WAVELET BASED IMAGE COMPRESSION ON THE TEXAS INSTRUMENT
VIDEO PROCESSING BOARD TMS320DM6437
Riken Shah
B.E., Gujarat University, India, 2007
PROJECT
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
ELECTRICAL AND ELECTRONIC ENGINEERING
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2011
WAVELET BASED IMAGE COMPRESSION ON THE TEXAS INSTRUMENT
VIDEO PROCESSING BOARD TMS320DM6437
A Project
by
Riken Shah
Approved by:
__________________________________, Committee Chair
Jing Pang, Ph.D.
__________________________________, Second Reader
Fethi Belkhouche, Ph.D.
____________________________
Date
ii
Student: Riken Shah
I certify that this student has met the requirements for format contained in the University
format manual, and that this project is suitable for shelving in the Library and credit is to
be awarded for the Project.
__________________________, Graduate Coordinator
Preetham Kumar, Ph.D.
Department of Electrical and Electronic Engineering
iii
________________
Date
Abstract
of
WAVELET BASED IMAGE COMPRESSION THE TEXAS INSTRUMENT VIDEO
PROCESSING BOARD TMS320DM6437
by
Riken Shah
Time has become a crucial issue in today’s lifestyle and to keep up the pace with
the world we need to come up with technologies that can process things faster. With high
speed technology in image processing industry, the demand of good quality data is
increasing rapidly. The usage of image and streaming of video on internet have increased
exponentially. In addition, more storage capacity and more bandwidth as HD (High
Density) image and video have become more and more popular. In this project, mainly I
demonstrated two different methods of image compression DCT based image
compression and WAVELET based image compression on JPEG2000 image standard. I
designed DCT based image compression and WAVELET based image compression
codes in matlab and compared their results. After that, I implemented the wavelet
algorithm using C and C# in visual studio to verify the design. Finally I implemented the
same algorithm on TI’s digital signal processing board EVM320DM6437, based on C
language.
In addition, for implementing discrete wavelet transform on EVM320DM6437
board, I captured the image frame from a video signal. Then, I extracted the Y
iv
components of the image. Then I used Code Composer Studio software to implement the
code written in C language to successful display the compression result on Television.
_______________________, Committee Chair
Jing Pang, Ph.D.
_______________________
Date
v
ACKNOWLEDGEMENTS
As I am starting discussing the project, the first thing I would love to do is really
thank and say something to all the people who have contributed also motivated me for
completing this project successfully. I would like to thank Dr. Jing Pang for providing me
such a nice opportunity to work on this project, which was a great exposure to the field of
digital processing. I must say she has a very good knowledge in this field. I must say that
I would not have been possible for me to finish this project without her help throughout
the research.
I would like to thank Shetul Saksena, who also worked with me on this project.
Without his help, it would have been impossible for me to finish this project. His
experience and knowledge have played vital roles in this project.
I would like to thank Dr. Fethi Belkhouche for reviewing my report. His
experience was also a great help for me to understand this project. He showed me many
areas where I can improve my presentation of report work. Last but not the least I would
like to thank all of my family members for providing me strength and immense support.
Also, thanks to all the faculty members of Department of Electrical and Electronics
Engineering at California State University, Sacramento for the help and support to
complete my graduation successfully.
vi
TABLE OF CONTENTS
Page
Acknowledgements……………………………………………………………….……...vi
List of Figures...…………………………………………………………………………..ix
Chapter
1. INTRODUCTION……………………………………………………………………1
1.1 Introduction to image processing…….………………………………………1
1.2 Organization of report………………………………………………………..2
2. BASIC CONCEPT OF IMAGE PROCESSING AND TWO DIFFERENT TYPES
OF FORMAT JPEG AND JPEG2000……….………………………………………4
2.1 Introduction to image compression and decompression...……………………4
2.2 Types of image compression techquniques….……………………………….4
2.2.1 Lossy image compression………………………………………………4
2.2.2 Lossless image compression……………………………………………5
2.3 Two different formats JPEG and JPEG 2000………………………………...6
2.3.1 Introduction to image standard JPEG…………………………………..6
2.3.2 Introduction to image standard JPEG 2000…………………………….7
2.4 Two algorithms DCT and Wavelets used in JPEG and JPEG 2000…………..
respectively…………...…………...………………………………...………. 8
2.4.1 Wavelet based image processing………………………………………8
2.4.2 DCT based image processing………………………………………...11
2.4.2.1 Quantization in DCT………………………………………...12
vii
2.4.2.2 Zigzag scanning……………………………………………...13
3. MATLAB MODEL OF DCT AND WAVELET IMAGE PROCESSING………...14
3.1 Matlab representation of DCT based signal processing………………………...14
3.2 Wavelet based signal processing……………………………………………….19
4.
WAVELET IMPLEMENTATION USING C LANGUAGE…….………………..22
4.1 Components on hardware and interface………...…………………………..23
4.2 Wavelet based image processing using C and C#....………………………..23
5. CONCLUSION AND FUTURE WORK........………................................................29
Appendix A Matlab model for DCT based processing…………….........………...…….30
Appendix B C model for wavelet based image compression on hardware…………......34
REFEREENCES...………………………………………………………………………35
viii
LIST OF FIGURES
1.
Figure 1.1.1: Different steps of image compression and decompression..………..1
2.
Figure 2.4.1(a): Steps involved in wavelet based image compression...………….9
3.
Figure 2.4.1(b): DWT on each tile in an image………………..……………….....9
4.
