MATLAB Based Image Compression Technology Lu Jinhua (Shanxi Fenxi Coal advanced technical schools) Abstract This paper describes research in image compression MATLAB application of BP artificial neural network-based image compression The MATLAB implementation and wavelet-based image compression technology. MATLAB software uses MATLAB R2007a version, and through the After a computer experiment proved that wavelet transform and BP neural

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MATLAB Based Image Compression Technology

Lu Jinhua



(Shanxi Fenxi Coal advanced technical schools)

Abstract This paper describes research in image compression MATLAB
application of BP artificial neural network
-
based image compression

The MATLAB imple
mentation and wavelet
-
based image compression
technology. MATLAB software uses MATLAB R2007a version, and through the

After a computer experiment proved that wavelet transform and BP neural
network encoding and decoding of images to achieve high compressi
on ratio
can be guaranteed under very

Good image quality, with good visual effects.

Key words MATLAB R2007a; image compression; wavelet transform; BP
neural network

CLC number: TD679 Abstract: A Article ID :1672
-
0652 (2008) 12
-
0035
-
04

Digital image inf
ormation to make electronic and information technology
sector's most

Major problem is the huge amount of data storage and transmission problems,
image data compression

Reduction technology is the key to solving the problem. As the image data are
often ex
ists

All kinds of information redundancy, such as spatial redundancy, information
entropy redundancy, visual redundancy

And structural redundancy, and therefore can be said that image compression
is to remove the map

Like the various redundant to retain

useful information for the process ¨ J.

MATLAB is introduced by the United States for the Math Works Inc.

Numerical computing and graphics processing in scientific computing software,
which combines numerical sub
-


Analysis, matrix calculations, signal

processing and graphical display a variety
of functions in one.

In March 2007, Math Works Inc. MATLAB launched

R2007a version of the latest products, this version adds a lot of new power

Be able to, basically a lot of typical applications include the c
urrent number of
graphics processing

Li. The neural network toolbox in MATLAB provides a lot of use

In the image processing functions.
MATLAB R2007a in the wavelet tools

Box (Wavelet Toolbox 3.0) in the letter that contains a variety of wavelet
analysis


Number, can be used to signal and image compression processing,
compression can maintain

Signal and image features remain basically unchanged, compression is high
and compression speed,

And in the transfer process that has anti
-
jamming capability. This

article
discusses the application of

MATLAB neural network toolbox in the BP function and wavelet tools

Box (Wavelet Toolbox 3.0) function is the principle of image compression,

And examples of its implementation method is given and the corresponding
a
nalysis.

One based on BP artificial neural network image compression principle

BP network is currently the most commonly used an artificial neural network
model

Type, it can directly provide data compression capability. Using multilayer
feedforward netw
orks

Network model transformation capabilities for data transformation (encoding)
the basic idea of

Is: to a group of input patterns by a small number of hidden layer unit is
mapped to a

-
Output mode, and to output mode is equal to input mode as much as

possible.
When

Than the number of hidden layer unit came from a number of input mode, it
means that the hidden layer

Better performance in input mode, and to transmit to the output of this
performance

Layer, output layer nodes and input nodes is the sa
me. Study, the graph

Like the data sent to both the input layer, but also sent to the output layer as a
teacher signal,

The learning algorithm used in network training algorithm is good after the
input layer to the

Hidden layer network coding process, t
he image data, linear or

Non
-
linear transformation, from the hidden layer to output layer for the network
decoding process,

Pairs of compressed transform coefficients after the linear or nonlinear inverse
change

Change to restore the image of the origin
al data.

Considered for study are N × N image pixel points, each pixel

Gray value is quantified as m Bit (a total of 2m possible values). 2m

A gray
-
scale by a linear relationship into a value between 0 and 1 as the
network

The input and desired output
(teacher mode). Network randomly drawn each

nxn image block (after [0,1] interval of the transformation) as a learning model

Type, with the BP learning algorithm.
By adjusting the neural network of the
question

Connection weights, so that the training s
et the image reconstruction error E =.
Plant 1 g, mean

To a minimum.
Trained network hidden layer neuron vector (after

Quantitative) is the result of data compression, while the output neurons is the
vector

Reconstruction of data.

