Lossless Data Compression Using Rice Algorithm Based On Curve Fitting Technique

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1536
LOSSLESS DATA COMPRESSION USING RICE ALGORITHM BASED ON
CURVE FITTING TECHNIQUE
Ginu Thomas1, GK Sadanandan2
1PG Student, ECE Department, Toc-H Institute of Science and Technology, Kochi, India
2Professor, ECE Department, Toc-H Institute of Science and Technology, Kochi, India
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Abstract - Today, with the growing demands of
information storage and data transfer, data compression is
becoming greatly important. Data Compressionisatechnique
which is used to decrease the size of original data. This is very
useful when some huge files have to be transferred over
networks or being stored on a datastoragedevice. Thisproject
aims at the implementation of a lossless data compression
system using the Rice algorithm using the mathematicalcurve
fitting models. The Rice algorithm exploits a set of variable-
length codes to achieve compression. This is done using a two-
stage compressor, namely, a pre-processing stage followed by
an adaptive entropy coder. A function is generated based on
the input data sets that can be used to find points anywhere
along the curve. A comparison between the data compression
of modified Rice algorithm and ordinary Ricealgorithmisalso
presented. The design can be implemented in VHDL using
Modelsim.
Key Words: Fundamental sequence, Adaptive Entropy
Coder, Consultative Committee for Space Applications, Joint
Photographic Experts Group
1.INTRODUCTION
The spread of computing has led to an explosion in
the volume of data to be stored on hard disks and sent over
the Internet. This growth has led to a need for data
compression. Data compression is a process by which a file
may be transformed to another file, such thattheoriginal file
may be fully recovered from the original filewithoutanyloss
of actual information. This process may be useful if one
wants to save the storage space. For example if one wants to
store a 4MB file, it may be preferable to first compress it to a
smaller size to save the storage space. Also these
compressed files are much more easily exchanged over the
internet since they upload and download much faster. Data
compression is a method of encoding rules that allows
substantial reduction in the total number of bits to store or
transmit a file. The more information being dealt with, the
more it costs in terms of storage and transmission costs. In
short, Data Compression is the process of encoding data to
fewer bits than the original representation so that it takes
less storage space and less transmission time while
communicating over a network.
There are mainly lossless and lossy forms of data
compression. A lossy data compression method is one
where the data retrieves after decompression may not be
exactly the same as that of original data. Some examples of
lossy data compression algorithms are JPEG, MP3. Lossless
data compression is a technique that allows the exact
original data to be reconstructed from the compressed data.
This is in contrary to the lossy data compression in which
the exact original data cannot be reconstructed from the
compressed data [8]. The Rice compression algorithmisone
example of a lossless compression algorithm which is
suitable for compression of certain types of data. This paper
is based on a lossless data compression Rice Algorithm as
recommended by the CCSDS for the reduction of required
test data amount.
In this work we propose a strategy for designing
optimum losslessdata compressionsystems.Inthefollowing
sections the algorithm is described, and then the
modification of this algorithm is done. Simulation of both
ordinary and modified algorithm and their comparison is
done.
2. RICE ALGORITHM
The Rice algorithm uses a set of variable-length codes to
achieve compression. Variable length coding is the
compression method in which shorter code words are
assigned to data that are expected to occur with higher
frequency [4].
A block diagram of the Rice algorithm is shown in
fig 1.1. It consists of two main functional parts: the
preprocessor and an adaptive entropy coder. The
preprocessor is used to decorrelate the input sample by
taking the difference between the input data sample and the
previous data sample. Adaptive entropy coder consists of
number of options and each option the total number of bits
is calculated and the coding option that yields the least
number of bits is selected which is our compressed data.