Figure 2.4.1(c): Wavelet transform on an image at different level…….……...…10
5.
Figure 2.4.2(a): Equation to calculate DCT in 2D…………….….……………...11
6.
Figure 2.4.2.1(a): Quantization matrix for DCT……………...………………….12
7.
Figure 2.4.2.2(a): Path followed in zigzag scan…….……………………………13
8.
Figure 3.1.1: Original test image of sunview……….……………………………14
9.
Figure 3.1.2: YCbCr image result……………………………………………......15
10.
Figure 3.1.3: Y component extraction of an image………..…………………….15
11.
Figure 3.1.4: Quantized Y component of an image..…………………...…...…...16
12.
Figure 3.1.5: Reconstructed YCbCr image…………………..…...……..……….17
13.
Figure 3.1.6: Reconstructed RGB image……………………………..……….…17
14.
Figure: 3.1.7: Error image………………………..………………………………18
15.
Figure 3.2.1: Input image for wavelet based image compression.……………….19
26.
Figure 3.2.2: Processed image……………….…………………………………..19
17.
Figure 3.2.3: Reconstructed image………..……………………………………..20
18.
Figure 3.2.4: Matrix for wavelet based image compression…………………..…20
19.
Figure 4.1.1: TI DAVINCI signal processing board…………………………….22
20.
Figure 4.1.2: Block diagram for TI DAVINCI signal processing board.………..23
ix
21.
Figure 4.2.1: Image used to do wavelet processing using C#...............................24
22.
Figure 4.2.2: Wavelet based image processing 2 level………….……………….25
23.
Figure 4.2.3: Wavelet based image processing 3 level………….………..……...26
x
1
Chapter 1
INTRODUCTION
1.1 Introduction to image processing
Image compression is getting more and more attention day by day as high speed
compression and good quality of image are in high demand. One advantage of an Image
compression is to reduce the time taken for transmission of an image [1]. For example, an
image has 512 rows and 512 columns. Without compression, totally 512x512x8 =
2,097,152 bits data needed to be stored [1]. And, each pixel is represented by 8-bit data
format. Now to compress it means to reduce the number of bits needed to store those bits
without sacrificing a lot for image quality [1]. General image compression and
decompression block diagram is shown below.
Figure 1.1.1 Different steps of image compression and decompression [2]
Compression is useful to reduce the cost of extra use of transmission bandwidth
or storage for larger size images. In addition, there is always decompression followed by
compression. This process involves loss of data, and one should control the ratio of the
2
information being lost, as images should not be compressed to a level that one cannot
even recover them with minimal loss.
What is JPEG2000? JPEG 2000 can give significant increase in performance as
compared to JPEG [2]. It is an extension to JPEG. It was introduced in 2000, so it is
called JPEG2000. The biggest advantage of it is flexibility. One can get compressed
images scalable in nature. The compression technique can be truncated at any point in
time, now the resolution and signal to noise ratio depend on the point they are truncated
[2]. In addition, it is possible to achieve variable compression rate due to its flexibility
and adaptability. With JPEG, compression algorithm is applied before encoding while
with JPEG2000 it is done in a single step. Wavelet is the algorithm name used to do
processing of JPEG2000 format. From the research, it has been found that compression
achieved by this method is 20% more as compared to JPEG [2]. Wavelet algorithm works
on a pixel value. It can vary from values 0 (black) to 256 (white). In wavelet based
compression, average and difference of two adjacent pixels are taken. Then the value
close to 0 is discarded. This is a method to reduce the number of bits. Because of its
effectiveness in edge detection, it is used in medical images as well as in military images.
Police department use it for finger print detection. DMV follows this algorithm for car
plate detection.
1.2 Organization of report
Chapter 2 consists of basic information of what compression and decompression.
In addition, the difference between two industry standards JPEG and JPEG 2000 I
3
described.
Different components of these two standards are explained with lots of
figures. It makes this report easy to understand.
Chapter 3 has the entire information about Matlab model implemented for both
DCT based and Wavelet based image processing. Again, the results generated by those
matlab codes are included in the report. It is making the report easy for readers to
understand the difference between both of them.
Chapter 4 consists of C language based model of wavelet-based algorithm. This
algorithm was implemented on Texas Instruments’ digital signal processing board
provided by Dr. Jing Pang. All the result images generated by that algorithm to prove the
reliability of the code are included in this report.
Chapter 5 shows how Wavelet is better than DCT based algorithm along with
results to support it. In addition, the implementation of the code on hardware is also
shown.
4
CHAPTER 2
BASIC CONCEPT OF IMAGE PROCESSING AND TWO DIFFERENT TYPES OF
FORMAT JPEG AND JPEG2000
2.1 Introduction to image compression and decompression
Any image taken from a camera is stored in a computer in form of a matrix. Each
pixel is represented by a value from 0 to 255. More pixels in an image means that more
memory is needed to store that image. Therefore, digital image processing concept is
concentrated on reduction of memory consumption. It has been found that general
1024X1024-color image occupies 3 MB of memory for storage [3]. Digital image
transmission is also a major area, where data compression and decompression methods
are required. Since most communication channels are noisy, Robustness of these
techniques is equally important [3].
2.2 Types of image compression techniques
Digital image compression can be divided mainly in two large categories: lossless
and lossy compression. Lossless compression is used for artificial images. They use low
bits rate. There is a possibility of loss some of the data during this process. While lossless
compression is preferred in medical images and military images.
