frequency plot of a processed audio signal using these SMR’s. In this example the audio compression was
`severe (768 to 64 kbits/sec) so the coder may not necessarily be able to mask all the quantization noise.
`
`2.2.1.1.2 Example for Psychoacoustic Model 1
`
`This example uses the same example audio signal as above. Figure 15a shows the how psychoacoustic model 1 identifies the
`local spectral peaks as tonal and non-tonal components. Figure 15b shows the remaining tonal and non-tonal components after
`the decimation process. This process both removes components that would be below the threshold in quiet and removes the
`weaker tonal components within roughly half a critical band width (0.5 Bark) of a stronger tonal component. Psychoacoustic
`model 1 uses the decimated tonal and non-tonal components to determine the global masking threshold in a subsampled
`frequency domain. This subsampled domain corresponds approximately to a perceptual domain. Figure 15c shows the global
`masking threshold calculated for the example audio signal. Psychoacoustic model 1 selects the minimum global masking
`threshold within each subband to compute the SMR’s. Figure 16a shows the resulting signal-to-mask ratio and figure 16b is a
`frequency plot of the processed audio signal using these SMR’s.
`
`2.3 Layer Coding Options
`
`The MPEG/audio standard has 3 distinct layers for compression. Layer I forms the most basic algorithm while Layer II and
`Layer III are enhancements that use some elements found in Layer I. Each successive layer improves the compression
`performance but at the cost of greater encoder and decoder complexity. Every MPEG/audio bitstream contains periodically
`spaced frame headers to identify the bitstream. Figure 17 gives a pictoral representation of the header syntax. A 2 bit field in the
`MPEG header identifies the layer in use.
`
`2.3.1 Layer I
`
`The Layer I algorithm codes audio in frames of 384 audio samples. It does so by grouping together 12 samples from each of the
`32 subbands, as shown in figure 18. Besides the code for audio data, each frame contains a header, an optional Cyclic
`Redundancy Code (CRC) error check word, and possibly ancillary data. Figure 19a shows the arrangement of this data in a
`Layer I bitstream. The numbers within parentheses give the possible number of bits that can be used to encode each field. Each
`group of 12 samples gets a bit allocation and, if the bit allocation is not zero, a scale factor. The bit allocation tells the decoder
`the number of bits used to represent each sample. For Layer I this allocation can be 0 to 15 bits per subband. The scale factor is
`
`7
`
`

`
`a multiplier that sizes the samples to make full use of the range of the quantizer. Each scale factor has a 6 bit representation.
`The decoder multiplies the decoded quantizer output with the scale factor to recover the quantized subband value. The dynamic
`range of the scalefactors alone is over 120 dB. The combination of the bit allocation and the scale factor provide the potential for
`representing the samples with a dynamic range well over 120 dB. Joint stereo coding slightly alters the representation of left and
`right channel audio samples and will be covered later.
`
`2.3.2 Layer II
`
`The Layer II algorithm is a straightforward enhancement of Layer I. It codes the audio data in larger groups and imposes some
`restrictions on the possible bit allocations for values from the middle and higher subbands. It also represents the bit allocation,
`the scale factor values, and the quantized samples with a more compact code. Layer II gets better audio quality by saving bits in
`these areas so more code bits are available to represent the quantized subband values.
`
`The Layer II encoder forms frames of 1152 samples per audio channel. Whereas Layer I codes data in single groups of 12
`samples for each subband, Layer II codes data in 3 groups of 12 samples for each subband. Figure 18 shows this grouping as
`well. Again discounting stereo redundancy coding, there is one bit allocation and up to three scale factors for each trio of 12
`samples. The encoder uses a different scale factor for each group of 12 samples only if necessary to avoid audible distortion.
`The encoder shares scale factor values among two or all three