Dynamic information storage or retrieval – With servo positioning of transducer assembly over track... – Optical servo system
Reexamination Certificate
2000-06-30
2001-06-12
Hindi, Nabil (Department: 2651)
Dynamic information storage or retrieval
With servo positioning of transducer assembly over track...
Optical servo system
C369S059130
Reexamination Certificate
active
06246645
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to an information encoding method for encoding the information, an associated information decoding apparatus and an information recording medium having the encoded information recorded thereon.
2. Description of the Related Art
As what can record the encoded acoustic information or the encoded speech information, referred to herein as audio signals, an information recording medium, such as a magneto-optical disc, has so far been proposed. A variety of high-efficiency encoding techniques exist for encoding audio or speech signals. Examples of these techniques include so-called transform coding as a blocking frequency spectrum splitting system and a so-called sub-band coding system (SBC) as a non-blocking frequency spectrum splitting system. In the transform coding, audio signals on the time axis are blocked every pre-set time interval, the blocked time-domain signals are transformed into signals on the frequency axis, and the resulting frequency-domain signals are encoded from band to band. In the sub-band coding system, the audio signals on the time axis are split into plural frequency bands and encoded without blocking. In a combination of the sub-band coding system and the transform coding system, the audio signals on the time axis are split into plural frequency bands by the sub-band coding system, and the resulting band-based signals are transformed into frequency-domain signals by orthogonal transform for encoding.
As band-splitting filters used in the sub-band coding system, there is a so-called quadrature mirror filter (QMF) discussed in R. E. Crochiere, “Digital Coding of Speech in Sub-bands”, Bell Syst. Tech. J., Vol. 55, No. 8, 1976. This QMF filter divides the frequency spectrum in two bands of equal bandwidths. With the QMF filter, so-called aliasing is not produced on subsequent synthesis of the band-split signals. The technique of splitting the frequency spectrum into equal frequency bands is discussed in Joseph H. Rothweiler, Polyphase Quadrature Filters—A New Subband Coding Technique”, ICASSP 83 BOSTON. With the polyphase quadrature filter, the signal can be split at a time into plural frequency bands of equal bandwidths.
Among the techniques for orthogonal transform, there is known such a technique in which the input audio signal is split into frames of a predetermined time duration and the resulting frames are processed by discrete Fourier transform (DFT), discrete cosine transform (DCT) or modified DCT (MDCT) to convert the signals from the time axis to the frequency axis. Discussions of a MDCT may be found in J. P. Princen and A. B. Bradley, “Subband/Transform Coding Using Filter Bank Based on Time Domain Aliasing Cancellation”, ICASSP 1987.
If the above-mentioned DFT or DCT is used as a method for orthogonal transform of waveform signals, using a time block made up of M samples, M independent real-number data are obtained. For reducing the junction distortion between these time blocks, M
1
sample data each are usually overlapped between both neighboring time blocks. Thus, in DFT or DCT, M real-number data are obtained on an average for (M-M
1
) sample data, so that these M real-number data are subsequently quantized and encoded.
Conversely, if the above-mentioned MDCT is used as the method for method for orthogonal transform, M independent real-number data are obtained from 2M samples obtained on overlapping M sample data between two neighboring time blocks. That is, if MDCT is used, M real-number data are obtained for M sample data on an average, these M real-number data being then quantized and encoded. In the decoding apparatus, waveform elements obtained on inverse transform in each block are summed together with interference to re-construct the waveform signals.
Meanwhile, if the time block for orthogonal transform is lengthened, the frequency resolution is increased, with the result that the signal energy is concentrated in specified spectral signal components. Thus, with the MDCT in which orthogonal transform is carried out using a long time block obtained on overlapping one-half sample data between both neighboring time blocks, and in which the number of the spectral signal components is not increased as compared the number of the original time-domain sample data, a higher encoding efficiency can be realized than if the DFT or DCT is used. Morever, if neighboring time blocks are overlapped with each other with a sufficiently long overlap, junction distortion between time blocks of waveform signals can be reduced.
By quantizing signal components split from band to band by a filter or orthogonal transform, it becomes possible to control the band subjected to quantization noise, thus enabling encoding with perceptually higher encoding efficiency by exploiting masking effects. By normalizing respective sample data with the maximum value of the absolute values of the signal components in each band prior to quantization, the encoding efficiency can be improved further.
As the band splitting width used for quantizing the signal components resulting from splitting of the frequency spectrum of the audio signals, the band width taking into account the psychoacoustic characteristics of the human being is preferably used. That is, it is preferred to divide the frequency spectrum of the audio signals into a plurality of, for example, 25, critical bands. The width of the critical bands increases with increasing frequency. In encoding the band-based data in such case, bits are fixedly or adaptively allocated among the various critical bands. For example, when applying adaptive bit allocation to the special coefficient data resulting from a MDCT, the spectra coefficient data generated by the MDCT within each of the critical bands is quantized using an adaptively allocated number of bits. The following two techniques are known as the bit allocation technique.
In R. Zelinsky and P. Noll, “Adaptive transform Coding of Speech Signals”, IEEE Transactions of Acoustics, Speech and Signal processing”, vol. ASSP-25, August 1977, bit allocation is carried out on the basis of the amplitude of the signal in each critical band. This technique produces a flat quantization spectrum And minimizes noise energy, but the noise level perceived by the listener is not optimum because the technique does not exploit the psychoacoustic masking effect.
In M. A. Krassener, “The Critical Band Coder—Digital Encoding of the Perceptual Requirements of the Auditory System”, MIT, ICASSP 1980, there is described a technique in which the psychoacoustic masking effect is used to determine a fixed bit allocation that produces the necessary bit allocation for each critical band. However, with this technique, since the bit allocation is fixed, non-optimum results are obtained even for a strongly tonal signal such as a sine wave.
For overcoming this problem, it has been proposed to divide the bits that may be used for bit allocation into a fixed pattern allocation fixed for each band or each small block subdivided from the band and a bit allocation portion dependent on the amplitude of the signal in each block. The division ratio is set depending on a signal related to the input signal such that the division ratio for the fixed allocation pattern portion becomes higher the smoother the pattern of the signal spectrum.
With this method, if the audio signal has high energy concentration in a specified spectral signal component, as in the case of a sine wave, abundant bits are allocated to a block containing the signal spectral component for significantly improving the signal-to-noise ratio as a whole. In general, the hearing sense of the human being is highly sensitive to a signal having sharp spectral signal components, so that, if the signal-to-noise ratio is improved by using this method, not only the numerical values as measured can be improved, but also the audio signal as heard may be improved in quality.
Various other bit allocation methods have been proposed and the perceptual models have become refined, such that, if th
Hindi Nabil
Sonnenschein Nath & Rosenthal
Sony Corporation
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