Image Compression. Qiaoyong Zhong. November 19, CAS-MPG Partner Institute for Computational Biology (PICB)

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1 Image Compression Qiaoyong Zhong CAS-MPG Partner Institute for Computational Biology (PICB) November 19, / 53

2 Image Compression The art and science of reducing the amount of data required to represent an image. 2 / 53

3 The central parts of the Milky Way (ESO, 2012) 108, , 500 3/ = 24.71GB 3 / 53

4 Outline 1 Fundamentals 2 Some Basic Compression Methods 3 Digital Image Watermarking 4 / 53

5 Fundamentals Data: the means by which information is conveyed Compression ratio C = b b where b and b are numbers of bits in two representations of the same information. Relative data redundancy R = 1 1 C = 1 b b = b b b e.g. b = 10, b = 1, C = 10, R = / 53

6 Image Data Redundancies Types of image data redundancies: Coding redundancy Spatial and temporal redundancy Irrelevant information 6 / 53

7 Coding Redundancy Given an M N image, r k is a discrete random variable in the interval [0, L 1] to represent the intensities of the image, the probability of r k : p r (r k ) = n k MN k = 0, 1, 2,..., L 1 Then the average number of bits required to represent each pixel is L 1 L avg = l(r k )p r (r k ) k=0 where l(r k ) is the number of bits used to represent r k, which can be a constant (fixed-length code) or variable (variable-length code). 7 / 53

8 Information Theory A random event E with probability P(E) is said to contain units of information. I (E) = log 1 = log P(E) P(E) Given a source of independent random events {a 1, a 2,..., a J }, its entropy is J H = P(a j ) log P(a j ) Entropy of an image: j=1 L 1 H = p r (r k ) log 2 p r (r k ) k=0 8 / 53

9 Information Theory Shannon s first theorem [ Lavg,n lim n n ] = H where L avg,n is the average number of code symbols required to represent all n-symbol groups. It provides a lower bound that can be achieved using variable-length code! 9 / 53

10 Fidelity Criteria Image compression can be lossy or lossless. To estimate the information loss: objective fidelity criteria root-mean-square error e rms = e(x, y) = ˆf (x, y) f (x, y) [ subjective fidelity criteria 1 MN M 1 ] N 1 1/2 e(x, y) 2 x=0 y=1 10 / 53

11 Fidelity Criteria Objective vs. subjective criteria e rms = 5.17, 15.67, for (a), (b), (c) respectively. 11 / 53

12 Image Compression Models Mapper: transforms f (x,... ) into a format designed to reduce spatial and temporal redundancy Quantizer: excludes irrelevant information Symbol coder: e.g. variance-length code 12 / 53

13 Image Formats 13 / 53

14 Outline 1 Fundamentals 2 Some Basic Compression Methods 3 Digital Image Watermarking 14 / 53

15 Huffman Coding Entropy H = 2.14bits/symbol L avg = = 2.2bits/pixel 15 / 53

16 Huffman Coding Variable-length, instantaneous uniquely decodable block codes The source symbols are coded once at a time Used in CCITT, JBIG2, JPEG, MPEG-1,2,4, H.26{1,2,3,4} etc. 16 / 53

17 Golomb Coding Optimal coding of nonnegative geometrically distributed integer inputs P(n) = (1 p)p n Golomb code of n with respect to m, G m (n): 1 Form the unary code of quotient n/m. (The unary code of an integer q is defined as q 1s followed by a 0.) 2 Let k = log 2 m, c = 2 k m, r = nmodm, and compute truncated remainder r such that { r r truncated to k 1 bits 0 r < c = r + c truncated to k bits otherwise 3 Concatenate the results of steps 1 and / 53