2 Based on BP artifici
al neural network implementation
of image compression

With the basic BP network for image compression is divided into two bands

Section: Training and coding.
The first phase, the sample image data set as a

Signal input and teacher training BP network; s
econd stage: entropy series

Yards. Using BP artificial neural network image compression steps, such as

Follows:

A) Training sample structure.
To an image pixel for all

For the compression network input should be properly control the size of the
network
, therefore, the first

First division of the image.
Compressed image to be set up by the N × N pixels

Point of composition, be divided into M sub
-
image blocks, each sub
-
image
block are

By P × P sub
-
pixel block constituted.
Here to 128 × 128 of

The imag
e pixel matrix is divided into 4 × 4 sub
-
pixel block matrix as an
example to illustrate the

The generation of training samples.

[32] 44

[64] 44

i; 0 i

0 i

[993] [994]




[1024]. 4 x4

= [[1] 4 X4, [2]

, [1024]]. ]

By the above method to generate t
he pixel block matrix must also be
normalized

A treatment, this paper is the mean of the distribution of pre
-
treatment; pending
Map

Like the gray range [,], transform domain [Y, y], so that

The current gray value of pixels to be processed as follows:, t
hen the map Y is:

: Dan Zhu Tang. = +

, M f

m I

1

m

. m m

Spend
-
type the original image pixel value is designated to the [0,1] range

In doing so, constitutes all the pretreatment process of training samples.

2) Create a neural network. Samples of
the image data set as input

And teacher training BP network signals, BP network image compression bit

Rate is defined as:

Bit Rate: (6, such as / pi, ez)

l "tL

Here the input image is divided into an n
-
dimensional vector, and f,
respectively

H on beh
alf of encoding a number of bits needed and encoding a coupling
weight

(Ie, W: and b ') the number of bits required, k is the hidden layer neurons of the
month

Number.

Image data compression ratio = input layer node / hidden layer nodes.

After a good n
eural network training, network coupling weights in this pressure

Reduction process remains unchanged. Newff function in MATLAB, is used

Create back
-
propagation network.

3) The neural network training. Used to build an input matrix,

Each column represe
nts an input model, with the input matrix as a target
output

Matrix, began training the network. The training process can be used in a
different school

Learning algorithm. In the MATI AB function is realized using train neural
network

Training.

4) enco
ding simulation. Algorithm with a given training network, and then
imitation

True. If the hidden layer neurons to K, then the network hidden layer generate
K

× 1024 matrix, between the hidden layer and output layer produce the 16 × K

Weight matrix, the
output layer produces the threshold 16 × 1 vector,
respectively, of the imitation

True results are entropy coded. MATLAB using sire function implementation of
the

Neural network simulation.

5) The image reconstruction.
Right entropy encoded bit stream t
o decode

Be the hidden layer output hj and weight (with b), substituting into the formula

Out the network output Y, which is a 16 × 1024 matrix, each matrix

Elements were multiplied by 255, to each pixel value from [0,1] restored to [0,

255], then each

column to the quantitative formation of the image block, all
sub
-
images

Combined into a complete image, thus completing the image reconstruction ¨].

The following experiment to prove that using BP neural network image
compression

Results.
Neural networ
k input nodes as the l6, respectively, the hidden layer S

For 8,4,2, representing the compression ratio of 2,4,8.
Lack of space

Procedures and operating processes omitted. The results shown in Figure 1
run.

Figure 1 Original image and compressed reconst
ructed image

Effect from the
reconstructed image can be seen, when the number of hidden layer neurons

Less (S = 2), the compression is relatively high, but the reconstructed image
quality than the

Poor, so simulation process, by increasing the God of the

hidden layer

After a few yuan to improve the reconstructed image quality.

3 Wavelet
-
based image compression

Wavelet decomposition method is a window size (ie, the window area) solid
-


Fixed, but its shape can change, time window and frequency window c
an be
asked to change the time

Frequency localization analysis method, namely, low
-
frequency part of the
high frequency sub
-


Identified rate and a lower time resolution, in the high
-
frequency part of the
high time

Between the resolution and lower frequ
ency resolution, it is called "math
significantly

Mirror. "It is this feature, so that wavelet transform has the right signals from
the

Adaptability.

