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Wavelet Video Processing Technology Seminar Report ‘03
INTRODUCTION
Uncompressed multimedia data requires considerable storage capacity and
transmission bandwidth. Despite rapid progress in mass storage density processor
speeds and digital communication system performance, demand for data storage
capacity and data transmission bandwidth continues to outstrip the capabilities of
available technologies. The recent growth of data intensive multimedia-based
web applications have not only sustained the need for more efficient ways to
encode signals and images but have made compression of such signals central to
storage and communication technology.
For still image compression, the joint photographic experts group (JPEG)
standard has been established. The performance of these codes generally
degrades at low bit rates mainly because of the underlying block-based Discrete
cosine Transform (DCT) scheme. More recently, the wavelet transform has
emerged as a cutting edge technology, within the field of image compression.
Wavelet based coding provides substantial improvements in picture quality at
higher compression ratios. Over the past few years, a variety of powerful and
sophisticated wavelet based schemes for image compression have been
developed and implemented. Because of the many advantages, the top contenders
in JPEG-2000 standard are all wavelet based compression algorithms.
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Wavelet Video Processing Technology Seminar Report ‘03
IMAGE COMPRESSION
Image compression is a technique for processing images. It is the
compressor of graphics for storage or transmission. Compressing an image is
significantly different than compressing saw binary data. Some general purpose
compression programs can be used to compress images, but the result is less than
optimal. This is because images have certain statistical properties which can be
exploited by encoders specifically designed for them. Also some finer details in
the image can be sacrificed for saving storage space.
Compression is basically of two types.
1. Lossy Compression
2. Lossless Compression.
Lossy compression of data concedes a certain loss of accuracy in
exchange for greatly increased compression. An image reconstructed following
lossy compression contains degradation relative to the original. Often this is
because the compression scheme completely discards redundant information.
Under normal viewing conditions no visible is loss is perceived. It proves
effective when applied to graphics images and digitized voice.
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Wavelet Video Processing Technology Seminar Report ‘03
Lossless compression consists of those techniques guaranteed to generate
an exact duplicate of the input data stream after a compress or expand cycle.
Here the reconstructed image after compression is numerically identical to the
original image. Lossless compression can only achieve a modest amount of
compression. This is the type of compression used when storing data base
records, spread sheets or word processing files.
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Wavelet Video Processing Technology Seminar Report ‘03
IMAGE COMPRESSION SYSTEM
A typical lossy image compression system consists of three closely
connected components namely.
(a) Source encoder
(b) Quantizer
(c) Entropy encoder
FIGURE 1 IMAGE COMPRESSION SYSTEM
Source encoder
This is a linear transformer in which the given signal or image is
transformed to a different domain. Compression using wavelet transforms
belongs to a class of technique called transform coding. The objectives of
transform coding are
I) To create a representation for the data in which there is less correlation
among the coefficient values. This called decorrelating the data.
II) To have a representation in which it is possible to quantize different
coordinates with different precision.
The other two components are discussed later.
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SourceEncoder Quantizer Entropy
encoder
Input
signal/image
Compressed
signal/ image
Wavelet Video Processing Technology Seminar Report ‘03
STEPS IN COMPRESSION
The usual steps involved in compressing an image are.
1. Specifying the rate (bits available) and distortion (tolerable error)
parameters for the target image.
2. Dividing the image data into various classes, based on their importance.
3. Dividing the available bit budget among these classes such that the
distortion is a minimum.
4. Quantize each class separately using the bit allocation information.
5. Encode each class separately using an entropy coder and write to the file.
Bit allocation
The first step in compressing an image is to segregate the image data in to
different classes. Depending on the importance of the data it contains, each class
is allocated a portion of the total bit budget, such that the compressed image has
the minimum possible distortion. Then procedure is called bit allocation.
The Rate Distortion theory is often used for solving the problem of
allocating bits to a set of classes, or for bit rate control in general. The theory
aims at reducing the distortion for a given target bit rate, by optimally allocating
bits to the various classes of data. One approach to solve the problem of optimal
bit allocation using Rate Distortion theory is explained below.
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Wavelet Video Processing Technology Seminar Report ‘03
1. Initially, all classes are allocated a predefined maximum numbers of bits.
2. For each class, one bit is reduced from its quota of allocated bits, and the
distortion due to the reduction of that one bit is calculated.
3. Of all the classes, the class with minimum distortion for a reduction of 1
bit is noted, and 1 bit is reduced from its quota of bits.
4. The total distortion for all classes D is calculated.
5. The total rate for all the classes is calculated as R = p (i) * B (i), where p is
the probability and B is the bit allocation for each class.
6. Compare the target rate and distortion specifications with the values
obtained above. If not optimal, go to step 2.
Here we keep on reducing one bit at a time till we achieve optimality
either or distortion or target rate, or both.
