enhancing efficiency of huffman coding
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Enhancing Efciency o HumanCoding Using Lempel Ziv Coding orImage Compression
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Contents Image compression
Flow o compression
ypes o image compression
HL !chemeHL Coding " !tage #
HL Coding " !tage $
HL %ecoding
Comparison
Conclusion
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Image Compression
%igital images re(uire huge amounts o space or
storage and large )andwidths or transmission*
◦ + ,-. / -0. color image re(uires close to #12 o
space*
he goal o image compression is to reduce the
num)er o )its re(uired to store and transmit a
digital image*
◦ 3educe storage re(uirements and increase
transmission rates*
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Flow o compression
• he image 5le is converted into a series o
)inary data6 which is called the )it7stream
• he decoder receives the encoded )it7stream
and decodes it to reconstruct the image
• he total data (uantity o the )it7stream is
less than the total data (uantity o the original$#' -
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ypes o imagecompression
Image compression
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Lossless Compression
he original image can )e perectly recovered
orm the compressed image
8oiseless 7 they do not add noise to the signal
9image:
+lso called entropy coding 7 decomposition
techni(ues to eliminate&minimi;e redundancy
Used only or a ew applications with stringent
re(uirements such as medical imaging
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HL !CHE1E
a ast lossless image compression schemes
consists o $ stages>
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!tage # > Human CodingExample :
%ata > ?#44=#'-'#''#4'=.,4-= @
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Symbols Probability
. .*.'
# .*$
$ .
4 .*$
- .*#
' .*$'
, .*.'
= .*#'
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contd****
Symbols Hufman Codewords
# ..
4 .#
' #.
= ##.
$ ###...
. ###..#
, ###.#
- ####
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contd***
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Code tree
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!tage $ > LZ< Coding
Butput o stage # Input to stage $ >
Encoded data 9using Human Coding: >
..6.#6.#6##.6..6#.6####6#.6..6#.6#.6..6.#6
#.6##.6###..#6###.#6.#6####6##.
Concatenate all the Human code words
apply LZ< algorithm
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contd***
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LZ< %ictionary
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!tage # > LZ< %ecoding3ead a value rom the encoded input and
output the corresponding string rom the
initiali;ed dictionary*
+t the same time6 o)tain the ne/t value romthe input6 add to the dictionary the
concatenation o the string Dust output and the
5rst character o the string o)tained )y
decoding the ne/t input value*
he decoder then proceeds to the ne/t input
value which was alread read in as the ne/t$#' #'
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!tage $ > Human%ecoding he result o the #st stage is the concatenated
Human code words >
...#.###...#.#####...#.#....##.##.###
..####.#.#######.
+ter the code has )een created6 coding and&or
decoding is accomplished in a simple loo7up
ta)le manner*
+ter decoding the a)ove Human codeword
)y using the Human tree we get the original
message i*e* ?#44=#'-'#''#4'=.,4-=@
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Comparison )etween HumanCoding HL Coding
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Conclusion+ new compression and decompression
techni(ue called as HL !cheme )ased on
Human coding and Lempel Ziv coding is very
efcient techni(ue or compressing the image
to a great e/tent*
his techni(ue is simple in implementation and
utili;es less memory*
he advantage )ecome most apparent or
images with more si;e*
Future wors may )e on improving the$#' #0
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3eerences >
?Enhancing Efciency o Human Coding using
Lempel Ziv Coding or Image Compression@6 C*
!aravanan6 1* !urender6 International Gournal o
!ot Computing and Engineering 9IG!CE: I!!8> $$4#7
$4.=6 olume7$6 Issue7,6 Ganuary $.#4
? A study o various image compression techniues@6
!onal6 %inesh umar6 %epartment o Computer
!cience Engineering Juru Gham)heswar
University o !cience and echnology6 Hisar
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