1. SEMINAR REPORT ON
IMAGE COMPRESSION
Submitted in partial fulfillment for the award of the Degree of
Master of Computer Application
from
Biju Pattnaik University of Technology, Rourkela
By:- Group(3rd
) MCA 4th
Semester:--
PARDIP KUMAR
Under the guidance of
K. MOHANTA
Department of MCA
PURUSHOTTAM INSTITUTE OF ENGINEERING & TECHNOLOGY,
ROURKELA
2010
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2. ACKNOWLEDGEMENT
Contents:--
1.Overview
2.What is image compression?
3.Why do we need image compression?
4.Terminology used in image compression
5.Entropy
6.Compression system model
7.Algorithms for image is compressed
8.Different coding method used in image
compression
9.Some common image formats
10. Conclusion
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3. OVERVIEW
*Image Compression is the Image data Elaboration dedicated to the
image data representation.
*It analyzes the techniques allowing to reduce the amount of data to
describe the information content of the image.
*Data transfer rates are not keeping up due to bandwidth limitation and
bottlenecks in existing systems.
*Image compression allows faster retrieval from disk and makes use of
advances in CPU speed.
*Image compression is intranet friendly.
*Image compression has important applications in image storage and
delivery.
* Therefore, with scalable image compression, it is possible to browse
large images quickly and on demand.
*image compression makes possible the adjustment of image quality and
resolution after the photo is shot, so that instead, the original digital
photos always can be shot at the highest possible quality and resolution,
and when the camera memory is filled to capacity, the compressed
bitstream of existing shots may be truncated to smaller size to leave
room for the upcoming shots
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4. WHAT IS IMAGE COMPRESSION?
Image compression is the application of data compression on digital
images. In effect, the objective is to reduce redundancy of the image
data in order to be able to store or transmit data in an efficient form.
A chart showing the relative quality of various jpg settings and also
compares saving a file as a jpg normally and using a "save for web"
technique
Image compression can be lossy or lossless. Lossless compression is
sometimes preferred for medical imaging, technical drawings, icons or
comics. This is because lossy compression methods, especially when
used at low bit rates, introduce compression artifacts. Lossless
compression methods may also be preferred for high value content, such
as medical imagery or image scans made for archival purposes. Lossy
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5. methods are especially suitable for natural images such as photos in
applications where minor (sometimes imperceptible) loss of fidelity is
acceptable to achieve a substantial reduction in bit rate. The lossy
compression that produces imperceptible differences can be called
visually lossless.
Why do we need compression?
Requirements may outstrip the anticipated increase of storage space and
bandwidth.
The figures in Table 1 show the qualitative transition from simple text to full-
motion video data and the disk space, transmission bandwidth, and transmission
time needed to store and transmit such uncompressed data.
Table 1 Multimedia data types and uncompressed storage space, transmission
bandwidth, and transmission time required. The prefix kilo- denotes a factor of
1000 rather than 1024.
Multimedia Data Size/Duration
Bits/Pixel or
Bits/Sample
Uncompressed
Size
(B for bytes)
Transmission
Bandwidth
(b for bits)
Transmission
Time (using a
28.8K Modem)
A page of text 11'' x 8.5'' Varying resolution 4-8 KB 32-64 Kb/page 1.1 - 2.2 sec
Telephone quality speech 10 sec 8 bps 80 KB 64 Kb/sec 22.2 sec
Grayscale Image 512 x 512 8 bpp 262 KB 2.1 Mb/image 1 min 13 sec
Color Image 512 x 512 24 bpp 786 KB 6.29 Mb/image 3 min 39 sec
Medical Image 2048 x 1680 12 bpp 5.16 MB 41.3 Mb/image 23 min 54 sec
SHD Image 2048 x 2048 24 bpp 12.58 MB 100 Mb/image 58 min 15 sec
Full-motion Video
640 x 480, 1 min
(30 frames/sec)
24 bpp 1.66 GB 221 Mb/sec 5 days 8 hrs
The examples above clearly illustrate the need for sufficient storage space, large
transmission bandwidth, and long transmission time for image, audio, and video
data. At the present state of technology, the only solution is to compress
multimedia data before its storage and transmission, and decompress it at the
receiver for play back. For example, with a compression ratio of 32:1, the space,
bandwidth, and transmission time requirements can be reduced by a factor of 32,
with acceptable quality.
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6. Terminology used in image compression
It refers relation between original image and the compressed file.
(1). Compression Ratio:--A large number implies a better compression.
Ex Image 256X256 pixels, 256 level grayscale can be compressed file
size 6554 byte.
Original Image Size = 256X256(pixels) X 1(byte/pixel)
= 65536 bytes
(2). Bit Per Pixel:--A smaller number implies a better compression.
