# Relative Data Redundancy

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1 Image Compression

2 Relative Data Redundancy Let b and b denote the number of bits in two representations of the same information, the relative data redundancy R is R = 1-1/C C is called the compression ratio, defined as C = b/b e.g., C = 10, the corresponding relative data redundancy of the larger representation is 0.9, indicating that 90% of its data is redundant 4/14/2014 2

3 Why do we need compression? Data storage Data transmission 4/14/2014 3

4 How can we implement compression? Coding redundancy Most 2-D intensity arrays contain more bits than are needed to represent the intensities Spatial and temporal redundancy Pixels of most 2-D intensity arrays are correlated spatially and video sequences are temporally correlated Irrelevant information Most 2-D intensity arrays contain information that is ignored by the human visual system 4/14/2014 4

5 Examples of Redundancy 4/14/2014 5

6 Coding Redundancy The average number of bits required to represent each pixel is L1 L l( r ) p ( r ) 0.25(2) 0.47(1) 0.24(3) 0.03(3) 1.81bits avg k r k k0 8 C R 11/ /14/2014 6

7 Spatial and Temporal Redundancy 1. All 256 intensities are equally probable. 2. The pixels along each line are identical. 3. The intensity of each line was selected randomly. 4/14/2014 7

8 Spatial and Temporal Redundancy 1. All 256 intensities are equally probable. 2. The pixels along each line are identical. 3. The intensity of each line was selected randomly. Run-length pair specifies the start of a new intensity and the number of consecutive pixels that have that intensity. Each 256-pixel line of the original representation is replaced by a single 8-bit intensity value and length 256 in the run-length representation. The compression ratio is ( ) 8 128:1 4/14/2014 8

9 Irrelevant Information / :1 4/14/2014 9

10 Measuring Image Information A random event E with probability P(E) is said to contain 1 I ( E) log -log P( E) PE ( ) units of information. 4/14/

11 Measuring Image Information Given a source of statistically independent random events from a discrete set of possible events { a, a,..., a } with associated probabilities { P( a ), P( a ),..., P( a )}, the average information per source output, called the entropy of the source a j J H - P( a )log P( a ) j1 J is called source symbols. Because they are statistically independent, the source called zero memory source. j j J 4/14/

12 Measuring Image Information If an image is considered to be the output of an imaginary zero-memory "intensity source", we can use the histogram of the observed image to estimate the symbol probabilities of the source. The intensity source's entropy becomes r L1 H - pr( rk)log pr( rk) k 0 p ( r ) is the normalized histogram. k 4/14/

13 Measuring Image Information For the fig.8.1(a), H [0.25log log log log 0.03] bits/pixel /14/

14 Fidelity Criteria Let f ( x, y) be an input image and of f ( x, y). The images are of size M N. f ( x, y) be an approximation The root - mean - square error is M1N1 1 erms f ( x, y) f ( x, y) MN x0 y0 2 1/2 4/14/

15 Fidelity Criteria The mean - square signal -to - noise ratio of the output image, denoted SNR ms SNR M1N1 x0 y0 ms M1N1 2 x0 y0 f ( x, y) f ( x, y) f ( x, y) 2 4/14/

16 RMSE = 5.17 RMSE = RMSE = /14/

17 Image Compression Models 4/14/

18 Image Compression Standards 4/14/

19 4/14/

20 4/14/

21 4/14/

22 Some Basic Compression Methods: Huffman Coding 4/14/

23 Some Basic Compression Methods: Huffman Coding The average length of this code is L 0.4*1 0.3* 2 0.1*3 0.1* *5 0.04*5 avg = 2.2 bits/pixel 4/14/

24 Some Basic Compression Methods: Golomb Coding Given a nonnegative integer n and a positive integer divisor m 0, the Golomb code of n with respect to m, denoted G ( n), constructed as follows: m Step 1. Form the unary code of quotient n/ m (The unary code of integer q is defined as q 1s followed by a 0) k Step2. Let k= log 2 m, c 2 m, r n mod m,and compute truncated remainder r ' such that r truncated to k -1 bits 0 r c r ' r c truncated to k bits otherwise Step 3. Concatenate the results of steps 1 and 2. 4/14/

