Lossless Image Compression for Transmitting Over Low Bandwidth Line

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 9, September 2011

Lossless Image Compression For
Transmitting Over Low Bandwidth Line

G. Murugan, Research Dr. E. Kannan, Supervisor S. Arun , ECE Dept.
Scholar , Singhania University ,Singhania University and Asst.Professor Veltech High
& Sri Venkateswara College Dean Academic Veltech Engg college,Chennai email-
of Engg , Thiruvallur University yesarun001@yahoo.com

Abstract solution, as, on the one hand, it provides significantly higher
The aim of this paper is to develop an effective loss less
compression gains vis-à-vis lossless algorithms, and on the
algorithm technique to convert original image into a compressed one. other hand it provides guaranteed bounds on the nature of loss
Here we are using a lossless algorithm technique in order to convert introduced by compression.
original image into compressed one. Without changing the clarity of Another way to deal with the lossy-lossless dilemma
the original image. Lossless image compression is a class of image
compression algorithms that allows the exact original image to be faced in applications such as medical imaging and remote
reconstructed from the compressed data. sensing is to use a successively refindable compression
technique that provides a bit stream that leads to a progressive
We present a compression technique that provides reconstruction of the image. Using wavelets, for example, one
progressive transmission as well as lossless and near-lossless can obtain an embedded bit stream from which various levels
compression in a single framework. The proposed technique
produces a bit stream that results in a progressive and ultimately
of rate and distortion can be obtained. In fact with reversible
lossless reconstruction of an image similar to what one can obtain integer wavelets, one gets a progressive reconstruction
with a reversible wavelet codec. In addition, the proposed scheme capability all the way to lossless recovery of the original. Such
provides near-lossless reconstruction with respect to a given bound techniques have been explored for potential use in tele-
after decoding of each layer of the successively refineable bit stream. radiology where a physician typically requests portions of an
We formulate the image data compression problem as one of image at increased quality (including lossless reconstruction)
successively refining the probability density function (pdf) estimate of while accepting initial renderings and unimportant portions at
each pixel. Experimental results for both lossless and near-lossless lower quality, and thus reducing the overall bandwidth
cases indicate that the proposed compression scheme, that requirements. In fact, the new still image compression
innovatively combines lossless, near-lossless and progressive coding
attributes, gives competitive performance in comparison to state-of-
standard, JPEG 2000, provides such features in its extended
the-art compression schemes. form [2].
In this paper, we present a compression technique
that incorporates the above two desirable characteristics,
1.INTRODUCTION namely, near-lossless compression and progressive refinement
Lossless or reversible compression refers to from lossy to lossless reconstruction. In other words, the
compression techniques in which the reconstructed data proposed technique produces a bit stream that results in a
exactly matches the original. Near-lossless compression progressive reconstruction of the image similar to what one
denotes compression methods, which give quantitative bounds can obtain with a reversible wavelet codec. In addition, our
on the nature of the loss that is introduced. Such compression scheme provides near-lossless (and lossless) reconstruction
techniques provide the guarantee that no pixel difference with respect to a given bound after each layer of the
between the original and the compressed image is above a successively refinable bit stream is decoded. Note, however
given value [1]. Both lossless and near-lossless compression that these bounds need to be set at compression time and
find potential applications in remote sensing, medical and cannot be changed during decompression. The compression
space imaging, and multispectral image archiving. In these performance provided by the proposed technique is
applications the volume of the data would call for lossy comparable to the best-known lossless and near-lossless
compression for practical storage or transmission. However, techniques proposed in the literature. It should be noted that to
the necessity to preserve the validity and precision of data for the best knowledge of the authors, this is the first technique
subsequent reconnaissance diagnosis operations, forensic reported in the literature that provides lossless and near-
analysis, as well as scientific or clinical measurements, often lossless compression as well as progressive reconstruction all
imposes strict constraints on the reconstruction error. In such in a single framework.
situations near-lossless compression becomes a viable 2. METHODOLOGY

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2.1COMPRESSION TECHNIQUES or dropping some of the chrominance information in the
image.
LOSSLESS COMPRESSION
Transform coding. This is the most commonly used
Where data is compressed and can be reconstituted
method. A Fourier-related transform such as DCT or
(uncompressed) without loss of detail or information. These
the wavelet transform are applied, followed
are referred to as bit-preserving or reversible compression
by quantization and entropy coding.
systems also [11].
Fractal compression.
LOSSY COMPRESSION
Where the aim is to obtain the best possible fidelity for a 2.3COMPRESSION
given bit-rate or minimizing the bit-rate to achieve a given The process of coding that will effectively reduce the
fidelity measure. Video and audio compression techniques are total number of bits needed to represent certain information.
most suited to this form of compression [12].
STORAG DECODE
If an image is compressed it clearly needs to be E OR R
uncompressed (decoded) before it can ENCOD NETWOR
viewed/listened to. Some processing of data may be
ER KS (DECOM
possible in encoded form however.
INPUT (COMPR PRESSIO
Lossless compression frequently involves some form ESSION) N)
of entropy encoding and are based in information
theoretic techniques
Lossy compression use source encoding techniques
that may involve transform encoding, differential
encoding or vector quantisation Fig.1. a general data compression scheme

