Coded data generation or conversion – Digital code to digital code converters
Reexamination Certificate
2002-10-25
2004-06-15
Wamsley, Patrick (Department: 2819)
Coded data generation or conversion
Digital code to digital code converters
C348S421100, C375S240240
Reexamination Certificate
active
06750789
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates in general to the analysis of signals that are coded in arbitrary manner and decoded again, and in particular to the analysis of a decoded signal that has been processed using a coding algorithm that is based on a spectral representation of the original signal.
BACKGROUND OF THE INVENTION AND PRIOR ART
It is generally known to code audio and/or video signals using a specific coding method in order to obtain a coded version of the original signal; the coded version of the original signal basically should differ from the original signal to the effect that the data quantity of the coded signal is smaller than the data quantity of the original signal. In this event, the coding algorithm for obtaining the coded signal from the original signal as well as the decoding algorithm, being in essence the inverted coding algorithm, are referred to as data-reducing coding algorithm.
For data reduction of audio signals, there are various coding algorithms that are subject matter of a number of international standards, such as e.g. MPEG-1, MPEG-2, MPEG-4 or also MPEG-2 AAC (AAC=Advanced Audio Coding), with the latter coding algorithm being described in detail, for example, in international standard ISO/IEC 13818-7.
In the following, reference will be made to
FIG. 7
illustrating a block diagram of an MPEG audio coding method. Such an audio coder typically comprises an audio input
70
for inputting a stream of time-discrete sampling values which are, e.g. PCM sampling values having e.g. a width of 16 bits. In an analysis filter bank
71
, the stream of time-discrete sampling values is divided into coding blocks or frames of sampling values using a corresponding window function, and is then converted to a spectral representation e.g. by a filter bank or by a Fourier transform or a modified Fourier transform, such as e.g. a modified discrete cosine transform (MDCT). At the output of the analysis filter bank
71
, there are thus present consecutive coding blocks or frames of spectral coefficients, with a block of spectral coefficients being the spectrum of a coding block of audio sampling values. Often, a 50% overlap of consecutive coding blocks is employed so that, for each block, a window of e.g. 2048 audio sampling values is observed and 1024 new spectral coefficients are created by such processing.
The time-discrete audio signal at input
70
, moreover, is fed into a psychoacoustic model
72
in order to obtain a data reduction, such that, as is known, the masking threshold of the audio signal is calculated as a function of the frequency in order to carry out, in a block
73
, designated quantizing and coding, a quantization of the spectral coefficients that is dependent upon the masking threshold.
In other words, the quantization of the spectral coefficients is carried out coarsely such that the quantization noise introduced thereby is still below the psychoacoustic masking threshold calculated by the psychoacoustic model
72
, so that this quantization noise is not audible in the ideal case. This procedure has the effect that typically a specific number of spectral coefficients, which are still unequal 0 at the output of the analysis filter bank
71
, are set to 0 after quantization since the psychoacoustic model
72
has determined that these are masked by adjacent spectral coefficients and are therefore inaudible.
Also independently of a psychoacoustic or psychooptic model, each quantizer has a specific quantization step width, with spectral values smaller than the step width being set to zero by the quantization. Depending on the quantizer, there is also the possibility that just values that are clearly smaller than the step width are set to zero, whereas values slightly below the step width are rounded up. In most cases, each quantizer sets at least some values to zero, thereby already achieving a data reduction.
After quantization, there is provided a spectral representation of the coding block of time-discrete sampling values in which the quantization noise should possibly be below the psychoacoustic masking threshold. These spectral values that are quantized in data-reducing manner may then be coded, depending on the coder employed, in loss-free manner using entropy coding, which may be e.g. Huffman coding. Due to this, a stream of code words is obtained, to which is added, in a bit stream multiplexer
74
, side information that is still required by a decoder, such as information concerning the analysis filter bank, information concerning the quantization, such as e.g. scale factors, or side information concerning additional functional blocks. In case of MPEG-2 AAC, such additional functional blocks are, for example, TNS processing, intensity stereo processing, mid/side stereo processing or a prediction from spectrum to spectrum.
At an output
75
of the coder, which is also referred to as bit stream output, the signal coded in accordance with the coding algorithm illustrated in
FIG. 7
is then present in the form of blocks.
With respect to the decoder, the coded signal at the output
75
of the coder shown in
FIG. 7
is fed to a bit stream input
80
of a decoder illustrated in
FIG. 8
which first carries out a bit stream demultiplexing operation in a block
81
, referred to as bit stream demultiplexer, in order to separate the spectral data from the side information. At the output of block
81
, there are again available the code words representing the individual spectral coefficients. Using a corresponding table, the code words are decoded in order to obtain quantized spectral values. These quantized spectral values are then processed in a block
82
designated “inverse quantization” in order to calculate back the quantization introduced in block
73
(FIG.
7
). At the output of block
82
, there are available once more dequantized spectral coefficients which are now transformed to the time domain by means of a synthesis filter bank
83
operating in inverse manner to the analysis filter bank
71
(FIG.
7
), in order to obtain the decoded signal at an audio output
84
.
When considering the coding/decoding concept illustrated in
FIGS. 7 and 8
, it becomes clear that a block-oriented method is involved here in which the block generation is effected by the analysis filter bank block
71
of FIG.
7
and in which the block formation is cancelled again only at the audio output
84
of the decoder illustrated in FIG.
8
.
It becomes clear furthermore that a lossy coding concept is involved here since the decoded signal present at audio output
84
in general contains less information than the original signal present at audio input
70
. By way of the quantizer
73
controlled by the psychoacoustic model
72
, information is removed from the original signal present at audio input
70
, with this information being not added any more in the decoder, but rather being dispensed with. Seen in purely subjective manner, this waiver of information in the ideal case has not led to quality impairments due to the psychoacoustic model
72
that is matched to the properties of the human ear, but has led merely to a desired data compression.
It is to be pointed out here that the coding concept described with reference to FIG.
7
and
FIG. 8
by way of an audio signal is also applied correspondingly to image or video signals in which, instead of the temporal audio signal, a video signal is present and in which the spectral representation is not a spectrum of sound here, but a spectrum of place. As for the rest, video signal compression also involves an analysis filter bank, a psychooptic model, quantization and redundancy coding controlled thereby, with the entire coding/decoding concept taking place blockwise as well.
The decoded signal (in case of the example of
FIG. 8
, the decoded audio signal at audio output
84
) typically is again a stream of time-discrete sampling values based on an underlying coding block raster which, however, is generally not visible in the decoded signal, unless specific precautions are taken.
While the
Brandenburg Karlheinz
Herre Juergen
Schildbach Wolfgang
Schug Michael
Sporer Thomas
Beyer Weaver & Thomas LLP
Fraunhofer-Gesellschaft Zur Foerderung, Der Angewandten Forschun
Wamsley Patrick
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