25 Some Basic Compression Methods: Golomb Coding Step 1. Form the unary code of quotient n/ m (The unary code of integer q is defined as q 1s followed by a 0) k Step2. Let k= log 2 m, c 2 m, r n mod m, and compute truncated remainder r ' such that r truncated to k -1 bits 0 r c r ' r c truncated to k bits otherwise G 4 (9) : 9 / 4 2, the unary code is k= log2 4 2, c 2 4 0, r 9 mod 4 1. r ' 01 Step 3. Concatenate the results of steps 1 and 2. G 4 (9) /14/

26 Some Basic Compression Methods: Golomb Coding Step 1. Form the unary code of quotient n/ m (The unary code of integer q is defined as q 1s followed by a 0) k Step2. Let k= log 2 m, c 2 m, r n mod m, and compute truncated remainder r ' such that r truncated to k -1 bits 0 r c r ' r c truncated to k bits otherwise G 4 (9) : 9 / 4 2, the unary code is k= log2 4 2, c 2 4 0, r 9 mod 4 1. r ' 01 Step 3. Concatenate the results of steps 1 and 2. G 4 (9) /14/

27 Some Basic Compression Methods: Golomb Coding Step 1. Form the unary code of quotient n/ m (The unary code of integer q is defined as q 1s followed by a 0) k Step2. Let k= log 2 m, c 2 m, r n mod m, and compute truncated remainder r ' such that G 4 (7)? r truncated to k -1 bits 0 r c r ' r c truncated to k bits otherwise Step 3. Concatenate the results of steps 1 and 2. 4/14/

28 Some Basic Compression Methods: Arithmetic Coding 4/14/

29 Some Basic Compression Methods: Arithmetic Coding How to encode a 2 a 1 a 2 a 4? 4/14/

30 Decode The length of the message is 5. Since 0.8>code word > 0.4, the first symbol should be a Therefore, the message is a 3 a 3 a 1 a 2 a /14/

31 LZW (Dictionary coding) LZW (Lempel-Ziv-Welch) coding, assigns fixed-length code words to variable length sequences of source symbols, but requires no a priori knowledge of the probability of the source symbols. LZW is used in: Tagged Image file format (TIFF) Graphic interchange format (GIF) Portable document format (PDF) LZW was formulated in /14/

32 The Algorithm: A codebook or dictionary containing the source symbols is constructed. For 8-bit monochrome images, the first 256 words of the dictionary are assigned to the gray levels Remaining part of the dictionary is filled with sequences of the gray levels 4/14/

33 Important features of LZW 1. The dictionary is created while the data are being encoded. So encoding can be done on the fly 2. The dictionary is not required to be transmitted. The dictionary will be built up in the decoding 3. If the dictionary overflows then we have to reinitialize the dictionary and add a bit to each one of the code words. 4. Choosing a large dictionary size avoids overflow, but spoils compressions 4/14/

34 Example: /14/

35 4/14/

36 Some Basic Compression Methods: LZW Coding /14/

37 Decoding LZW Let the bit stream received be: In LZW, the dictionary which was used for encoding need not be sent with the image. A separate dictionary is built by the decoder, on the fly, as it reads the received code words. 4/14/

38 Recognized Encoded value pixels Dic. address Dic. entry /14/

39 Some Basic Compression Methods: Run-Length Coding 1. Run-length Encoding, or RLE is a technique used to reduce the size of a repeating string of characters. 2. This repeating string is called a run, typically RLE encodes a run of symbols into two bytes, a count and a symbol. 3. RLE can compress any type of data 4. RLE cannot achieve high compression ratios compared to other compression methods 4/14/

40 Some Basic Compression Methods: Run-Length Coding 5. It is easy to implement and is quick to execute. 6. Run-length encoding is supported by most bitmap file formats such as TIFF, BMP and PCX 4/14/

41 Some Basic Compression Methods: Run-Length Coding WWWWWWWWWWWWBWWWWWWWWWWWWBBBW WWWWWWWWWWWWWWWWWWWWWWWBWWWW WWWWWWWWWW RLE coding: 12W1B12W3B24W1B14W 4/14/

42 Some Basic Compression Methods: Symbol-Based Coding In symbol- or token-based coding, an image is represented as a collection of frequently occurring sub-images, called symbols. Each symbol is stored in a symbol dictionary Image is coded as a set of triplets {(x1,y1,t1), (x2, y2, t2), } 4/14/