18 Golomb Coding Optimal coding of nonnegative geometrically distributed integer inputs P(n) = (1 p)p n Golomb code of n with respect to m, G m (n): 1 Form the unary code of quotient n/m. (The unary code of an integer q is defined as q 1s followed by a 0.) 2 Let k = log 2 m, c = 2 k m, r = nmodm, and compute truncated remainder r such that { r r truncated to k 1 bits 0 r < c = r + c truncated to k bits otherwise 3 Concatenate the results of steps 1 and 2. Example: G 4 (9), 9/4 = 2, unary code is 110, k = log 2 4 = 2, c = 0, r = 1, r = 01, G 4 (9) = / 53

19 Golomb Coding Golomb codes are optimal when log2 (1 + p) m = log 2 (1/p) 18 / 53

20 Golomb Coding M(n) = { 2n n 0 2 n 1 n < 0 19 / 53

21 Golomb Coding - Example 20 / 53

22 Golomb Coding Usually used for the coding of transform of intensities, not for the intensities directly Variable-length, instantaneous uniquely decodable block codes Used in JPEG-LS, AVS 21 / 53

23 Arithmetic Coding The entire sequence of source symbols (message) is assigned a single arithmetic code word. Use an interval between 0 and 1 to represent a source symbol. Starts from [0, 1), as the message extends, the interval becomes smaller and smaller. More number of digits (or bits) are required to represent smaller intervals. Used in JBIG1, JBIG2, JPEG-2000, H.264, MPEG-4 AVC etc. 22 / 53

24 Arithmetic Coding Message a 1 a 2 a 3 a 3 a 4 can be encoded with a subinterval [ , ), or simply / 53

25 LZW Coding Addresses spatial redundancies. Assigns fixed-length code words to variable length source symbols. Builds a dictionary of sequences of source symbols. Used in GIF, TIFF, PDF. 24 / 53

26 LZW Coding - Example 25 / 53

27 LZW Coding - Example A 16-pixel 8-bit image encoded using 10 9-bit codes / 53

28 Run-Length Coding Compresses a simple form of spatial redundancy groups of identical intensities. Represents runs of identical intensities as run-length pairs. Particularly effective for binary images. Used in CCITT, JBIG2, JPEG, M-JPEG, MPEG-1,2,4, BMP etc. 27 / 53

29 Run-Length Coding RLE in BMP encoded mode: two bytes pair, the first byte specifies the number of consecutive pixels that have the intensity contained in the second byte. absolute mode: the first byte is 0, while the second is 28 / 53

30 Symbol-Based Coding Represents an image as a collection of frequently occurring sub-images (symbols). Uses a symbol dictionary to store symbols. The image is coded as a set of triplets {(x 1, y 1, t 1 ), (x 2, y 2, t 2 ),... } Used in JBIG2, binary images only. 29 / 53

31 Symbol-Based Coding 30 / 53

32 Bit-Plane Coding 1 Decompose a multilevel image into a series of binary images (bit planes). 2 Apply run-length coding, symbol-based coding etc. to the bit planes individually. Used in JBIG1, JPEG / 53

Block Transform Coding 1 Divide the image into small non-overlapping blocks of equal size (e.g. 8 8). 2 Apply 2-D transform on the blocks independently.

33 Block Transform Coding 1 Divide the image into small non-overlapping blocks of equal size (e.g. 8 8). 2 Apply 2-D transform on the blocks independently. 3 Quantize the transform coefficients (compression by discarding those with small magnitudes). 4 Encode the retained transform coefficients. Used in JPEG, M-JPEG, MPEG-1,2,4, H.26{1,2,3,4}, DV and HDV, VC-1 etc. 32 / 53

34 Transform selection Forward discrete transform: Inverse discrete transform: n 1 n 1 T (u, v) = g(x, y)r(x, y, u, v) x=0 y=0 n 1 n 1 g(x, y) = T (u, v)s(x, y, u, v) u=0 v=0 r(x, y, u, v) and s(x, y, u, v) are called the forward and inverse transformation kernels respectively. 33 / 53

