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- 1. Data Compression Presented to: Prof. Dr. Maha Sharkas presented by: Sherif Mohamed 1
- 2. Data Compression Outline 1) 2) 3) 4) 5) 6) Introduction. Types of Data Compression. Lossless Compression. Lossless Algorithms. Lossy Compression. Lossy Algorithms . 2
- 3. Data Compression • It is the process of reducing the amount of data required to represent a given quantity of information. • Save time and help to optimize resources i. In sending data over communication line: less time to transmit and less storage to host. • Reduce length of data transmission time over the network. 3
- 4. Types of Data Compression 1. Lossless Compression. 2. Lossy Compression. 4
- 5. Data Compression Algorithm • Lossless Compression If the compressed data is reconstructed and the original data is obtain without any loss of information then this technique is called lossless compression. 5
- 6. Types of Data Compression • Lossy Compression If the compressed data is reconstructed and the approximation of original data is obtain and loss of information occurs then this technique is called lossy compression. The original message can never be recovered exactly as it was before it was compressed. 6
- 7. Types of Data Compression Lossless Original Data Compressed Data Original Data Approximated 7
- 8. Data Compression Algorithm 8
- 9. Lossless Algorithm • Huffman Encoding There’s no unique Huffman code. Algorithm: ① ② ③ Make a leaf node for each code symbol Add the generation probability of each symbol to the leaf node Take the two leaf nodes with the smallest probability and connect them into a new node Add 1 or 0 to each of the two branches The probability of the new node is the sum of the probabilities of the two connecting nodes If there is only one node left, the code construction is completed. If not, go back to (2) 9
- 10. Huffman Coding • z 10
- 11. Huffman Coding • Encoding • Decoding 11
- 12. Lossless Algorithm • Lempel Ziv Walsh (LZW) Encoding • It is dictionary based encoding • Basic idea: Create a dictionary or a code table of strings used during communication. If both sender and receiver have a copy of the dictionary, then previously-encountered strings can be substituted by their index in the dictionary. • LZW to compress text, executable code and similar data files to about one half their original size. 12
- 13. LZW Coding Have 2 phases: Building an indexed dictionary Compressing a string of symbols • Algorithm: Extract the smallest substring that cannot be found in the remaining uncompressed string. Store that substring in the dictionary as a new entry and assign it an index value Substring is replaced with the index found in the dictionary Insert the index and the last character of the substring into the compressed string 13
- 14. LZW Coding • Lempel Ziv Compression 14
- 15. 15
- 16. LZW Coding • Lempel Ziv Decompression 16
- 17. 17
- 18. Lossless Algorithm • Run Length Encoding • Simplest method of compression. • Replace consecutive repeating occurrences of a symbol by 1 occurrence of the symbol itself, then followed by the number of occurrences. 18
- 19. Run Length Coding • The method can be more efficient if the data uses only 2 symbols (0s and 1s) in bit patterns and 1 symbol is more frequent than another. 19
- 20. Lossy Algorithm • Used for compressing images and video files (our eyes cannot distinguish subtle changes, so lossy data is acceptable). • These methods are cheaper, less time and space. • Several methods: JPEG: compress pictures and graphics MPEG: compress video MP3: compress audio 20
- 21. Lossy Algorithm • JPEG Encoding • Used to compress pictures and graphics. • In JPEG, a grayscale picture is divided into 8x8 pixel blocks to decrease the number of calculations. • Basic idea: Change the picture into a linear (vector) sets of numbers that reveals the redundancies. The redundancies is then removed by one of lossless 21
- 22. JPEG Encoding 22
- 23. JPEG Encoding • DCT(Discrete Cosine Transform) • Image is divided into blocks of 8*8 pixels. • For grey-scale images, pixel is represented by 8-bits. • For color images, pixel is represented by 24-bits or three 8-bit groups. 23
