seminar code
09-08-2014, 09:48 AM
Effective compression using SPIHT with Entropy encoder
[attachment=66771]
Abstract
SPIHT is computationally very fast and among the
best image compression algorithms known today. According to
statistic analysis of the output binary stream of SPIHT
encoding, propose a simple and effective method combined
with Huffman encode for further compression. A large number
of experimental results are shown that this method saves a lot
of bits in transmission, further enhanced the compression
performance.
INTRODUCTION
In recent years, wavelet transform [1] [2] as a branch of
mathematics developed rapidly, which has a good
localization property[3] in the time domain and frequency
domain, can analyze the details of any scale and frequency.
so, it superior to Fourier and DCT. It has been widely
applied and developed in image processing and
compression.
EZW stands for ‘Embedded Zerotree Wavelet’,which is
abbreviated from the title of Jerome Shapiro’s 1993
article[4],”Embedded Image Coding Using Zerotrees of
Wavelet Coefficients”. EZW is a simple and effective
image compression algorithm, its output bit-stream ordered
by importance.Encoding was able to end at any location,so
it allowed to achieve accurate rate or distortion. This
algorithm does not need to train and require pre-stored
codebook. In a word, it does not require any prior
knowledge of original image.
More improvements over EZW are achieved by SPIHT,
by Amir Said and William Pearlman, in 1996 article, "Set
Partitioning In Hierarchical Trees" [5] . In this method,
more (wide-sense) zerotrees are efficiently found and
represented by separating the tree root from the tree, so,
making compression more efficient. Experiments are shown
that the image through the wavelet transform, the wavelet
coefficients’value in high frequency region are generally
small [6], so it will appear seriate "0" situation in quantify.
SPIHT does not adopt a special method to treat with it,but
direct output. In this paper, focus on this point, propose a
simple and effective method combined with Huffman
encode for further compression. A large number of
experimental results are shown that this method saves a lot
of bits in transmission, further enhanced the compression
performance
Description of the algorithm
Image data through the wavelet decomposition,the
coeffi-cient of the distribution turn into a tree. According to
this feature, defining a data structure: spatial orientation
tree. 4-level wavelet decomposition of the spatial
orientation trees structure are shown in Figure1.We can see
that each coeffc-ient has four children except the ‘red’
marked coeffcients in the LL subband and the coeffcients in
the highest subbands (HL1;LH1; HH1).
The following set of coordinates of coeffcients are used to
represent set partitioning method in SPIHT algorithm. The
location of coeffcient is notated by(i,j),where i and j indicate
row and column indices, respectivel
ANALYSIS OF EXPERIMENTAL RESULTS
In order to verify the validity of this algorithm, images
usually using all are analyzed, we use 5-level pyramids
constructed with the 9/7-tap filters.Table2 is shown the
experiment results of two standard 512×512 grayscale
image Lena, Goldhill at different rate. Average code length
which is calculated as follows:
CONCLUSION
Proposing a simple and effective method combined with
Huffman encoding for further compression in this paper
that saves a lot og bits in the image data transmission.
There are very wide range of practical value for today that
have a large number of image datas to be transmitted.
[attachment=66771]
Abstract
SPIHT is computationally very fast and among the
best image compression algorithms known today. According to
statistic analysis of the output binary stream of SPIHT
encoding, propose a simple and effective method combined
with Huffman encode for further compression. A large number
of experimental results are shown that this method saves a lot
of bits in transmission, further enhanced the compression
performance.
INTRODUCTION
In recent years, wavelet transform [1] [2] as a branch of
mathematics developed rapidly, which has a good
localization property[3] in the time domain and frequency
domain, can analyze the details of any scale and frequency.
so, it superior to Fourier and DCT. It has been widely
applied and developed in image processing and
compression.
EZW stands for ‘Embedded Zerotree Wavelet’,which is
abbreviated from the title of Jerome Shapiro’s 1993
article[4],”Embedded Image Coding Using Zerotrees of
Wavelet Coefficients”. EZW is a simple and effective
image compression algorithm, its output bit-stream ordered
by importance.Encoding was able to end at any location,so
it allowed to achieve accurate rate or distortion. This
algorithm does not need to train and require pre-stored
codebook. In a word, it does not require any prior
knowledge of original image.
More improvements over EZW are achieved by SPIHT,
by Amir Said and William Pearlman, in 1996 article, "Set
Partitioning In Hierarchical Trees" [5] . In this method,
more (wide-sense) zerotrees are efficiently found and
represented by separating the tree root from the tree, so,
making compression more efficient. Experiments are shown
that the image through the wavelet transform, the wavelet
coefficients’value in high frequency region are generally
small [6], so it will appear seriate "0" situation in quantify.
SPIHT does not adopt a special method to treat with it,but
direct output. In this paper, focus on this point, propose a
simple and effective method combined with Huffman
encode for further compression. A large number of
experimental results are shown that this method saves a lot
of bits in transmission, further enhanced the compression
performance
Description of the algorithm
Image data through the wavelet decomposition,the
coeffi-cient of the distribution turn into a tree. According to
this feature, defining a data structure: spatial orientation
tree. 4-level wavelet decomposition of the spatial
orientation trees structure are shown in Figure1.We can see
that each coeffc-ient has four children except the ‘red’
marked coeffcients in the LL subband and the coeffcients in
the highest subbands (HL1;LH1; HH1).
The following set of coordinates of coeffcients are used to
represent set partitioning method in SPIHT algorithm. The
location of coeffcient is notated by(i,j),where i and j indicate
row and column indices, respectivel
ANALYSIS OF EXPERIMENTAL RESULTS
In order to verify the validity of this algorithm, images
usually using all are analyzed, we use 5-level pyramids
constructed with the 9/7-tap filters.Table2 is shown the
experiment results of two standard 512×512 grayscale
image Lena, Goldhill at different rate. Average code length
which is calculated as follows:
CONCLUSION
Proposing a simple and effective method combined with
Huffman encoding for further compression in this paper
that saves a lot og bits in the image data transmission.
There are very wide range of practical value for today that
have a large number of image datas to be transmitted.