Classifying image data
An image is represented as a two dimensional array of coefficients, each
coefficient representing the brightness level in that point. When looking from a
higher perspective, the coefficients cannot be differentiated as more important
one, and lesser important one. But most natural images have smooth colour
variations, with the fine details being represented as sharp edges in between the
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Wavelet Video Processing Technology Seminar Report ‘03
smooth variations. Technically, the smooth variations in colour can be termed as
low frequency variations and the sharp variations as high frequency variations.
The low frequency components constitute the base of an image and the
high frequency components add upon them to refine the image thereby giving a
detailed image. Hence the smooth variations are demanding more importance
than the details.
Separating the smooth variations and details of the image can be done in
many ways. One such way is the decomposition of the image using Discrete
Wavelet Transform (DWT).
DWT of an image
A low pass filter and a high pass filter are chosen, such that they exactly
halve the frequency range between themselves. The filter pass is called the
analysis filter pair. First the low pass filter is applied for each row of data,
thereby getting the low frequency components of the row. But since the low pass
filter is a half band filter, the output data contains frequencies only in the first
half of the original frequency range. So they can be subsampled by two, so that
the output data now contains only half the original number of samples. Now the
high pass filter is applied for the same row of data, and similarly the high pass
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Wavelet Video Processing Technology Seminar Report ‘03
components are separated and placed by the side of the low pass components.
This procedure is done for all rows.
Next, the filtering is done for each column of the intermediate data. The
resulting two dimensional array of coefficients contains four bands of data, each
labeled as LL(low- Low), HL (high-low), LH (Low-High) and HH (High-High).
The LL band can be decomposed once again in the same manner, thereby
producing even more subbands. This can be done up to any level, thereby
resulting in a pyramidal decomposition as shown.
The LL band at the highest level can be classified as most important and
the other detail bands can be classified as of lesser importance, with the degree of
importance decreasing from the top of the pyramid to the bands at the bottom.
FIGURE 2
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LL
LH
HL
HH LH HH
HL
LL HL
HHLH
HHLH
HL
HL
HHLH
LH
HL
HH
LL
Single level decomposition
Two level decomposition Three level decomposition
Wavelet Video Processing Technology Seminar Report ‘03
Inverse DWT of an image.
Just as a forward transform is used to separate the image data into various
classes of importance a reverse transform is used to reassemble the various
classes of data into a reconstructed image. A pair of high pass and low pass filters
is used here also. Then filter pair is called the synthesis filter pair. The filtering
procedure is just the opposite. We start from the topmost level, apply the filters
coloumnwise first and then rowwise and proceed to the next level, till we reach
the first level.
Quantization
Quantization refers to the process of approximating the continuous set of
values in the image data with a finite set of values. The input to a quantizer is the
original data, and the output is always one among a finite number of levels. The
quantizer is a function whose set of output values are discrete, and usually finite.
Obviously, this is a process of approximation, and as good quantizer is one which
represents the original signal with minimum loss or distortion.
A quantizer can be specified by its input partitions and output levels. If the
input range is divided into levels of equal spacing, then the quantizer is termed as
a uniform quantizer, and if not, it is termed as a non-uniform quantizer. A
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Wavelet Video Processing Technology Seminar Report ‘03
uniform quantizer can be easily specified by its lower bound and step size. Also,
implementing a uniform quantizer is easier than a non-uniform quantizer.
In a uniform quantizer, if the input falls between n*r and (n=1)*r, the
quantizer out put the symbol n.
FIGURE 3 UNIFORM QUANTIZER
Just the same way a quantizer partitions its input and outputs discrete
levels, a dequantizer is one which receives the output levels of a quantizer and
converts them into normal data, by translating each level into a reproduction
point in the actual range of data.
The optimum quantizer (encoder) and optimum dequantizer (decoder)
must satisfy the following conditions.
Given the output levels or partitions of the encoder, the best decoder is one
that puts the reproduction parts x1 on the centers of mass of the partitions.
This is known as centered condition.
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| X | X | X | X | X |
(n-2)r (n-1)r nr (n+1)r (n+2)r (n+3)r
n-2 n-1 n n+1 n+2 output
input
Wavelet Video Processing Technology Seminar Report ‘03
Given the reproduction points of the decoder, the best encoder is one that puts
the partition boundaries exactly in the middle of the reproduction points i.e.,
each x is translated to its nearest reproduction point. This is known as nearest
neighbor condition.
The quantization error (x-x1) is used as a measure of the optimality of the
quantizer and dequantizer.
Entropy coding
After the data has been quantized in to a finite set of values, it can be
encoded using an entropy coder to give additional compression. Entropy means
the amount of information present in the data, and an entropy coder encodes the
given set of symbols with the minimum number of bits required to represent
them.
Two of the most popular entropy coding schemes are Huffman coding and
Arithmetic coding.
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Wavelet Video Processing Technology Seminar Report ‘03
CHIP PROVIDES WAVELET TRANSFORMS
Analog Devices have developed a family of general purpose wavelet-
codec chips. The latest chip, ADV6OLIC, claims to accommodate compression
ratios from visually lossless to as great as 350-to-1. Figure below shows the
architecture of the chip.