- 24. JPEG Encoding • DCT takes an 8*8 matrix and produces another 8*8 matrix and its values represent by this equation: T[i][j] = 0.25 C(i) C(j) ∑ ∑ P[x][y] Cos(2x+1)iπ/16 * Cos (2y+1)jπ/16 i = 0, 1, …7, j = 0, 1, …7 C(i) = 1/√2, i =0 = 1 otherwise T contains values called ‘Spatial frequencies’ 24
- 25. JPEG Encoding • T[0][0] is called the DC coefficient, related to average values in the array, Cos 0 = 1 • Other values of T are called AC coefficients, cosine functions of higher frequencies Case 1: all P’s are same, image of single color with no variation at all, AC coefficients are all zeros. Case 2: little variation in P’s, many, not all, AC coefficients are zeros. Case 3: large variation in P’s, a few AC coefficients are zeros. 25
- 26. JPEG Encoding 26
- 27. JPEG Encoding P-matrix T-matrix • 20 30 40 50 60 … 720 -182 0 -19 0 … 30 40 50 60 70 -182 0 0 0 0 40 50 60 70 80 0 0 0 0 0 50 60 70 80 90 -19 0 0 0 0 60 70 80 90 100 0 0 0 0 0 … … ‘Uniform color change and little fine detail, easier to compress after DCT’ 27
- 28. JPEG Encoding P-matrix T-matrix • 100 150 50 100 100 … 835 15 -17 59 5… 200 10 110 20 200 46 -60 -36 11 14 10 200 130 30 200 -32 -9 130 105 -37 100 10 90 190 120 59 -3 27 -12 30 10 200 200 120 90 50 -71 -24 -56 -40 …. …. ‘Large color change, difficult to compress after DCT’ 28
- 29. JPEG Encoding • Quantization • Provides an way of ignoring small differences in an image that may not be perceptible. • Another array Q is obtained by dividing each element of T by some number and rounding-off to nearest integer ( The loss is occur in this step) 29
- 30. JPEG Encoding T-matrix Q-matrix • 152 0 -48 0 -8… 15 0 -5 0 -1… 0 0 0 0 0 0 0 0 0 0 -48 0 38 0 -3 -5 0 4 0 0 0 0 0 0 0 0 0 0 0 0 -8 0 -3 0 13 -1 0 0 0 1 … … ‘Divide by 10 and round-off ’ . 30
- 31. JPEG Encoding • Dividing T-elements by the same number is not practical, may result in too much loss. • Retain the effects of lower frequencies as much as possible. • To define a quantization array Q, then Q[i][j] = Round (T[i][j] ÷ U[i][j]), i = 0, 1,.........7, j = 0, 1, …7 31
- 32. JPEG Encoding • U = 1 3 5 7 9… Q= 152 0 -10 0 -1 … 3 5 7 9 11 0 0 0 0 0 5 7 9 11 13 -10 0 4 0 0 7 9 11 13 15 0 0 0 0 0 9 11 13 15 17 -1 0 0 0 0 … … ‘Q can be well compressed due to redundant elements’ 32
- 33. JPEG Encoding • Compression Quantized values are read from the matrix and redundant 0s are removed. To cluster the 0s together, the matrix is read diagonally in an zigzag fashion. The reason is if the matrix doesn’t have fine changes, the bottom right corner of the matrix is all 0s. JPEG usually uses lossless run-length encoding at the compression phase. 33
- 34. JPEG Encoding 34
- 35. JPEG Encoding 8x8 pixel block Discrete Cosine Transform Quantization Run Length, Huffman Encoding to .jpg file 35
- 36. MPEG Encoding Used to compress video. Basic idea: Each video is a rapid sequence of a set of frames. Each frame is a spatial combination of pixels, or a picture. Compressing video = spatially compressing each frame + temporally compressing a set of frames. 36
- 37. MPEG Encoding • Spatial Compression Each frame is spatially compressed by JPEG. • Temporal Compression Redundant frames are removed. Usually little difference from one to the next frame is suitable for compression. {For example, in a static scene in which someone is talking, most frames are the same except for the segment around the speaker’s lips, which changes from one frame to the next}. Three frames: I, P, B. 37
- 38. MPEG Encoding • I (intra-picture) frame: Just a JPEG encoded image. • P (predicted) frame: Encoded by computing the differences between a current and a previous frame. • B (bidirectional) frame: Similar to P-frame except that it is interpolated between previous and future frame. 38
- 39. MPEG Encoding • order in which to transmit: I – P – I P is sandwiched between groups of B P is the difference from the previous I-frame B is interpolated from the nearest I and P order in which to display I – B – B – P – B – B – I 39
- 40. Thank You 40

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