1. Lecture 1 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, a.hussain@ljmu.ac.uk

2. Lecture contents  Introduction to image compression  Image compression measures.  Image compression system.  Image compression methods.

3. 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

4. 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

5. 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

6. 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)

7. Image compression measures  compression ratio and defined by:  mean-square error defined for an size image by

8. Image compression measures  Another form of fidelity measure that depends on the mean square error is the signal to noise ratio (SNR)

9. 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

10. Image compression system  Image compression systems consist of two parts, the encoder and the decode

11. Image compression system

12. 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

13. 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

14. 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.

15. 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

16. Example SymbolProbability12345 a1a1 0.4 0.5 a2a2 0.2 0.30.4 a3a3 0.1 0.120.180.2 a4a4 0.08 0.10.12 a5a5 0.050.070.08 a6a6 0.040.05 a7a7 0.03

17. 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.

18. 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

19. 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.

20. 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

21. Example  Consider the message a2, a1, a3, a4, with the initial probability interval between 0 and 1. SymbolProbabilityInitial Subinterval a1a1 0.4[0.0, 0.4) a2a2 0.3[0.4, 0.7) a3a3 0.2[0.7,0.9) a4a4 0.1[0.9, 1.0)

22. Example..

23. 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

24. Lossless predictive coding

25. 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.

26. 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.