Image compression may be lossy or lossless. Lossless
compression is preferred for archival purposes and often for
medical imaging, technical drawings, clip art, or comics. This
is because lossy compression methods, especially when used
at low bit rates, introduce compression artifacts. Lossy
methods are especially suitable for natural images such as
photographs in applications where minor (sometimes
imperceptible) loss of fidelity is acceptable to achieve a
substantial reduction in bit rate. The lossy compression that
produces imperceptible differences may be called visually Fig.2 lossy image compressionresult result
lossless.
2.2METHODS FOR LOSSLESS IMAGE
COMPRESSION ARE
Run-length encoding – used as default method
in PCX and as one of possible in BMP, TGA, TIFF
DPCM and Predictive Coding
Entropy encoding
Adaptive dictionary algorithms such as LZW – used
in GIF and TIFF
Deflation – used in PNG, MNG, and TIFF
Chain codes

2.3METHODS FOR LOSSY COMPRESSION
Reducing the color space to the most common colors
in the image. The selected colors are specified in the color
palette in the header of the compressed image. Each pixel
just references the index of a color in the color palette.
This method can be combined with dithering to
avoid posterization. Fig. 3 lossless image comparison ratio
Chroma sub sampling. This takes advantage of the
fact that the human eye perceives spatial changes of
brightness more sharply than those of color, by averaging

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Divide image up into 8x8 blocks
Each block is a symbol to be coded
Compute Huffman codes for set of block
Encode blocks accordingly
3.2HUFFMAN CODING ALGORITHM

Fig.4lossy and lossless comparison ratio
3.HUFFMAN CODING
Huffman coding is based on the frequency of
occurrence of a data item (pixel in images). The principle
is to use a lower number of bits to encode the data that
occurs more frequently. Codes are stored in a Code Book
which may be constructed for each image or a set of
images. In all cases the code book plus encoded data must
be transmitted to enable decoding.
The Huffman algorithm is now briefly summarised:
A bottom-up approach
1. Initialization: Put all nodes in an OPEN list, keep it No Huffman code is the prefix of any other Huffman codes so
sorted at all times (e.g., ABCDE). decoding is unambiguous
2. Repeat until the OPEN list has only one node left: • The Huffman coding technique is optimal (but we
(a) From OPEN pick two nodes having the lowest must know the probabilities of each symbol for this
frequencies/probabilities, create a parent node of to be true)
them. • Symbols that occur more frequently have shorter
(b) Assign the sum of the children's frequencies/ Huffman codes
probabilities to the parent node and insert it into 4.LEMPEL-ZIV-WELCH (LZW) ALGORITHM
OPEN.
THE LZW COMPRESSION ALGORITHM CAN
(c) Assign code 0, 1 to the two branches of the tree, SUMMARISED AS FOLLOWS
and delete the children from OPEN.
w = NIL;
The following points are worth noting about the while ( read a character k )
above algorithm:
{
Decoding for the above two algorithms is trivial as long if wk exists in the dictionary
as the coding table (the statistics) is sent before the data. w = wk;
(There is a bit overhead for sending this, negligible if the data else
file is big.) add wk to the dictionary;
Unique Prefix Property output the code for w;
w = k;
No code is a prefix to any other code (all symbols }
are at the leaf nodes) great for decoder, unambiguous. If prior THE LZW DECOMPRESSION ALGORITHM IS AS
statistics are available and accurate, then Huffman coding is FOLLOWS
very good. read a character k;
3.1HUFFMAN CODING OF IMAGES output k;
w = k;
In order to encode images: while ( read a character k )