Bit Per Pixel = (No. of Bits)/(No. of Pixels)
Ex Image 256X256 pixels, 256 level grayscale can be compressed file
size 6554 byte.
Original Image Size = 256X256(pixels) X 1(byte/pixel)
= 65536 bytes
Compressed file = 6554(bytes)X8(bits/pixel)
= 52432 bits
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RC
SizeFileCompressed
SizeFileedUncompress
RationCompressio ==
10
6554
65536
≈=Rationcompressio
8.0
65536
52432
≈=PixelperBits

7. Entropy
Entropy encoding which is a way of lossless compression that is done on an
image after the quantization stage. It enables to represent an image in a more
efficient way with smallest memory for storage or transmission In most images
their neighboring pixels are correlated and thus contain redundant information. Our
task is to to find less correlated representation
of the image, then perform redundancy reduction and irrelevancy reduction.
Redundancy reduction removes duplication from the signal source (for instance a
digital image). Irrelavancy reduction omits parts of the signal that will not be
noticed by the Human Visual System(HVS).
Entropy encoding further compresses the quantized values in lossless manner
which gives better compression in overall. It uses a model to accurately determine
the probabilities for each quantized value and produces an appropriate code based
on these probabilities so that the resultant output code stream will be smaller than
the input stream.
An entropy encoder further compresses the quantized values losslessly to
give better overall compression. It uses a model to accurately determine the
probabilities for each quantized value and produces an appropriate code based on
these probabilities so that the resultant output code stream will be smaller than the
input stream. The most commonly used entropy encoders are the Huffman encoder
and the arithmetic encoder, although for applications requiring fast execution,
simple run-length encoding (RLE) has proven very effective.
• Average information in an image.
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Compression system model consist of two main parts one is compression
and another is Decompression.
In compression the process is mainly divided in input, preprocessing,
encoding and compressed file respectively. First image is inputed then processing
is done by using the terminology. In encoding process we use Run length coding
and Huffman coding and image is finally compressed.
In Decompression system the process is just converse of the compression.
Here compressed image is introduced first ,than it is decoded and postprocessing is
done. Finally output is obtained.
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9. Algorithms for image is compressed
The image compression algorithms can be divided into two branches:
- Lossless algorithms :- The information content is not
modified .
- Lossy algorithms:-The information content is reduced and it
is not recoverable.
Lossless compression algorithm
The emergence of multimedia technologies has made digital libraries a
reality. Nowadays, libraries, museums, film studios, and governments
are converting more and more data and archives into digital form. Some
of the data indeed need to be stored without any loss. In this algorithm
many coding techniques that will effectively reduce the total number of
bits needed to represent the above information. The process involved is
generally referred to as compression. The intermediate medium could
either be data storage or a communication/ computer network. If the
compression and decompression process induce no information loss, the
compression scheme is lossless; otherwise it is lossy.
A general data compression scheme in lossless algorithm
Input output
data data
If the total number of bits required to represent the data before
compression is B0 and the total number of bits required to represent the
data after compression is B1, then we define the compression ratio as
compression ratio =B0/B1
the compression ratio is much larger than 1.0. the higher the
compression ratio, the better the lossless compression scheme, as long as
it is computational feasible.
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Encoder
(compression
)
Storage
or
networks
Decoder
(decompression
)

10. Lossy Algorithm
Image compression is in multimedia application, where a higher
compression ratio is required , lossy methods are usually adopted.
Lossless compression ratio technique is low when the image histogram
is relatively flat. For this we have to use lossy compression. In lossy
compression , the compress image is usually not the same as the original
image but is meant to form a close approximation to the original image
perceptually. To quantitatively describe how close the approximation is
to original data, some form of distortion measure is required.
Lossless vs. Lossy compression:
In lossless compression schemes, the reconstructed image, after
compression, is numerically identical to the original image. However
lossless compression can only a achieve a modest amount of
compression. An image reconstructed following lossy compression
contains degradation relative to the original. Often this is because the
compression scheme completely discards redundant information.
However, lossy schemes are capable of achieving much higher
compression. Under normal viewing conditions, no visible loss is
perceived (visually lossless).
Compression Methods used for image compression
Lossy/Lossless data compression in Image Processing try to
eliminate the spatial redundancies
Example of coding techniques are :-
Huffman coding:-
This method was first introduced by David A. Huffman in a 1952, this was
attracted an overwhelming amount of research and has been adopted in many
important and/or commercial application, such as fax machines, JPEG, and MEPG.
The encoding steps of the Huffman coding described in bottom-up manner.