43 Some Basic Compression Methods: Symbol-Based Coding 4/14/

44 Some Basic Compression Methods: Bit-Plane Coding An m-bit gray scale image can be converted into m binary images by bit-plane slicing. These individual images are then encoded using run-length coding. Code the bitplanes separately, using RLE (flatten each plane row-wise into a 1D array), Golomb coding, or any other lossless compression technique. Let I be an image where every pixel value is n-bit long Express every pixel in binary using n bits Form n binary matrices (called bitplanes), where the i-th matrix consists of the i-th bits of the pixels of I. 4/14/

45 Some Basic Compression Methods: Bit-Plane Coding Example: Let I be the following 2x2 image where the pixels are 3 bits long The corresponding 3 bitplanes are: /14/

46 However, a small difference in the gray level of adjacent pixels can cause a disruption of the run of zeroes or ones. Eg: Let us say one pixel has a gray level of 127 and the next pixel has a gray level of 128. In binary: 127 = & 128 = Therefore a small change in gray level has decreased the run-lengths in all the bit-planes! 4/14/

47 GRAY CODE 1. Gray coded images are free of this problem which affects images which are in binary format. 2. In gray code the representation of adjacent gray levels will differ only in one bit (unlike binary format where all the bits can change. 4/14/

48 Let g m-1.g 1 g 0 represent the gray code representation of a binary number. Then: g g i m1 a i a a m1 0 m i1 2 i In gray code: 127 = = /14/

49 Gray Coding To convert a binary number b 1 b 2 b 3..b n-1 b n to its corresponding binary reflected Gray code. Start at the right with the digit b n. If the b n-1 is 1, replace b n by 1-b n ; otherwise, leave it unchanged. Then proceed to b n-1. Continue up to the first digit b 1, which is kept the same since it is assumed to be a b 0 =0. The resulting number is the reflected binary Gray code. 4/14/

50 Examples: Gray Coding Dec Gray Binary /14/

51 4/14/

52 4/14/

53 Decoding a gray coded image The MSB is retained as such,i.e., a i g i a i 0 i m 2 1 a m1 g m1 4/14/

54 Differential Pulse Code Modulation (DPCM) Example: AAABBCDDDD encoded as A Change reference symbol if delta becomes too large Works better than RLE for many digital images 4/14/

55 Block Transform Coding 4/14/

56 Block Transform Coding Consider a subimage of size n n whose forward, discrete transform T( u, v) can be expressed in terms of the relation n1 n1 T ( u, v) g( x, y) r( x, y, u, v) x0 y0 for u, v 0,1,2,..., n -1. 4/14/

57 Block Transform Coding Given T( u, v), g( x, y) similarly can be obtained using the generalized inverse discrete transform n1 n1 g( x, y) T ( u, v) s( x, y, u, v) u0 v0 for x, y 0,1,2,..., n -1. 4/14/

58 Image transform Two main types: -orthogonal transform: e.g. Walsh-Hdamard transform, DCT -subband transform: e.g. Wavelet transform 4/14/

59 Orthogonal transform Orthogonal matrix W c c c c w w w w w w w w w w w w w w w w d d d d C=W.D Reducing redundancy Isolating frequencies 4/14/

60 Block Transform Coding Walsh-Hadamard transform (WHT) 4/14/

61 Block Transform Coding Discrete Cosine Transform (DCT) r( x, y, u, v) s( x, y, u, v) (2x 1) u (2y 1) v ( u) ( v)cos cos 2n 2n where ( u/ v) 1 n 2 n for u/ v 0 for u / v 1, 2,..., n -1 4/14/

62 Example 4/14/

63 In each case, 50% of the resulting coefficients were truncated and taking the inverse transform of the truncated coefficients arrays. RMSE = 2.32 RMSE = 1.78 RMSE = /14/

64 Subimage Size Selection 4/14/

65 Subimage Size Selection 4/14/

66 Bit Allocation The overall process of truncating, quantizing, and coding the coefficients of a transformed subimage is commonly called bit allocation Zonal coding The retained coefficients are selected on the basis of maximum variance Threshold coding The retained coefficients are selected on the basis of maximum magnitude 4/14/