2.2.1 Lossy image compression
In lossy compression and decompression methods, accuracy is so important.
There will be a data loss but it should be under the limit of tolerance. It should be good
enough for application of image processing. This kind of compression is used for
transmitting or storing multimedia data, where compromise with the loss is allowed. In
5
contrast to lossless data processing, lossy signal processing repeatedly does compression
and decompression on file. That eventually will affect the quality of data. The concept of
lossy compression is supported by rate distortion theory. CPC, JPEG, Fractal
compression are some examples of lossy signal processing methods. To conclude, on the
receiver side, when there is a human eye the signal allows lossy compression as human
eye can tolerate some imperfection [3].
2.2.2 Lossless image compression
In this type of compression, after decompression the images are almost the same
as output images. It can allow the difference between the original image and the
reconstructed image up to the certain predefined value. Lossless compression can be a
valuable solution where we have strict constrains on the reconstruction. In addition, this
method is useful where little information on each pixel is very important. We call wavelet
a loss less technique. In wavelet algorithm, we do successive re-construction. This makes
it possible to receive all the information without a data loss. RLE, LZW, Entropy coding
are some examples of lossless data compression.
Following are some basic steps involved in compressing and decompressing
image.
Step 1: Give specifications like what are the bits we have available and what the
error tolerance on the receiver side.
Step 2: Separate data into different categories depending on their importance.
Step 3: Quantize each category using all the information we have available.
Step 4: Encode all the information with help of entropy coder.
6
Step 5: On receiver side read data that is quantized. This process is usually faster
than transmitter side.
Step 6: Decode that data using entropy decoder.
Step 7: Dequantization of the data received after step 6.
Step 8: Reconstruct the image
[4]
2.3 Two different formats JPEG and JPEG 2000
In this section, two different image standards JPEG and JPEG 2000 are discussed.
2.3.1 Introduction to image standard JPEG
JPEG (Joint Photographic Experts Group) comes from the committee name that
introduced this standard. This is a lossy type of image compression. It is effectively used
on still images. On the other hand, MPEG is used for moving image standards. JPEG is
called lossy because the reconstructed image on the receiver side is not the same as the
transmitter side. The advantage is that one can achieve much higher compression with
this standard but at the cost of some data. The small difference in color is invisible to
human eye and JPEG standard works on that concept. If detail analysis of the image
pixel-by-pixel is done, it would be realized that the amount of data being lost is high.
Although the data is lost, it can be controlled by setting parameters. One can keep
decreasing size of the file, by reducing the number of bits until it is not affecting the
quality of image. The image should be recognized well on the receiver side.
There is one mode called progressive mode, which handles real time image
transmission. In multiple scans, DCT co-efficients are sent. Depending on each scan, we
can get more compressed image. This mode is a hierarchical mode, which is used for an
7
image at multi resolution. Images with resolution like 1024X1024 and 2048X2048, are
stored as a difference with next small size images. There were many extensions
introduced to JPEG. Quantization is one of them. This method is most popular these days
for image compression. It is easy to separate most important and less important parts of
the image with this method. To code them, it is necessary to pay high attention to
important part and less attention to less important parts.
[5]
2.3.2 Introduction to image standard JPEG 2000
JPEG 2000 is a result of extended study performed on JPEG standard for
improvements. Although studies are being done since 1996, the first paper on JPEG 2000
was published in December 2000. The Committee’s initial aim was to introduce these
concepts of high bit rate and better quality. Later scalability and Region of interest
concept were introduced. This makes it even more powerful. It can compress data with
low bit rate but without losing and the data image can be recovered. That is the reason it
is called the lossless compression [6].
Following are the steps involved in JPEG 2000:

Better efficiency in compression of data can be achieved.

There are more chances of lossless compression.

Different output with different resolution can be decoded.

Desired bit rate with this method can be achieved.

Division of image in to different steps so that we can take use of all of them.

Region of interest extraction.
8

Very high noise resilience improvement.

We can achieve access to any bit rate to get access of that particular image. File
format is more flexible.
This standard enables reuse of image created once in many other applications.
The biggest advantage is that one does not need to recompress it. It has the same stream
of bits. If a server leaves some data for a use to its consumers, then they can customize
same image differently [7].
2.4 Two algorithms DCT and Wavelets used in JPEG and JPEG 2000 respectively
2.4.1 Wavelet based image processing
Wavelet is a lossless algorithm, implemented on JPEG 2000 standard, which
analyzes images in a recursive style. Users can get a series of images and with
improvement at every level. It is almost similar to DCT, except we get a better resolution
at the end. The first step in this process is DWT (Discrete Wavelet Transform). After that,
the steps performed are quantization and encoding. Decompression is done by doing all
this steps in reverse order.
9
Figure 2.4.1(a) Steps involved in Wavelet based compression [8]
Another advantage of this type of image processing is that we can view image at
different resolution level.
Figure 2.4.1(b) DWT on each tile in an image
With the help of DWT, low-resolution coefficient can be calculated. This
calculation is known as a tree structure. We can calculate that by equation given below:
……………[9]
10
Where aj,k is discrete wavelet transform of f(t). Scaling of the image
depends on variable k and shape of the wavelet is dependent of variable j.