Wavelet Transform for image compression The basic idea is: put the image
into the

The multi
-
resolution

decomposition, broken down into. Different space,
different frequency sub
-
graph

Like, and then pairs the image coefficients are encoded. Coefficient of wavelet
encoding is

Transform core for image compression, compression coefficient of the
substance is

the amount of

Of compression. Image is generated after the wavelet transform of the number
of wavelet image

According to the original image data on the total amount equal to the wavelet
transform itself

Does not have compression. The reason to use it f
or image compression,
because

The resulting wavelet image has different characteristics with the original
image, shown in Figure

Like the energy concentrated in the low
-
frequency part, while the horizontal,
vertical and diagonal

Line part of the energy
is less; horizontal, vertical and diagonal part of the
characterization of

The original image in the horizontal, vertical and diagonal part of the edge
information, with

A clear direction of features. Brightness of the image can be called
low
-
frequency p
art of the water

Flat, vertical and diagonal part of the details of the image can be called.

Wavelet multi
-
resolution analysis process will be through an image into

Approximation and details of two parts, the details of the corresponding
small
-
scale tra
nsient, it

In this scales very stable. Therefore, the details stored on the approximation

Part of the next scale, break it down, repeat the process can be.

Application of MATLAB wavelet toolbox for image compression, there are two

Methods.

1) Use wave
dec2 function of the image wavelet decomposition, then

With appcodf2 function decomposition image reconstruction, and finally

wcodemat function quantization coding, to get results images (procedures and

Running omitted). The results of operations shown
in Figure 2.

Figure 2 Wavelet image compression based on the results of

Here you can see, the first extract the original image compression

Wavelet decomposition of the first layer of low
-
frequency information, this time
a better compression, pressure

R
eduction is relatively small (about 1 / 4 size). The second is to extract the first
compression

Layer decomposition of low
-
frequency part of the low
-
frequency part (ie the
second layer of the low
-
frequency Division

Points), compressed relatively large (a
bout 1 / 12), compression in the visual

Also can be basically. As the decomposition level increases, the compression
ratio is decreasing

The.

Above the low
-
frequency information to preserve the original image
compression method is only

One of the most
simple compression methods, which eliminate the need for
other treatment can be

Get better compression results. For the above example can also be extracted
only

Wavelet decomposition of the third and fourth floor of the low
-
frequency
information. In theo
ry,

Availability of any compression ratio of the compressed image. In the
compression ratio and image

Have high quality requirements, as other coding method.

2) the use of wavelet toolbox in the exclusive function of the threshold value of
compressed im
age

wdencmp for a given image compression. First, applications are given

Function wdencmp compression efficiency, that is, decomposition coefficient
set 0

Number of coefficients and retention of the percentage of the energy
percentage (procedures and op
erating

Process omitted).
The results of operations shown in Figure 3.

4 Conclusion

MATLAB softwares were introduced to obtain the majority of users on the
Green

Gaze, with the version of the continuous improvement of functions more and
more powerful,
especially the

March 2007 launch of MATLAB R2007a an increase of 350

New features, easy to use, easy to learn.
The calculation of the user from the
heavy

And programming freed. Therefore, the use of MATLAB software can be a
good

In order to achieve a mu
ltiplier effect.
This article is in the use of MATLAB

Based on the convenience of the application of BP neural network toolbox
functions and small

Wave function is easily achieved toolbox image compression, and has gone
through real
-

Verify Ming, compress
ion and can be better guaranteed than high image
quality.

Received Date 2008
-
09
-
20

References

[1] Ai
-
Ling Wang, Ye
-
sheng, Deng Qiuxiang.
MATLAB R2007 image
processing technology and application [M].
Beijing: Electronic Industry Press.
2008:35
-
39

[2] Li Yong.
Intelligent

M ATLab
的研究图像压缩技术

吕金华

摘要介绍了
MATLAB
在图像应用
compression.Emphasizes
两个图像压缩

小波和
BP R2007a MATLAB
的神经网络工具箱,计算机实验证明,更好

图像质量和更好的视觉效果可以证实,在高压缩率的情况时,使用这种方法学,

外径。

关键词
MATLAB

R2007
一,图像压缩,小波变换
; BP
神经网络

(上接第
30
页)

研究控制助理控制改造
Centralizati0n

设备在火电厂

荣展宇

对火电厂辅助控制系统摘要聚焦,改变了当地的控制表明,
Con


centrating
控制,分析的可行性和必要性这一改变,并介绍了硬件的部署

助理系统使用的是荷兰国际集团,以集中监控
Xingneng
电力公司改革,并介绍
了前

perience
和实践的认识。

关键词助理控制设备,集中监控
;
研究