Fig: 2.1 Rice Algorithm
2.1 The Preprocessor
The preprocessing is done by using a unit delay
followed by a prediction error mapper. The preprocessor is
used to decorrelate data samples and subsequently map
them into symbols suitable for the entropy coding stage [6].
2.1.1 Unit delay predictor
One of the simplest predictive coders is a linear first-order
unit-delay which is shown in fig 2.1
Fig: 2..2 Unit Delay Predictor
The input data to the preprocessor, x, is a J-sample block of
n-bit samples:
x = x1, x2, . . . xJ.
The output, Δi, will be the difference between the input data
symbol and the preceding data sample.
2.1.2 Mapper
Based on the predicted value 𝑥𝑖 , the prediction error mapper
converts each prediction error value, Δi, to an n-bit nonnegative
integer, δi, suitable for processing by the entropy coder[5]. The
preprocessor transforms input data into blocks of
preprocessed samples, δi, where:
δi = δ1, δ2, . . . δJ.
The prediction error mapping function is given by
 = minimum (xiˆ– xmin, xmax – xiˆ)
Where Δi is the prediction error produced by the predictor,
xmin and xmax denote theminimumandmaximumvaluesof
any input sample xi.
Table 1.1 illustrates the operation of the prediction error
mapper of 8-bit data samples of values from 0 to 255.
Sample
value ix
Predictor
value xiˆ
Δi  i
δi
38 - - - -
65 38 27 38 54
100 65 35 65 70
123 100 23 100 46
159 123 36 123 72
201 159 42 96 84
220 201 19 54 38
250 220 30 35 60
Table 1.1 Operation of Error Mapper
2.2 Adaptive Entropy Coder
The Adaptive Encoder converts preprocessed
samples, δi, into an encoded bit sequence y. The entropy
coding module is a collection of variable-length codes
operating in parallel on blocks of J preprocessed samples.
For each block, the coding option achieving the best
compression is selected to encode the block. The encoded
block is preceded by an ID bit pattern that identifies the
coding option to the decoder.
The different compression options available are:
 Fundamental sequence encoding
 The split-sample options
 Zero block option
 No compression option
2.2.1 Fundamental Sequence Encoding
For the FS code we recognize that each ' 1' digit signals the
end of a code word, and the number of preceding zeros
identifies which symbol was transmitted. This simple
encoding procedure allows FS codewords to be decoded
without the use of lookup tables [6].

2.2.2 Split Sample option
Most of the options in the entropy coder are called 'split-
sample options'. The kth splitsample option takes a blockofJ
preprocessed data samples, splits off the k least significant
bits (LSB) from each sample and encodes the remaining
higher order bits with a simple FS codeword before
appending the split bits to the encoded FS data stream [6].
Table 2.1 shows some examples of split sample options.
Table 2.1: Example of Split Sample Options
2.2.3 Zero Block Option
The Zero-Block option is selectedwhenoneormore
blocks of preprocessed samples are all zeros.Inthiscase,the
numbers of adjacent all zero preprocessed blocks are
encoded by an FS code word.
2.2.4 No compression option
When none of the above options provides any data
compression on a block, the no compression option is
selected. Under this option, the preprocessedblock ofdata is
transmitted without alteration except for a prefixed
identifier.
3. MODIFIED RICE ALGORITHM USING CURVE
FITTING TECHNIQUE
Curve fitting is the process of a curve or a
mathematical function that best suits to a set of data points.
An equation is produced by curve fitting that can be used to
find points anywhere along the curve. Thus prediction of
next data value (𝑥ˆ𝑖) is possible with this method [5]. It
captures the trend in the data by assigning a single function
across the entire range.
3.1 Polynomial Curve Fitting Method
The general form of a polynomial is given by


j
k
k
k xaaxf
1
0)(
The goal is to identify the coefficients 0a and ka such that
f(x) fits the data well. Add up the length of data points and
this gives the expression of the ‘error’ between data and
fitted line. The curve that provides a minimum error is then
the best curve, using least squares methodm[1]. Thegeneral
expression for any error using the least squaresapproachis:
The best line has minimum error between line and data
points. This is called the least squares approach, since we
minimize the square of the error [1]. Substitute the form of
eq. (2) into general least square error eq. (1), we get
Error=  

j
k
k
k
n
i
i xaay
1
2
0
1
))((
Where n-number of data points, i-current data point, j-order
of the polynomial.
The best suited curve will give minimum error between line
and data points. So the objective is to minimize the eq. (3)
and also find the corresponding values of the
coefficients 0a , ka . To minimizeeq. (3),takethederivativeof
error with respect to each coefficient 0a , ka wherek=1,2….j,
set each value to zero. Representing the equation using a
matrix form as follows as:
















 ji
i
j
i
j
i
xx
xn
...
.........
...
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ja
a
a
a
:
2
1
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=
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

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









j
i
i
i
i
ii
i
y
yx
yx
y
:
2
To know the value of 0a , ka ,solve eq. (4).For that take
















 ji
i
j
i
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xn
...
.........
...