Figure 2.4.1(c) Wavelet transform on an image at different level [10]
The purpose of wavelet transform is to get maximum number of zero or near to
zero pixel values. As shown in above figure (a), the original image on which wavelet
transform needs to be performed is given. Now, if the threshold value is high, we get a
low-resolution image. This will convert all the pixel values to zero, whose magnitude
level is less. Image (c) is a reconstructed image given with inverse wavelet transform. To
find the error between two images, we mainly use two parameters. They are MSE (Mean
square error) and PSNR (Peak signal to noise ratio) .They both are given by the equations
below.
MSE =
PSNR = 10
…………………………[10]
…………………………………[10]
11
Then Mean Square error (MSE) is one of the different ways to find the value of
difference between two things with estimation. So, in general MSE of an estimator A’
with respect to the estimated parameter A is defined as
MSE (A’) = E ((A’-A) 2)……………………………[8]
In addition, PSNR (Pick Signal to Noise Ratio) is a ratio between maximum
power of a signal and power of corrupted noise. These signals generally have a very long
range, hence a logarithmic scale is used. It is widely used standard for checking image
quality in image processing.
2.4.2 DCT based image processing
This technique is widely used in image processing. Even though it is lossy it
provides very high compression rate. It uses the ideas from Discrete Fourier Transform
(DFT) and Fast Fourier Transform (FFT). The main idea behind this algorithm is to
convert the signal in to frequency domain. DCT in two dimensions is given by equation
given below
Figure 2.4.2(a): Equation to calculate DCT in 2D
Here 1 D row transform is performed on rows and then on columns. This makes 2
Dimension transform separable. IDCT is a same process but in reverse order. Typical 8x8
image block has two 64 2 D functions. In which are generated by multiplying 8 bits of 1
12
D array to another. Each sets represent, horizontal and vertical frequencies respectively.
From both of the dimensions, coefficient with frequency value zero is called DC
coefficient. Others are known as AC coefficient. It is needed to make the value of the
coefficients of lower frequency near DC component. They can be removed, as they are
not important as high frequency values. The human eye is less sensitive to high frequency
value [4].
2.4.2.1 Quantization in DCT
It always requires more space to store an original image that is uncompressed. If
the image on which DCT needs to be performed is of 8 bit. The output can be of 11 bits
range can vary from [-1024, 1023]. The main advantage with quantization is, division to
DCT matrix is done and it stores only integer values. These values are very small. The
higher the value of quantization, the more data one lose. Technically, it is a method to
reduce the number of bits to store an image .
Q=
Figure2.4.2.1(a) Quantization matrix for DCT [12]
13
This matrix is designed in such a way that it has corresponding value for each
element of the image matrix. One should encrypt the essential data with low value of step
size and less important value with big step size.
2.4.2.2 Zigzag scanning
After quantization, encoding of that information is necessary and zigzag scan is
the method for that. In this method, low frequency elements are scanned first,followed by
high frequency elements.
Figure 2.4.2.2(a) Path followed in zigzag scan
When compression on an image is performed, one tries to make most of the bits either
zero or near to zero. To encode the bits we have to put all the information, which is
similar to each other, together. This scan follows the scan path as shown in the diagram.
The scan selectsthe value with highest value and then lowest value. To get all the zero
values together, RLE method is done.
14
Chapter 3
MATLAB MODEL OF DCT AND WAVELET IMAGE PROCESSING
3.1 Matlab representation of DCT based signal processing
The main aim of this project was to show the difference between DCT based
image processing and Wavelet based image processing. The first thing I did was
implement DCT based image compression and decompression on an image. I took same
image to do the wavelet transform too.
Figure 3.1.1: Original test image of sunview
I used inbuilt functions of matlab to implement this algorithm. First, I converted
that image into Ycbcr format. That was the first step of the compression. The next step
was to extract the Y component of that image. Results are shown below in figure.
15
Figure 3.1.2 YCbCr image result
Figure 3.1.3 Y componant extraction of an image
16
Now we want to verify on the receiver side, that are we getting the same
reconstructed image, back on receiver side back to check that we perform the IDCT
(Inverse Discrete Cosine Transform). The logic was implemented again in matlab and
results are as below.
Figure 3.1.4 Quantized Y componant of an image
17
Figure 3.1.5 Reconstructed YCbCr image
Figure 3.1.6 Reconstructed RGB image
18
If we see this image from our eye we may not see the difference between original
image and reconstructed image. If you see the difference image given above you can
clearly see the amount of data we are loosing. This is the main advantage of the
compression using this meathod we are loosing some amount of data here, but we can
achieve high compression ratio.
Figure 3.1.7 Error image
19
3.2 Wavelet based signal processing
Now, the next step was to implement wavelet-based image processing. From the
results, it is seen that the difference between PSNR and image quality between two
methods is noticeable. Initially, the region of interest is accessed from where we want to
do the compression. After that, the method of wavelet should be selected. Here, in this
case it is DWT. Then RLE should be performed, this can significantly reduce the number
of bits.
Figure 3.2.1 Input image for wavelet based image compression
Figure 3.2.2 Processed image
20
Figure 3.2.3 Reconstructed image
In wavelet based image compression, the predefined matrix is used to do the
multiplication with rows and columns. Here the average and difference, between two
adjacent pixels are calculated. These will give us most of the pixel values close to zero
and that is the main purpose. It has been seen that pixels next to each other do not have
much difference in values so the final matrix we get always has very small number value.