35 Discrete Fourier transform (DFT): Transform selection r(x, y, u, v) = e j2π(ux+vy)/n s(x, y, u, v) = 1 n 2 ej2π(ux+vy)/n Walsh-Hadamard transform (WHT): r(x, y, u, v) = s(x, y, u, v) = 1 n ( 1) m 1 i=0 b i (x)p i (u)+b i (y)p i (v) Discrete cosine transform (DCT): r(x, y, u, v) = s(x, y, u, v) [ ] [ ] (2x + 1)uπ (2y + 1)vπ = α(u)α(v) cos cos 2n 2n 34 / 53

36 Comparison of transforms Transform selection 50% of the coefficients are truncated, e rms = 2.32, 1.78, 1.13 respectively. 35 / 53

37 Subimage Size Selection The most popular sizes are 8 8 and and DCT performs better than the others. 36 / 53

38 JPEG Defines three coding systems: a lossy baseline coding system, based on the DCT an extended coding system for greater compression, higher precision etc. a lossless independent coding system for reversible compression Support for the baseline system is required to be JPEG compatible. 37 / 53

39 JPEG - Example Compression ratio = 25:1 and 52:1 respectively. 38 / 53

40 Predictive Coding Can be lossless or lossy Used in JBIG2, JPEG, JPEG-LS, MPEG-1,2,4, H.26{1,2,3,4}, HDV, VC-1 etc. 39 / 53

41 Lossless Predictive Coding Prediction error: e(n) = f (n) ˆf (n) 1-D linear prediction function: Intraframe [ m ] ˆf (x, y) = round α i f (x, y i) i=1 Interframe for video compression [ m ] ˆf (x, y, t) = round α i f (x, y, t i) i=1 40 / 53

42 Differential coding Lossless Predictive Coding ˆf (x, y) = f (x, y 1) 41 / 53

43 Interframe prediction Lossless Predictive Coding ˆf (x, y, t) = f (x, y, t 1) 42 / 53

44 Wavelet Coding No need to construct subimages compared with block transform coding Used in JPEG / 53

45 JPEG-2000 Compression ratio = 25, 52, 75, 105 respectively Better than JPEG both in an objective view and subjective view 44 / 53

46 Outline 1 Fundamentals 2 Some Basic Compression Methods 3 Digital Image Watermarking 45 / 53

47 Digital Image Watermarking Visible watermarks Invisible watermarks Can be performed in the spatial domain or the transform domain. 46 / 53

48 Visible Watermarking An opaque or semi-transparent sub-image or image that is placed on top of another image Performed in the spatial domain: f w = (1 α)f + αw where f w is the watermarked image, f is the unmarked image, w is the watermark, 0 < α / 53

49 Visible Watermarking 48 / 53

50 Invisible Watermarking Cannot be seen with the naked eye. LSB watermarking Invisible, fragile, performed in the spatial domain: ( ) f f w = 4 + w 4 64 where 4 ( ) f 4 sets the two least significant bits of f to 0, w 64 shifts its two most significant bits into the two least significant bit positions (f and w are 8-bit grayscale images). 49 / 53

51 LSB watermarking is fragile! Invisible Watermarking 50 / 53

52 Invisible Watermarking DCT-based watermarking Invisible, robust, performed in the transform domain Steps: 1 Compute the 2-D DCT of the image to be watermarked. 2 Locate its K largest coefficients, c 1, c 2,..., c K, by magnitude. 3 Create a watermark: w 1, w 2,..., w K, where w i N (0, 1). 4 Embed the watermark: c i = c i (1 + αw i ) 1 i K replace the original c i with c i. 5 Compute the inverse DCT of the result from step / 53

53 Robust Invisible Watermark DCT-based watermarking is robust! 52 / 53

54 Thanks! 53 / 53

Reduce the amount of data required to represent a given quantity of information Data vs information R = 1 1 C

Reduce the amount of data required to represent a given quantity of information Data vs information R = 1 1 C Image Compression Background Reduce the amount of data to represent a digital image Storage and transmission Consider the live streaming of a movie at standard definition video A color frame is 720 480