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

ja
a
a
a
:
2
1
0
(1)
(2)
(3)
(4)
A=
X=


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

j
i
i
i
i
ii
i
y
yx
yx
y
:
2
The following solution method can be used:
AX=B
Using built in Mathcad matrix inversion, the coefficients
the values of coefficients a 0a , ka are solved.
>> X = A
1
*B
Finally a polynomial equation that fits for the data points is
obtained. The coefficient values are then quantized; these
values are then implemented in the function whichisused to
predict the next set of values [6].
4. SIMULATION RESULTS AND DISCUSSION
4.1 Rice Algorithm
The first stage is preprocessor which is used to decorrelate
the input sample. The next data value is basedonthecurrent
data value. The Table 4.1 shows the first sage output using
ordinary Rice Algorithm.
Table 4.1 Output obtained during mapping using Rice
algorithm
Fig. 4.1 Output waveform of Rice algorithm showing
compressed data
Simulation result for Rice algorithm is shown in fig 4.1 using
Modelsim. Here the 5thsplit sample option(byte7)ischosen
as the minimum number of bits when compared with other
options used, which is the compressed data.
4.2 Modified Rice Algorithm
In Modified Rice algorithm the next data valueisobtained by
using a curve fitting function based on the function
generated, so that we can compress more data. The table 4.2
shows the output sage of preprocessor in modified Rice
algorithm.
Table 4.2: Output obtained during mapping using modified
Rice algorithm
Fig 4.2 Curve fitted values obtained using modified RICE
algorithm in Matlab.
The next data values obtained during mapping using
modified rice algorithm is shown in fig 4.2. Function values
are obtained through curve fitting method using Matlab.
Fig 4.3 Simulation result in Matlab: Graph showing 8 input
samples and curve fitting function
B =

Fig 4.4 Simulation result in ModelSim: output waveform of
modified rice algorithm showing compressed data
Here the 2nd split sample option (byte4) is chosen as the
minimum number of bits whencomparedwithotheroptions
used, which is the compressed data.
For the above example the number of bits in compressed
data is 30 (Fig 4.4) when modified rice algorithm is used.
Whereas the number of bits in compressed data is 52 (Fig:
4.1) when modified rice algorithm is used. Thus the number
of bits of compressed data reduces when modified rice
algorithm is used.
4. COMPARISON BETWEEN RICE ALGORITHM
AND MODIFIED RICE ALGORITHM
1. In rice algorithm, we predict that the next data
which is similar to that of the current data.Whereas
in modified rice algorithm based on curve fitting, a
function is developed for the given set of inputs [5]
and so the next data value can be predicted
according to the function which results in smaller δ
values. Hence we can compress more data.
2. Higher compression is possible when modifiedRice
algorithm is applied to a set of inputs.
3. In rice algorithm, after compression while sending
the data to the receiver, a reference, (n-1) values of
δ and the ID corresponding to the compression
option used are transmitted. Whereas in modified
rice algorithm the function coefficients, n values of
δ and the ID corresponding to the compression
option are transmitted to the receiver.
5. CONCLUSIONS
In this paper, a comparison between the Rice
algorithm and modified Rice algorithm is done. The basic
idea behind Rice coding is to store as many words as
possible with less bits than in the original representation o
that we can reduce the storage volume. In the case of
ordinary Rice algorithm, the next data value is based on the
current data value but in the case of modified rice algorithm
with curve fitting, a function is generated based on the input
data set, with which upcoming values are predicted. These
predicted values will be small and hence we can compress
more data’s as compared to ordinary Rice algorithm. Thus
later method finds numerous applications in most of the
industries especially in space applications. The simulation
result shows that thedata iscompressedmorewithmodified
rice algorithm.
REFERENCES
[1] Gurley, Numerical Methods Lecture 5 - Curve Fitting
Techniques, Computer Methods
[2] Armein Z. R. Langi, “An FPGA Implementation of a
Simple Lossless Data Compression Coprocessor”, IEEE,
17-19 July 2011
[3] CCSDS (Consultative committeeforspacedata systems),
GREEN BOOK ISSUE-2, December 2006
[4] R. F. Rice and J. R. Plaunt, “Adaptive variable length
coding for efficient compression of spacecrafttelevision
data,” IEEE Trans. Commun. Techno. Vol.COM-19,partI,
pp. 889-897, Dec. 1971.
[5] Pratheeka Paulose, “Implementation Of Modified Rice
Algorithm Based On Curve Fitting Techniques”,
International Journal of Advanced Research in Electrical,
Electronics and Instrumentation Engineering, Vol. 2,
Issue 6, June 2013
[6] Rakhee Sasi, “CCSDS Lossless Data Compression
Algorithm in FPGA for Space Applications” International
Journal of Engineering and Advanced Technology,
Volume-2, pp: 2249 – 8958 ,Issue-6, August 2013
[7] Salvatore Coco, Valentina D’Arrigo,DomenicoGiunta, “A
Rice-Based Lossless Data Compression System For
Space Applications”, pp. 377-381, July 2015
[8] Amarjit Kaur, “A Review on Data Compression
Techniques”, IJARCSSE, Volume 5, Issue 1, January2015
[9] Udaya Kumar H, Madhu B C, “Design and
Implementation of Lossless Data Compression
Coprocessor using FPGA”, IJERT, Vol. 4 Issue 05, May-
2015

Lossless Data Compression Using Rice Algorithm Based On Curve Fitting Technique

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Lossless Data Compression Using Rice Algorithm Based On Curve Fitting Technique