W1=(1/1.41)
Figure 3.2.4 Matrix for wavelet based image compression [14]
21
The concept of averaging and differencing explains the concept of filter. When
we do averaging that means we are doing filtering of high frequency data. It acts as a low
pass filter when it is used to smoothen the data [14]. In the same way, differencing
represents high pass filter that stops low frequency signals. It can be best method to find
noise from the signal because the noise is a high frequency signal [14]. Combining both,
we call it filter bank. Different features of signals as background, noise, edges correspond
to different frequencies. This is the main idea behind the wavelet based image-processing
concept.
22
Chapter 4
WAVELET IMPLEMENTATION USING C LANGUAGE
Figure 4.1.1 TI DAVINCI signal processing board
I was working with Professor Dr. Jing Pang ion Texas Instrument’s
TMS320DM6437 DVDP platform for different signal processing application. It comes
with the software Code Composer Studio. Some sample code that explains use of this
platform came with the software. The thing here is that this software supports only C
language and some related languages. Our main idea behind this project is to write the
code for wavelet based image compression using C family language. Therefore, I
designed a code using C and C# to follow the same algorithm that can be used on this
platform. This platform is proven a high performance processor TMS320C64x family for
signal processing. This processor consumes less power and cost wise it is affordable
compared to other processors with same functions.
23
4.1 Components on hardware and interface
This is the best platform to work with especially when someone is looking for a
platform, which is best suited for both image, and video application. Also, some of the
best features of this kit is TMS320C64x
TM
DSP core are operating frequency of 600
MHz, 128 KB L2 cache and 32 KB L1 cache. This is the block diagram of the platform
shown in diagram.
Figure 4.1.2 Block diagram for TI DAVINCI signal processing board
In the project, we captured one image from a sample video preview program,
which was transmitting video from camera to television. We tried to extract Y component
from that image.
4.2 Wavelet based image processing using C and C#
The algorithm was implemented using same theory we did in matlab but the
problem was to show the result to verify it. C does not have anything with which one can
display the image and see the processed result. Therefore, for that we took help of one of
24
my friend who is in computer science department he knows C#. He help us developed
some forms to open an image. We implement that algorithm on image and saw the result
using C# to verify that. The implementation was so perfect we achieved the very good
ratio using that algorithm. We also have included the functions like how many level of
compression we want and what should be the threshold level entered by user. That gave
this code good flexibility and range I can show to others. We calculated SNR same way
we have calculated in Matlab.
Figure 4.2.1 Image used to do wavelet processing using C#
25
I took this beautiful image to implement my algorithm using C and C#. It is the
same algorithm I implemented using matlab but the reason behind this is that Dr. Pang
wants to implement these processing steps on TI DSP platform I explained earlier and
this is going to be a huge benefit in that area.
In this algorithm there is a for loop which can be run multiple times depending on
user. One can run that loop same number of times we want to achieve the level. Here are
some image results I have included to show the 2 level compression and 3 level
compression.
Figure 4.2.2 Wavelet based image processing 2 level
26
Figure 4.2.3 Wavelet based image compression 3 level
From both the images and results it is been seen that threshold is inversely
proportional to image quality. As we increase the threshold value, we can see the value of
SNR is reduced. SNR as discussed measure of quality of image compare to noise in
signal.
The main function used here in design is function DWT(). In this function I have
used a wavelet matrix. The beauty of this matrix is that if one does a multiplication of
that matrix with our image matrix then it is automatically taking an average and a
difference of adjacent pixels. Therefore, after the matrix multiplication as shown in above
example, compressed image can be achieved. In this case, both original and compressed
images are shown the result was displayed on Television.
27
Figure 4.2.4 Input image for wavelet transform on TI’s DSP board
Figure 4.2.5 Output image after first level of wavelet based image compression on TV
28
From the output result, the first level of wavelet compression was achieved on
TI’s board and output can clearly see with the edges in the result image. The edge
detection is an essential part of a wavelet based image compression and it is been proved
by the results shown here.
29
Chapter 5
CONCLUSION AND FUTURE WORK
The major idea behind this project was to show a difference between the DCT
based image compression and a wavelet based image compression. The concept was
implemented in software language C/C++, so that the logic can be implemented on a
Texas Instrument’s DSP board. I have successfully implemented, both DCT based and
wavelet-based algorithm in MATLAB. By viewing the SNR for both the DCT and
wavelet based image compression, a clear quality difference between them can be
noticed. It can be observed that wavelet can give better SNR as high as 20% in
comparison to DCT. In addition, wavelet based image compression is best for edge
detection and due to level based compression, it is also flexible. We can use any level to
do the processing. Reliability has helped the Police department to use this algorithm for a
finger print reader. In hospitals to check images this algorithm is been used. At both these
places accuracy and quality is very essential.
PSNR achieved by DCT based compression is 34. Code can be found below in
appendix to verify the result. PSNR calculated by wavelet based processing method is 64.
Hence, from the results it can be inferred that quality in received image after wavelet
based processing is higher than DCT.
A logic based on above concept was developed using C language and C#. This can be
very useful for future reference. Dr. Pang is working on a TI’s DSP board
TMS320DM6437 that only supports C and C++ language. In addition, with this
algorithm one can achieve a higher level of compression.