Step 1:---------------------------------------------------------------------
1. Sort the gray levels by decreasing probability.
2. Add the two smallest probabilities.
3. Sort the new value into the list.
4. Repeat until only two probabilities remain.
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11. Step 2:---------------------------------------------------------------------
1. Give the code 0 to the highest probability, and the code 1
to the lowest probability in the present node.
2. Go backwards through the tree and add 0 to the highest and 1
to the lowest probability in each node until all gray levels have a
unique code
Example Huffman coding :-------------------------------------------
Huffman code of original image
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12. Example of Huffman coding
Example of Huffman coding
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13. The Huffman code
• The Huffman code results in unambiguous code.
• The code is reversible without loss.
• The table for the translation of the code has to be stored together
with the coded image.
• The Huffman code does not take correlation between adjacent
pixels into consideration.
Run-length coding
Instead of assuming a memoryless source, run-length
coding(RLC) exploits memory present in the information source.
It is one of the simplest forms of the data compression. The basic
13

14. idea is that if the information source we wish to compress has the
property that symbols tend to form continuous groups, instead of
coding each symbol in the group individually, we can code one
such symbol and the length of the group.
• E.g.,
56 56 56 82 82 82 83 80
56 56 56 56 56 80 80 80
creates the run-length code (56, 3)(82, 3)(83, 1)(80, 4)(56, 5).
• The code is calculated row by row.
• Very efficient coding for binary data.
• Important to know position, and the image dimensions must be
stored with the coded image Used in most fax machines
Example of Run-length coding:------------------------------------------
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15. Run-length coding
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16. • Compression Achieved
• Original image requires 3 bits per pixel (in total - 8x8x3=192 bits).
• Compressed image has 29 runs and needs 3+3=6 bits per
run (in total - 174 bits or 2.72 bits per pixel).
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17. Some common image formats
• JPEG Joint Photographic Experts Group - Exists in many different versions
but is always some kind of transformation coding. JPEG is not reversible
due to quantification.
The JPEG standard supports numerous modes(variations).
Some of the commonly used ones are:
• Sequential Mode
• Progressive Mode
• Hierarchical Mode
• Lossless Mode
Sequential Mode
This is the default JPEG mode. Each gray-level image or color image
component is encoded in a single left- to-right, top-to-bottom scan. We
implicitly assume this mode in the discussion so far. The ”Motion JPEG” video
code uses Baseline Sequential JPEG, applied to each image frame in the video.
Progressive Mode
Progressive JPEG Mode delivers low quality versions of the image
quickly, followed by higher quality passes, and has become widely supported in
web browser s. such multiple scans of images are of course most useful when
the speed of the communication line is low. In Progressive Mode, the first few
scans carry only a few bits and deliver a rough picture what is to follow. After
each additional scan, more data is received, and image quality is gradually
enhanced.
Hierarchical Mode
Hierarchical JPEG Mode encodes the image in a hierarchy of
several different resolutions. The encoded image at the lowest resolution is
basically compressed low-pass-filtered image, whereas the images at
successively higher resolutions provide additional details.
Three level hierarchical JPEG encoder is:-
1. Reduction of image resolution
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18. 2. Compress low-resolution image f4
3. Compress difference image d2 & d1
Three level hierarchical JPEG coder
1. Decompress the encoded low-resolution image F4
2. Restore image f2 at the intermediate resolution
3. Restore image f at the original resolution
Lossless Mode
Lossless JPEG is a very special case of JPEG which indeed has no
loss in its image quality. It is rarely used, since its compression ratio is very low
compared to other, lossy modes. On the other hand, it meets a special need, and
the newly developed JPEG-LS standard is specifically aimed at lossless image
compression.
4-step process are used in JPEG Image Compression
1. Transforming RGB to YIQ/YUV, separating intensity from color
2. Identifying redundant data using a Discrete Cosine Transformation(DCT)
3. Quantizing remaining data
4. Encoding the result to minimize storage requirements
Example of JPEG compression
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Start_of__mag

19. MPEG
The Moving Pictures Experts Group (MPEG) was established in 1988 to create
standard for delivery of digital video and audio. Membership grew from about 25
experts in 1988 to a community of more than 350, from about 200 companies and
organization. It is appropriately recognized that proprietary interests need to be
maintained within the family of MPEG standards.
MPEG introduces a third frame type- B-frames – and their accompanying
bidirectional motion compression. If both directional is successful, two motion
vector will be sent, and the two corresponding matching macroblocks are averaged
before comparing to the target macroblock for generating the prediction error.. the
MPEG encoder and decoder cannot work for any macroblock fro B-frame without
its succeeding P or I-frame, the actual coding and transmission order is different
from the display order of the video .
Conclusion
• Image compression is important.
• Image compression has come a long way.
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20. • Image compression is nearly mature, but there is always room for
improvement.
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