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/* k could be a character or a code. */ • Code this pair using a lossless method such as
{ Huffman coding
entry = dictionary entry for k; The difference is usually small so entropy
output entry; coding gives good results
add w + entry[0] to dictionary;
w = entry; Can only use a limited number of methods
on the edges of the image
}
5.LOSSY AND LOSSLESS ALGORITHMS
4.2ENTROPY ENCODING
TREC includes both lossy and lossless compression
Huffman maps fixed length symbols to variable algorithms. The lossless algorithm is used to compress data for
length codes. Optimal only when symbol the Windows desktop which needs to be reproduced exactly as
probabilities are powers of 2. it’s decompressed. The lossy algorithm is used to compress 3D
Arithmetic maps entire message to real number range image and texture data when some loss of detail is tolerable.
based on statistics. Theoretically optimal for long Let me just explain the point about the Windows
messages, but optimality depends on data model. desktop since it’s perhaps not obvious why I even mentioned
Also can be CPU/memory intensive. it. A Talisman video card in a PC is not only going to be
Lempel-Ziv-Welch is a dictionary-based compression producing 3D scenes but also the usual desktop for a Windows
method. It maps a variable number of symbols to a platform. Since there is no frame buffer, the entire desktop
fixed length code. needs to be treated as a sprite which in effect forms a
background scene on which 3D windows might be
Adaptive algorithms do not need a priori estimation superimposed. Obviously we want to use as little memory as
of probabilities, they are more useful in real possible to store the Windows desktop image so it makes
applications. sense to try to compress it, but it’s also vital that we don’t
4.2.1LOSSLESS JPEG distort any of the pixel data since it is possible that an
application might want to read back a pixel it just wrote to the
• JPEG offers both lossy (common) and lossless display via GDI. So some form of lossless algorithm is vital
(uncommon) modes. when compressing the desktop image.
• Lossless mode is much different than lossy (and also 5.1LOSSLESS COMPRESSION
gives much worse results)
Let’s take a look at how the lossless compression
• Added to JPEG standard for completeness algorithm works first as it the simpler of the two. Figure 4.1
shows a block diagram of the compression process.
• Lossless JPEG employs a predictive method
combined with entropy coding.
• The prediction for the value of a pixel (greyscale or RGBA DATA
color component) is based on the value of up to three COMPRESSED DATA
neighboring pixels RGB TO PREDICTI HUFFMAN
• One of 7 predictors is used (choose the one which YUV ON /RLE
gives the best result for this pixel). CONVERS ENCODIN
PREDICTOR PREDICTION
Fig. 4.1 the lossless compression process
P1 A
The RGB data is first converted to a form of YUV.
P2 B Using a YUV color space instead of RGB provides for better
compression. The actual YUV data is peculiar to the TREC
P3 C
algorithm and is derived as follows:
P4 A+B-C
Y=G
P5 A+(B-C)/2 U=R-G
P6 B+(A-C)/2 V=B-G
P7 (A+B)/2 The conversion step from RGB to YUV is optional.
Following YUV conversion is a prediction step which takes
advantage of the fact that an image such as a typical Windows
Table lossless jpeg desktop has a lot of vertical and horizontal lines as well as
• Now code the pixel as the pair (predictor-used, large areas of solid color. Prediction is applied to each of the
difference from predicted method) R, G, B and alpha values separately. For a given pixel p(x, y)
it’s predicted value d(x, y) is given by

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d(0, 0) = p(0, 0) The first step is to convert the RGB data to a form of YUV
d(0, y) = p(0, y) - p(0, y-1) for y > 0 called YOrtho using the following:
d(x, y) = p(x, y) - p(x-1, y) for x > 0 Y = (4R + 4G + 4B) / 3 - 512
The output values from the predictor are fed into a U=R-G
Huffman/RLE encoder which uses a set of fixed code tables. V = (4B -2R -2G) / 3
The encoding algorithm is the same as that used in JPEG for
encoding the AC coefficients. (See ISO International Standard Note that the alpha value is not altered by this step.
10918, “ Digital Compression and Coding of Continuous-Tone The next step is to apply a two-dimensional Discrete Cosine
Still Images”.) The Huffman/RLE encode outputs a series of Transform (DCT) to each color and alpha component. This
variable-length code words. These code words describe the produces a two-dimensional array of coefficients for a
length from 0 to 15 of a run of zeroes before the next frequency domain representation of each color and alpha
component. The next step is to rearrange the order of the
coefficient and the number of additional bits required to
coefficients so that low DCT frequencies tend to occur at low
specify the sign and mantissa of the next non-zero coefficient.
positions in a linear array. This tends to place zero coefficients
The sign and mantissa of the non-zero coefficient then follow in the upper end of the array and has the effect of simplifying
the code word. the following quantization step and improving compression
through the Huffman stage. The quantization step reduces the
5.2LOSSLESS DECOMPRESSION number of possible DCT coefficient values by doing an
Decompressing an image produced by the lossless integer divide. Higher frequencies are divided by higher
compression algorithm follows the steps shown in figure 4.2 factors because the eye is less sensitive to quantization noise
COPRESSION DATA in the higher frequencies. The quantization factor can vary
RGPA DATA
from 2 to 4096. Using a factor of 4096 produces zeros for all
HUFFMA INVERSE YUV TO input values. Each color and alpha plane has its own
N/RLE PREDICTI RGB quantization factor. Reducing the detail in the frequency
domain by quantization leads to better compression and the
5.2.1the lossless decompression process expense of lost detail in the image. The quantized data is then
Huffman encoded using the same process as was described for
The encoded data is first decoded using a Huffman decoder lossless compression.
using fixed code tables. The data from the Huffman decoder is
then passed through the inverse of the prediction filter used in 5.4LOSSY DECOMPRESSION
compression. For predicted pixel d(x, y) the output pixel The decompression process for images compressed
values p(x, y) are given by: using the TREC lossy compression algorithm is shown in
p(0, 0) = d(0, 0), p(0, y) = d(0, y-1) + d(0, y) figure
for y > 0 Type and LOD
Factors Parameters
p(x, y) = d(x-1, y) + d(x, y) for x > 0
The final step is to convert the YUV-like data back to RGB
using: R = Y + U, G = Y,B = Y + V
5.3LOSSY COMPRESSION Compressed Huffman/RLE Inverse Zig-zag
Data Decoding Quantize Reordering
The lossy compression algorithm is perhaps more interesting
since it achieves much higher degrees of compression that the
lossless algorithm and is used more extensively in
compressing the 3D images we are interested in. Figure 3
shows the compression steps.

