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Contemporary issues in IT. Lecture 1 Monday Lecture 10:00 – 12:00, Room 3.27 Lab 13:00 – 15:00, Lab 6.12 and 6.20 Lecturer : Dr Abir Hussain Room 633, [email protected]. Lecture contents. Introduction to image compression Image compression measures. Image compression system. - PowerPoint PPT Presentation
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Lecture 1
Contemporary issues in IT
Lecture 1Monday
Lecture 10:00 – 12:00, Room 3.27Lab 13:00 – 15:00, Lab 6.12 and 6.20
Lecturer: Dr Abir HussainRoom 633, [email protected]
Lecture contents
Introduction to image compression Image compression measures. Image compression system. Image compression methods.
Recommendation
Digital compression of still images and videos, by Roger Clarke, Academic press, 1995
Image compression introductions and basic concepts on the following web site: www.cms.livjm.ac.uk/cmsahus1
Introduction Images are very important representative
objects. The application of image compression for
transmission purposes is limited by real-time considerations.
the application of image compression for storage purposes is less strict.
There are two types of compression methods, lossless and lossy image compression
Introduction The application of image compression has
widened and its benefits are far from being counted digital computers in printing publishing and video production in television or satellite transmission video conferencing facsimile transmission of printed material graphics sensing images obtained from
reconnaissance aircraft archiving of medical images
Introduction There are three classical approaches to image
compression In the fist approach, the compression is performed by
removing the redundancy in the image data (example predictive coding)
The second approach of image compression is the one that aims at reducing the number of coefficient of the transformed image parameters while preserving the energy (transform coding, JPEG)
The final classical approach of image compression divides the image into nonoverlapped blocks, transforms the image blocks into one-dimensional vectors, which are subsequently quantised (vector quantisation)
Image compression measures
compression ratio and defined by:
mean-square error defined for an size image by
Compressed
OriginalR n
nC
M
1i
N
1j
2
rms )j,i(S)j,i(SNM
1e
Image compression measures
Another form of fidelity measure that depends on the mean square error is the signal to noise ratio (SNR)
dB
data image theof peak value tolog10
2
10
rmse
PeakSNR
Redundant Information
There are three types of redundant data that can be identified and removed by a digital image compression algorithm. coding redundancy interpixel redundancy psychovisual redundancy
Image compression system Image compression systems consist of two
parts, the encoder and the decode
Source encoder
Channel encoder
Channel Source decoder
Channel decoder
Encoder Decoder
f(i,j) f(i,j) ^
Image compression system
^ f(i,j) Channel Symbol
decoder Inverse mapper
Mapper Quantiser Symbol encoder f(i,j) Channel
Source encoder (a)
Source decoder (b)
Image compression methods two methods can be used, lossless and
lossy image compression techniques. In lossless image compression, the quantiser is
not utilised at the encoder and the aim of the compression is to reduce coding and interpixel redundancies.
lossy image compression methods use the correlation among the pixel data and the properties of the visual process to reduce the interpixel and psychovisual redundancies
Lossless image compression
Lossless image compression methods can provide compression ratios of 2 to 10 and they can be applied to both grey level and binary images Huffman coding Arithmetic coding Lossless predictive coding
Huffman coding Huffman coding was introduced by Huffman
in 1952. The coding process starts by examining the
probabilities of different grey levels in the image.
These probabilities are tabulated in a descending order with the highest probability at the top and the lowest probability at the bottom.
The two lowest probabilities are added together and the order of the probabilities is reorganised in a descending order for the proceeding process.
Huffman coding The next stage in Huffman coding is to
assign to the two remaining probabilities the binary symbols 0 and 1.
Then, we go backwards and assign to the two joined probabilities in the previous stage the symbol of the next stage plus the binary symbols 0 and 1 assigned to each probability.
Such process is repeated until the first stage of the process is achieved
ExampleSymbol Probability 1 2 3 4 5
a1 0.4 0.4 0.4 0.4 0.4 0.5
a2 0.2 0.2 0.2 0.2 0.3 0.4
a3 0.1 0.1 0.12 0.18 0.2
a4 0.08 0.08 0.1 0.12
a5 0.05 0.07 0.08
a6 0.04 0.05
a7 0.03
Arithmetic coding
Arithmetic coding can be used to minimise coding redundancy in the image data.
It outperforms Huffman coding The basic idea of arithmetic coding is
as simple as Huffman coding.
Arithmetic coding
Initially, the range of the input message is specified between 0 and 1.
Each probability is represented by a two end interval, the left end is closed while the right end is open
Arithmetic coding
The next step in the coding is to look at the message, since the first appearing symbol will limit the range of the message according to its specified interval.
Arithmetic coding Suppose that the current message is specified
in the interval Suppose that the range of the present
incoming symbol is [Qa1, Qa2), this means that the new range of the message is
)high,low[ oldold
2oldoldnew
1oldoldnew
Qarangelowhigh
Qarangelowlow
Example
Consider the message a2, a1, a3, a4, with the initial probability interval between 0 and 1.
Symbol Probability Initial Subinterval
a1 0.4 [0.0, 0.4)
a2 0.3 [0.4, 0.7)
a3 0.2 [0.7,0.9)
a4 0.1 [0.9, 1.0)
Example..
a4 a3 a2 a1
1 0.9
0.7
0.4
0
a4 a3 a2 a1
0.7
0.4
a4 a3 a2 a1
0.52
0.4
a4 a3 a2 a1
0.508
0.484
a4 a3 a2 a1
0.508
0.5056
Lossless predictive coding
In lossless predictive image compression approach, the interpixel redundancies are removed by predicting the current pixel value
using closely spaced pixel values and generating new values for coding.
The new values represent the error generated from the subtraction of the predicted value from the original value
Lossless predictive coding
Predictor Nearest integer
Symbol encoder
Input image
Sn
-
+ en Compressed
image
(a)
Compressed image
Symbol decoder
en
Predictor
Decompressed image
(b)
Sn
Sn ^
Sn ^ +
+
Practical
In today’s lab, we will have a look at various compressed images and compare them with the uncompressed images
Various techniques will be used Standard and medical images will be
used in the lab.
Summary
In today’s lecture, we gave an introduction to image compression
We studied various lossless image compression methods
In tomorrow’s lecture, we will go through the concept of lossy image compression techniques.