30
APPENDIX A
Matlab model for DCT based processing
clc;
clear all;
close all;
%Part (a)
RGB=imread('flower.jpg');
Ycbcr=rgb2ycbcr(RGB);
figure(1);
imshow(RGB);
title('Original Image');
%To get 4:2:0 format of YCbCr
for cols =(1:2:800)
for rows =(1:2:600)
YCbCr(rows + 1 , cols + 1 , 2) = 0;
end
end
for cols =(1:2:800)
for rows =(1:2:600)
YCbCr(rows + 1, cols + 1 , 3) = 0;
end
end
figure(2);
imshow(Ycbcr);
title('YCbCr Image');
y1=Ycbcr(:,:,1);
cb=Ycbcr(:,:,2);
cr=Ycbcr(:,:,3);
pDCTy=@dct2;
pDCTcb=@dct2;
pDCTcr=@dct2;
DCTy = blkproc ( y1, [8 8], pDCTy);
DCTcb = blkproc ( cb, [8 8], pDCTcb);
DCTcr = blkproc ( cr, [8 8], pDCTcr);
DCTy = fix(DCTy);
DCTcb = fix(DCTcb);
DCTcr = fix(DCTcr);
figure(3);
//read image name flower.jpg
//extract y component from image
//display the color image
//we took 800x600 pixel example
31
imshow(DCTy);
title('Y Component Image');
%Part (b)
%Quantize the DCT image by using the JPEG luminance and chrominance
I=imread('flower.jpg');
y=rgb2ycbcr(I);
y1=y(:,:,1);
%Quantizer Matrix
Y_quantizer=[16 11 10 16 24 40 51 61;
12 12 14 19 26 58 60 55;
14 13 16 24 40 57 69 56;
14 17 22 29 51 87 80 62;
18 22 37 56 68 109 103 77;
24 35 55 64 81 104 113 92;
49 64 78 87 103 121 120 101;
72 92 95 98 112 100 103 99];
Cbr_quantizer=[17 18 24 47 99 99 99 99;
18 21 26 66 99 99 99 99;
24 26 56 99 99 99 99 99;
47 66 99 99 99 99 99 99;
99 99 99 99 99 99 99 99;
99 99 99 99 99 99 99 99;
99 99 99 99 99 99 99 99;
99 99 99 99 99 99 99 99];
pDCTy = @dct2;
pDCTcb=@dct2;
pDCTcr=@dct2;
y1=y(:,:,1);
cb=y(:,:,2);
cr=y(:,:,3);
DCTy = blkproc ( y1, [8 8], pDCTy);
DCTcb = blkproc ( cb, [8 8], pDCTcb);
DCTcr = blkproc ( cr, [8 8], pDCTcr);
DCTy = fix(DCTy);
DCTy_temp1=DCTy(17:24,1:8);
DCTy_temp2=DCTy(17:24,9:16);
%Quantization of DCT image.
quantized_Y = blkproc(DCTy,[8 8],'x./P1',Y_quantizer);
quantized_cb=blkproc(DCTcb,[8,8],'x./P1',Cbr_quantizer);
32
quantized_cr=blkproc(DCTcr,[8,8],'x./P1',Cbr_quantizer);
%Rounding the values.
quantized_Y=round(quantized_Y);
quantized_Y1=round(quantized_Y);
quantized_cb=round(quantized_cb);
quantized_cr=round(quantized_cr);
quantized_Y_temp=quantized_Y1(17:24,1:16);
%Display 1st two blocks of 3rd Row
quantized_Y_temp1=quantized_Y1(17:24,1:8)
quantized_Y_temp2=quantized_Y1(17:24,9:16)
figure(4);
imshow(quantized_Y1);
title('Quantized Y Component');
figure(5);
subplot(1,2,1), subimage(quantized_Y_temp1);
title('1st Block of the 3rd Row');
subplot(1,2,2), subimage(quantized_Y_temp2);
title('2nd Block of the 3rd Row');
dcy=[];
for i=1:8:600
for j=1:8:800
dcy=[dcy quantized_Y(i,j)];
end;
end;
fun3=zigzac(quantized_Y_temp) ;
fun2=fun3(1:63)
% zigzac function call.
figure(6);
imshow(fun2);
%Inverse quantisation
y_invq = blkproc(quantized_Y,[8 8],'x.*P1',Y_quantizer);
cb_invq = blkproc(quantized_cb,[8 8],'x.*P1',Cbr_quantizer);
cr_invq = blkproc(quantized_cr,[8 8],'x.*P1',Cbr_quantizer);
y_invq_temp=y_invq(17:24,1:16);
% Inverse DCT transform
idc=@idct2;
Invdct_y=blkproc(y_invq,[8,8],idc);
33
Invdct_cb=blkproc(cb_invq,[8,8],idc);
Invdct_cr=blkproc(cr_invq,[8,8],idc);
% Create structure of unsigned integer datatype
Invdct_y=uint8(Invdct_y);
Invdct_cb=uint8(Invdct_cb);
Invdct_cr=uint8(Invdct_cr);
Invdct_y_temp=Invdct_y(17:24,1:16);
Reycbcr(:,:,1)=Invdct_y;
Reycbcr(:,:,2)=Invdct_cb;
Reycbcr(:,:,3)=Invdct_cr;
figure(7);
imshow(Reycbcr)
title('Reconstructed YCbCr image');
%To find the error image
ReRGB=ycbcr2rgb(Reycbcr);
figure(8);
imshow(ReRGB);
title('Reconstructed RGB image');
error=RGB-ReRGB;
figure(9);
imshow(error);
title('The Error image');
error
error_temp=error(17:24,1:16);
%To find the PSNR for the luminance component
rergb=ReRGB(:,:,1);
rgb=RGB(:,:,1);
mean=sum(sum((rergb-rgb).^2));
mse_Y=((1./(600.*800)).*mean)
% Peak value of teh RGB image is 255