FIGURE 4 ARCHITECTURE OF ADV601LC
In wavelet-based compression processing, the silicon area needed for
compression is the same as the area needed for decompression. In contrast, other
compression techniques require more work and special circuitry to compress than
to decompress a signal.
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Digital video
1/0 post
Wavelet filters,
decimator and
interpolator
Adaptive quantizer
Run length coder
Huffman coder
Host 1/0 post and
FIFO
Dram manager
On chip transform
buffer
Digital Component
Video I/O
Host
DRAM
Wavelet Video Processing Technology Seminar Report ‘03
The ADV60ILC accepts component digital video through its video
interface and delivers a compressed video stream through its host interface in
encode mode .In decode mode, the IC accepts a compressed bit stream through
its host interface and delivers component digital video through its video
interface.TheADV60ILC compresses images by filtering the video into 42
separate frequency bands. The chip then optimizes each band to include only
frequencies the naked eyes can discern. Because the eye lacks sensitivity at high
frequencies, this is no reason to compress and store this information.
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Wavelet Video Processing Technology Seminar Report ‘03
ADVANTAGES
Wavelet video processing technology offers some enticing features
1. The high image compression ratios reduces the hard disk storage capacity
for real time recording and for archival storage
2. it has higher resolution than DCT based JPEG and MPEG
3. it facilitates efficient post processing to even further compress the already
–compressed images for archival storage.
4. In magnification mode images can be enlarged almost to infinity without
the pixelation effects that accompany linear zooms.
5. The compressed video file cannot be edited
6. Because wavelet transforms compress the entire frame, any change makes
it impossible to decompress the image. This aspect is important for
courtroom evidence.Wavelet processing captures every image and creates
a mathematical map of the entire image from which it can be determined
whether the image has undergone alternations
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Wavelet Video Processing Technology Seminar Report ‘03
APPLICATIONS
1. JPEG2000 uses wavelet transforms to compress images
2. MPEG-4 uses wavelet tiling to allow the division of images into several
tiles, each with separate encoding
3. Kallix corp. uses wavelet technology in to video surveillance systems
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Wavelet Video Processing Technology Seminar Report ‘03
CONCLUSION
Wavelet-based coding provides substantial improvement in picture quality
at low bit rates because of overlapping bases function and better energy
compaction property of wavelet transforms. Because of the inherent multi
resolution nature wavelet based codes facilitate progressive transmission of
images thereby allowing variable bit rates. The JPEG-2000 standard incorporates
wavelet technology. Interesting issues like obtaining accurate models of images,
optimal representations of such models and rapidly computing such optimal
representation are the grand challenges facing the data compression community.
Interaction of harmonic analysis with data compression, joint source channel
coding, image coding based on models of human perception, scalability
robustness, error resilience, and complexity are a few of the many outstanding
challenges in image coding to be fully resolved and may affect image data
compression performance in the years to come.
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Wavelet Video Processing Technology Seminar Report ‘03
BIBLIOGRAPHY
1. Bill Travis, “Wavelets both implode and explode images”, EDN,
December 2000
2. Raghuveer.M.Rao and Ajit.S.Bopardikar, “Wavelet Transforms,
Introduction to theory and applications”, Pearson Education Asia.
3. Jaideva.C.Goswami and Andrew.K.Chan,”Fundamentals of
wavelets,theory,algorithms and application”, Wiley Interscience
Publication.
4. Chan.Y.T,”Wavelet basics”, Kluwer Academic Publishers.
5. http:/engineering.rowan.edu/~polikar/WAVELETS/WTtutorial.html
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Wavelet Video Processing Technology Seminar Report ‘03
ABSTRACT
The biggest obstacle to the multimedia revolution is digital obesity. This is
the blot that occurs when pictures, sound and video are converted from their
natural analog form into computer language for manipulation or transmission. In
the present explosion of high quality data, the need to compress it with less
distortion of data is the need of the hour. Compression lowers the cost of storage
and transmission by packing data into a smaller space.
One of the hottest areas of advanced form of compression is wavelet
compression. Wavelet Video Processing Technology offers some alluring
features, including high compression ratios and eye pleasing enlargements.
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Wavelet Video Processing Technology Seminar Report ‘03
CONTENTS
1. INTRODUCTION
2. IMAGE COMPRESSION
3. IMAGE COMPRESSION SYSTEM
Steps in compression
Bit allocation
Classifying image data
DWT of an image
Inverse DWT of an image
Quantization
Entropy coding
4. CHIP PROVIDES WAVELET TRANSFORMS
5. ADVANTAGES
6. APPLICATIONS
7. CONCLUSION
8. BIBLIOGRAPHY
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Wavelet Video Processing Technology Seminar Report ‘03
ACKNOWLEDGEMENT
I extend my sincere gratitude towards Prof. P.Sukumaran Head of
Department for giving us his invaluable knowledge and wonderful technical
guidance.
I express my thanks to Mr. Muhammed Kutty our group tutor and also
to our staff advisor Ms. Biji Paul for their kind co-operation and guidance for
preparing and presenting this seminar.
I also thank all the other faculty members of AEI department and my
friends for their help and support.
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