RGBA RGB to YUV Forward Zig-zag Inverse YUV to RGB
Conversion DCT Ordering DCT Conversion RGBA
Data
Data

Fig. 4.4 the lossy decompression process
The decompression process is essentially the reverse
Quantize
Huffman/RLE
Encoding
Compressed of that used for compression except for the inverse
Data
quantization stage. At this point a level of detail (LOD)
parameter can be used to determine how much detail is
required in the output image. Applying a LOD filter during
Type and
Factors
decompression is useful when reducing the size of an image.
The LOD filter removes the higher frequency DCT
Fig. 4.3 the lossy compression process coefficients which helps avoid aliasing in the output image

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when simple pixel sampling is being used to access the source [6] S. Martucci. Reversible compression of hdtv images using
pixels. median adaptive prediction and arithmetic coding. Proc. IEEE
Note that the level of detail filtering is not a part of International Symposium on Circuits and Systems, pages
the TREC specification and not all TREC decompressions will 1310–1313, 1990.
implement it. [7] M. Weinberger, G. Seroussi, and G. Sapiro. LOCO-I: A
low complexity, context-based, lossless image compression
6.EXPERIMENTAL RESULTS algorithm. Proc. Data Compression Conference, pages 140–
We present experimental results based on the steps 149, March 1996.
[8] ITU-T Rec. T.84 - Information Technology. Digital
Step1. Lossless Compression compression and coding of continuous-tone still images:
Step2. Lossless Decompression Extensions, July 1996.
[9] M. Weinberger, J. Rissanen, and R. Arps. Applications of
Step3. Lossless image Compression using Huffman coding universal context modeling to lossless compression of gray-
Step4. Lossless image Decompression using Huffman coding scale images. IEEE Trans. on Image Processing, 5(4):575–
586, April 1996.
Step5. Lossless image Compression for transmitting Low [10] G. Wallace. The jpeg still picture compression standard.
Bandwidth Line
Comms. of the ACM, 34(4):30– 44, April 1991.
7.CONCLUSIONS [11] ISO/IEC 14495-1, ITU Recommendation T.87,
“Information technology - Lossless and near-lossless
This work has shown that the compression of image can be
compression of continuous-tone still images,” 1999.
improved by considering spectral and temporal correlations as
well as spatial redundancy. The efficiency of temporal [12] M. J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I:
prediction was found to be highly dependent on individual A low complexity lossless image compression algorithm.”
image sequences. Given the results from earlier work that ISO/IEC JTC1/SC29/WG1 document N203, July 1995.
found temporal prediction to be more useful for image, we can
conclude that the relatively poor performance of temporal
prediction, for some sequences, is due to spectral prediction
being more efficient than temporal. Another Conclusions and
Future Work finding from this work is that the extra
compression available from image can be achieved without
necessitating a large increase in decoder complexity. Indeed
the presented scheme has a decoder that is less complex than
many lossless image compression decoders, due mainly to the
use of forward rather than backward adaptation.
Although this study considered a relatively large set of test
image sequences compared to other such studies, more test
sequences are needed to determine the extent of sequences for
which temporal prediction is more efficient than spectral
prediction..
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