Peak=255;
X=255./mse_Y;
PSNR=10*log(X)
34
APPENDIX B
C model for wavelet based image compression on hardware
/*
* ======== video_preview.c ========
*
*/
/* runtime include files */
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdarg.h>
/* BIOS include files */
#include <std.h>
#include <gio.h>
#include <tsk.h>
#include <trc.h>
/* PSP include files */
#include <psp_i2c.h>
#include <psp_vpfe.h>
#include <psp_vpbe.h>
#include <fvid.h>
#include <psp_tvp5146_extVidDecoder.h>
/* CSL include files */
#include <soc.h>
#include <cslr_sysctl.h>
/* BSL include files */
#include <evmdm6437.h>
#include <evmdm6437_dip.h>
/* Video Params Defaults */
#include <vid_params_default.h>
// IMAGE PROCESSING HEADER FILES
//#include "img_sobel.h"
//#include "img_yc_demux_le16.h"
35
/* This example supports either PAL or NTSC depending on position of JP1 */
#define STANDARD_PAL 0
#define STANDARD_NTSC 1
#define FRAME_BUFF_CNT 6
static int read_JP1(void);
static CSL_SysctlRegsOvly sysModuleRegs = (CSL_SysctlRegsOvly
)CSL_SYS_0_REGS;
// User Defined Function
void display (void * currentFrame);
void haar (void * currentFrame);
void dwt();
//float y[720 * 480];
//float z[720 * 480];
//float my_image[480][720];
float I_temp[480][720];
float O_temp[480][720];
//float a[720];
float w[480][480];
float wa[480][480];
float wt[480][480];
/*
* ======== main ========
*/
void main() {
printf("Video Preview Application\n");
fflush(stdout);
/* Initialize BSL library to read jumper switches: */
EVMDM6437_DIP_init();
/* VPSS PinMuxing */
/* CI10SEL - No CI[1:0]
/* CI32SEL - No CI[3:2]
*/
*/
36
/* CI54SEL - No CI[5:4]
*/
/* CI76SEL - No CI[7:6]
*/
/* CFLDSEL - No C_FIELD
*/
/* CWENSEL - No C_WEN
*/
/* HDVSEL - CCDC HD and VD enabled
*/
/* CCDCSEL - CCDC PCLK, YI[7:0] enabled
*/
/* AEAW
- EMIFA full address mode
*/
/* VPBECKEN - VPBECLK enabled
*/
/* RGBSEL - No digital outputs
*/
/* CS3SEL - LCD_OE/EM_CS3 disabled
*/
/* CS4SEL - CS4/VSYNC enabled
*/
/* CS5SEL - CS5/HSYNC enabled
*/
/* VENCSEL - VCLK,YOUT[7:0],COUT[7:0] enabled */
/* AEM
- 8bEMIF + 8bCCDC + 8 to 16bVENC */
sysModuleRegs -> PINMUX0 &= (0x005482A3u);
sysModuleRegs -> PINMUX0 |= (0x005482A3u);
/* PCIEN = 0: PINMUX1 - Bit 0 */
sysModuleRegs -> PINMUX1 &= (0xFFFFFFFEu);
sysModuleRegs -> VPSSCLKCTL = (0x18u);
return;
}
/*
* ======== video_preview ========
*/
void video_preview(void) {
FVID_Frame *frameBuffTable[FRAME_BUFF_CNT];
FVID_Frame *frameBuffPtr;
GIO_Handle hGioVpfeCcdc;
GIO_Handle hGioVpbeVid0;
GIO_Handle hGioVpbeVenc;
int status = 0;
int result;
int i;
int standard;
int width;
int height;
/* Set video display/capture driver params to defaults */
PSP_VPFE_TVP5146_ConfigParams tvp5146Params =
37
VID_PARAMS_TVP5146_DEFAULT;
PSP_VPFECcdcConfigParams
vpfeCcdcConfigParams =
VID_PARAMS_CCDC_DEFAULT_D1;
PSP_VPBEOsdConfigParams vpbeOsdConfigParams =
VID_PARAMS_OSD_DEFAULT_D1;
PSP_VPBEVencConfigParams vpbeVencConfigParams;
standard = read_JP1();
/* Update display/capture params based on video standard (PAL/NTSC) */
if (standard == STANDARD_PAL) {
width = 720;
height = 576;
vpbeVencConfigParams.displayStandard =
PSP_VPBE_DISPLAY_PAL_INTERLACED_COMPOSITE;
}
else {
width = 720;
height = 480;
vpbeVencConfigParams.displayStandard =
PSP_VPBE_DISPLAY_NTSC_INTERLACED_COMPOSITE;
}
vpfeCcdcConfigParams.height = vpbeOsdConfigParams.height = height;
vpfeCcdcConfigParams.width = vpbeOsdConfigParams.width = width;
vpfeCcdcConfigParams.pitch = vpbeOsdConfigParams.pitch = width * 2;
/* init the frame buffer table */
for (i=0; i<FRAME_BUFF_CNT; i++) {
frameBuffTable[i] = NULL;
}
/* create video input channel */
if (status == 0) {
PSP_VPFEChannelParams vpfeChannelParams;
vpfeChannelParams.id = PSP_VPFE_CCDC;
vpfeChannelParams.params =
