hardware implementation of data compression ... - INFN Bologna

2.5 — Lossy compression techniques
input signal
Figure 2.5: Decomposition of a signal in frequency components
pulse response completely specifies the filter: once we know the impulse
response of the filter, we know the relationship between the input and
the output of the filter. Notice that if the bi are all zero, there the
impulse response will die out after N samples. These filters are called
finite impulse response or FIR filters. In FIR filters Eq. 2.30 reduces
to a convolution operation between the input signal and the filter coefficients.
Filters with the nonzero values for some of the bi are called
infinite response filters or IIR filters.
The basic subband coding works as follows: the source is passed
through a bank of filters (a 3-level filter bank is shown in Fig. 2.6),
called the analysis filter bank which covers the range of frequencies
that make up the source; the outputs of the filters are then subsampled
as in Fig. 2.7. The justification of subsampling is the Nyquist rule and
its generalization, which tells that for perfect reconstruction we only
need twice as many samples per second as the range of frequencies.
This means that it is possible to reduce the number of samples at the
output of the filter as the range of frequencies is less than the range of
frequencies at the input of the filter. The process of reducing the number
of samples is called decimation or downsampling. The amount of
decimation depends on the ratio of the bandwidth of the filter output
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