67 RMSE = 4.5 RMSE = 6.5 4/14/

68 Threshold Coding T ( u, v) T ( u, v) round Z ( u, v ) Z Z(0,0) Z 0,1... Z 0, n 1 Z(1,0) Z( n 1,0) Z( n 1, n 1) 4/14/

69 Threshold Coding 4/14/

70 Threshold Coding 19:1 12:1 30:1 49:1 182:1 85:1 4/14/

71 Fact about JPEG Compression JPEG stands for Joint Photographic Experts Group Used on 24-bit color files. Works well on photographic images. Although it is a lossy compression technique, it yields an excellent quality image with high compression rates. 4/14/

72 Fact about JPEG Compression It defines three different coding systems: 1. a lossy baseline coding system, adequate for most compression applications 2. an extended coding system for greater compression, higher precision, or progressive reconstruction applications 3. A lossless independent coding system for reversible compression 4/14/

73 Steps in JPEG Compression 1. (Optionally) If the color is represented in RGB mode, translate it to YUV. 2. Divide the file into 8 X 8 blocks. 3. Transform the pixel information from the spatial domain to the frequency domain with the Discrete Cosine Transform. 4. Quantize the resulting values by dividing each coefficient by an integer value and rounding off to the nearest integer. 5. Look at the resulting coefficients in a zigzag order. Do a runlength encoding of the coefficients ordered in this manner. Follow by Huffman coding. 4/14/

74 Step 1a: Converting RGB to YUV YUV color mode stores color in terms of its luminance (brightness) and chrominance (hue). The human eye is less sensitive to chrominance than luminance. YUV is not required for JPEG compression, but it gives a better compression rate. 4/14/

75 RGB vs. YUV It s simple arithmetic to convert RGB to YUV. The formula is based on the relative contributions that red, green, and blue make to the luminance and chrominance factors. There are several different formulas in use depending on the target monitor. For example: Y = * R * G * B U = * R * G * B +128 V = 0.5 * R * G * B /14/

76 Step 1b: Downsampling The chrominance information can (optionally) be downsampled. The notation 4:1:1 means that for each block of four pixels, you have 4 samples of luminance information (Y), and 1 each of the two chrominance components (U and V). Y Y U, V Y Y 4/14/

77 Step 2: Divide into 8 X 8 blocks Note that with YUV color, you have 16 pixels of information in each block for the Y component (though only 8 in each direction for the U and V components). If the file doesn t divide evenly into 8 X 8 blocks, extra pixels are added to the end and discarded after the compression. The values are shifted left by subtracting /14/

78 Discrete Cosine Transform The DCT transforms the data from the spatial domain to the frequency domain. The spatial domain shows the amplitude of the color as you move through space The frequency domain shows how quickly the amplitude of the color is changing from one pixel to the next in an image file. 4/14/

79 Step 3: DCT The frequency domain is a better representation for the data because it makes it possible for you to separate out and throw away information that isn t very important to human perception. The human eye is not very sensitive to high frequency changes especially in photographic images, so the high frequency data can, to some extent, be discarded. 4/14/

80 Step 3: DCT The color amplitude information can be thought of as a wave (in two dimensions). You re decomposing the wave into its component frequencies. For the 8 X 8 matrix of color data, you re getting an 8 X 8 matrix of coefficients for the frequency components. 4/14/

81 Step 4: Quantize the Coefficients Computed by the DCT The DCT is lossless in that the reverse DCT will give you back exactly your initial information (ignoring the rounding error that results from using floating point numbers.) The values from the DCT are initially floating-point. They are changed to integers by quantization. 4/14/

82 Step 4: Quantization Quantization involves dividing each coefficient by an integer between 1 and 255 and rounding off. The quantization table is chosen to reduce the precision of each coefficient to no more than necessary. The quantization table is carried along with the compressed file. 4/14/

83 Step 5: Arrange in zigzag order This is done so that the coefficients are in order of increasing frequency. The higher frequency coefficients are more likely to be 0 after quantization. This improves the compression of run-length encoding. Do run-length encoding and Huffman coding. 4/14/

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88 Assuming the DC coefficient of the transformed and quantized subimage to its immediate left was -17. The resulting of DPCM difference is [-26 (-17)] = -9. 4/14/

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95 JPEG at bpp (enlarged) 4/14/ C. Christopoulos, A. Skodras, T. Ebrahimi, JPEG2000 (online tutorial)

96 JPEG2000 at bpp 4/14/ C. Christopoulos, A. Skodras, T. Ebrahimi, JPEG2000 (online tutorial)

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