hardware implementation of data compression ... - INFN Bologna

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Data compression techniques
value of the noise introduced by the preamplification electronics; then
there is a spread among this value due to the RMS of the Gaussian
distribution of the noise.
The noise level introduced by the electronics may vary with time and
with the amount of radiation absorbed: so far a compression algorithm
making use of the zero suppression technique has to allow a tunable
value of the threshold level, in order to accomodate fluctuations or
drifts in the baseline values. Following this indication, the threshold
level used in CARLOS v1 and v2 is completely presettable via software
using the JTAG port.
2.5.2 Transform coding
Transform coding [7] takes as input a data sequence and transforms it
into a sequence in which most part of the information is contained into
a few samples: so far the new sequence can be further compressed using
the other compression algorithms described up to now. The key point
of transform coding is the choice of the transform: this depends on the
features and redundancies of the input data stream to compress. The
algorithm, working on N elements at a time, consists of three steps:
– transform: the input sequence {sn} is split in N-long sequences;
then each block is mapped, using a reversible transformation, into
the sequence {cn}.
– quantization: the transformed sequence {cn} is quantized, i.e. a
number of bits is assigned to each sample depending on the dynamics
of the sequence, compression ratio desired and acceptable
distortion.
– coding: the quantized sequence {cn} is encoded using a binary
encoding technique such as Run Length encoding or the Huffman
coding.
These concepts can be expressed in a mathematical way: given a sequence
in input {sn}, it is divided in N-long blocks and it is mapped