(PSP_VPFECcdcConfigParams*)&vpfeCcdcConfigParams;
hGioVpfeCcdc =
FVID_create("/VPFE0",IOM_INOUT,NULL,&vpfeChannelParams,NULL);
status = (hGioVpfeCcdc == NULL ? -1 : 0);
}
/* create video output channel, plane 0 */
38
if (status == 0) {
PSP_VPBEChannelParams vpbeChannelParams;
vpbeChannelParams.id = PSP_VPBE_VIDEO_0;
vpbeChannelParams.params =
(PSP_VPBEOsdConfigParams*)&vpbeOsdConfigParams;
hGioVpbeVid0 =
FVID_create("/VPBE0",IOM_INOUT,NULL,&vpbeChannelParams,NULL);
status = (hGioVpbeVid0 == NULL ? -1 : 0);
}
/* create video output channel, venc */
if (status == 0) {
PSP_VPBEChannelParams vpbeChannelParams;
vpbeChannelParams.id = PSP_VPBE_VENC;
vpbeChannelParams.params = (PSP_VPBEVencConfigParams
*)&vpbeVencConfigParams;
hGioVpbeVenc =
FVID_create("/VPBE0",IOM_INOUT,NULL,&vpbeChannelParams,NULL);
status = (hGioVpbeVenc == NULL ? -1 : 0);
}
/* configure the TVP5146 video decoder */
if (status == 0) {
result = FVID_control(hGioVpfeCcdc,
VPFE_ExtVD_BASE+PSP_VPSS_EXT_VIDEO_DECODER_CONFIG,
&tvp5146Params);
status = (result == IOM_COMPLETED ? 0 : -1);
}
/* allocate some frame buffers */
if (status == 0) {
for (i=0; i<FRAME_BUFF_CNT && status == 0; i++) {
result = FVID_allocBuffer(hGioVpfeCcdc, &frameBuffTable[i]);
status = (result == IOM_COMPLETED && frameBuffTable[i] != NULL ? 0 : -1);
}
}
/* prime up the video capture channel */
if (status == 0) {
FVID_queue(hGioVpfeCcdc, frameBuffTable[0]);
FVID_queue(hGioVpfeCcdc, frameBuffTable[1]);
FVID_queue(hGioVpfeCcdc, frameBuffTable[2]);
}
39
/* prime up the video display channel */
if (status == 0) {
FVID_queue(hGioVpbeVid0, frameBuffTable[3]);
FVID_queue(hGioVpbeVid0, frameBuffTable[4]);
FVID_queue(hGioVpbeVid0, frameBuffTable[5]);
}
/* grab first buffer from input queue */
if (status == 0) {
FVID_dequeue(hGioVpfeCcdc, &frameBuffPtr);
}
/* loop forever performing video capture and display */
while (status == 0 ) {
/* grab a fresh video input frame */
FVID_exchange(hGioVpfeCcdc, &frameBuffPtr);
haar ((frameBuffPtr->frame.frameBufferPtr));
dwt();
display((frameBuffPtr->frame.frameBufferPtr));
/* display the video frame */
FVID_exchange(hGioVpbeVid0, &frameBuffPtr);
}
}
void haar (void * currentFrame)
{
int r,c;
int offset;
offset = 1;
for(r = 0; r < 480; r++)
{
for(c = 0; c < 720; c++)
{
I_temp[r][c] = * (((unsigned char * )currentFrame + offset) );
offset = offset + 2;
}
40
}
}
void dwt ()
{
int i,j,k;
for (i=0;i<480;i++)
{
for (j=0;j<240;j++)
{
if (j==(i+1)*2 -1 || j==(i+1)*2)
w[i][j]=0.5;
else
w[i][j]=0;
}
for(j=240;j<480;j++)
{
if (j==((i+1)-240)*2 -1)
w[i][j]=-(0.5);
else if (j==((i+1)-240)*2)
w[i][j]=0.5;
else
w[i][j]=0;
}
/*
for(j=0;j<480;j++)
wt[j][i]=w[i][j];*/
}
for (i=0;i<480;i++)
{
for(j=0;j<480;j++)
{
wa[i][j]=0;
for(k=0;k<480;k++)
wa[i][j]+=1.41*w[i][k]*w[k][j];
}
}
}
/*
for (i=0;i<480;i++)
{
41
for(j=0;j<480;j++)
{
I_temp[i][j]=0;
for(k=0;k<480;k++)
I_temp[i][j]+=2*wa[i][k]*wt[k][j];
}
}*/
//**********************************************************************
void display (void * currentFrame)
{
int r,c;
int offset;
offset = 1;
for(r = 0; r < 480; r++)
{
for(c = 0; c < 480; c++)
{
* (((unsigned char * )currentFrame + offset) ) = wa[r][c];
offset = offset + 2;
* (((unsigned char * )currentFrame)+ offset) = 0x80;
offset = offset + 2;
}
}
for(r = 0; r < 480; r++)
{
for(c = 480; c < 720; c++)
{
* (((unsigned char * )currentFrame + offset) ) = 0;
offset = offset + 2;
* (((unsigned char * )currentFrame)+ offset) = 0x80;
offset = offset + 2;;
}
}
}
/*
* ======== read_JP1 ========
* Read the PAL/NTSC jumper.
*
* Retry, as I2C sometimes fails:
*/
42
static int read_JP1(void)
{
int jp1 = -1;
while (jp1 == -1) {
jp1 = EVMDM6437_DIP_get(JP1_JUMPER);
TSK_sleep(1);
}
return(jp1);
}
43
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