# Digital Image Processing

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1 Digital Image Processing 5 January 7 Dr. ir. Aleksandra Pizurica Prof. Dr. Ir. Wilfried Philips Tel: 9/ UNIVERSITEIT GENT Telecommunicatie en Informatieverwerking Lossy Image Compression

2 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, A general image compression scheme reducing spatial correlation Orthogonal transformation Predicting pixel values from the previous values (integer) prediction errors or (real) transform coefficients removing visuallyirrelevant data Quantisation of the coefficients/prediction errors Not coding some coefficients/prediction errors integer numbers removing statistical redundancy Huffman coding Arithmetic coding bit stream 8b.3 version: 5/1/7 The RateDistortion (RD) curve A. W. Pizurica, Philips, Universiteit Gent, In lossy compression there is a tradeoff between quality and the compression factor A common numerical distortion criterion is squared error N M MN ( ~ f ( x, y) f ( x, y) ) y= x= or the PSNR, an average score given by a test panel, distorsion ratedistortion curve ratedistortion limit beter for a good technique it has to be monotonically decreasing! number of bits ( bit rate ) The Ratedistortion curve gives the corresponding values of the distrorsion and the number of bits, for different specifications of the coding parameters Usually it is computed for a representative set of images The Ratedistortion limit gives the smallest possible distorsion that can be achieved by any coding scheme for a given number of bits 8b.4

3 version: 5/1/7 Examples... A. W. Pizurica, Philips, Universiteit Gent, N M MN y= x= RMS error ( ~ f ( x, y) f ( x, y) ) Quasilossless region Lossy JPEG Lossy Btpc #bits (kbyte) Current compression standard Predictive method (prev. lesson!) The current compression standard is no longer optimal in the quasilossless region 8b.5 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Examples: compression versus distorsion 5 RMS error "lossless" Hadamard Lossy JPEG Btpc Lossless DCT MN Compression factor = #bits 8b.6

4 version: 5/1/7 Subjectieve quality measures A. W. Pizurica, Philips, Universiteit Gent, These measures take into account the characteristics of the human eye Example: frequency weighted mean square error M 1 N 1 ~ ~ F k, l Fk, l Wk, l ( F k, l, F k, l are DDFTs) k= l= The weighting function W k,l takes into account spatial and frequency sensitivity of the eye W k,l depends on the viewing distance! only for a fixed viewing distance! Interesting for optimizing coding schemes based on transform coding 8b.7 version: 5/1/7 Lossy prediction techniques A. W. Pizurica, Philips, Universiteit Gent, k x p prediction current pixel coded pixels + quantization k = [ x/ ] k Huffman coder prediction + + k scaling k Huffman decoder new pixel The coder makes prediction based on the coded pixels instead of on the basis of original pixels less accurate prediction in the coder no error propagation netto beter prediction 8b.8

5 version: 5/1/7 Quantization: example A. W. Pizurica, Philips, Universiteit Gent, Quantisation ( =) Dequantization (scaling with = ) b.9 version: 5/1/7 Transform coding A. W. Pizurica, Philips, Universiteit Gent, Goal: linearly transform image data into (quantized) coefficients with lower entropy a k f ( x, y) transform a = M quantisation x =... n 1 a n 1 y =... n 1 k j = [ a j / j ] statistical coding ~ f ( x, y) x =... n 1 y =... n 1 inverse transform a~ ~ a = M ~ a n 1 ~ a a scaling a~ = j k j j k statistical decoding Advantage of the orthogonality of the basis functions: energy preserved, squared error easy to estimate: d = n 1n 1 n 1 ( ~ f ( x, y) f ( x, y) ) = ( a~ i ai ) x= y= i= n / 4 als j = a~ j a j j / 8b.1

6 version: 5/1/7 The best orthogonal transform A. W. Pizurica, Philips, Universiteit Gent, The best transform is the one that yields the best ratedistortion (RD) curve: For a given compression factor distortion is as small as possible For a given distortion the compression factor is as high as possible The best transform in RD sense is the KarhunenLoève transform This transform is image dependent and notseparable! Based on experiments the DiscreteCosineTransform (DCT) is almost optimal This transform is image independent and separable (so faster to compute)! 8b.11 DCT based image compression

7 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, The discrete cosine transform (DCT) = image block a +a 1 +a +a 3 + +a 1 +a 11 +a 1 +a a +a 1 +a +a 3 + +a 3 +a 31 +a 3 +a 33 + q ( y) q ( x) i coefficients block j + Basis functions: q i (y)q j (x); ci i( x + 1/ )π qi ( x) = cos with c n n i = 1 for i= for i 8b.13 version: 5/1/7 Blokcodering met de DCT A. W. Pizurica, Philips, Universiteit Gent, big DCT a i, j small There are a lot of small coefficients; big ones are rare The histogram of the quantized coefficients is strongly nonuniform ideal for statistical coding Experiments show that block sizes n=8 to n=3 yield the best ratedistortion curve The DCT is separable and requires hence 4n computations per pixel n=8 is thus 4 times faster than n=3 usually n=8 is chosen 8b.14

8 version: 5/1/7 The JPEGstandard A. W. Pizurica, Philips, Universiteit Gent, JPEG = Joint Photographic Experts Group forward DCT quantisation entropycoding image data Division into blocks of 8x8 pixels Quantisation with frequency dependent accuracy : k i, j = [ ai, j / i, j ] Huffman coding or arithmetic coding The quantized DCcoefficients a, (one per block) are gathered in a smaller image which is coded by lossless JPEG Runlength coding is applied to highfrequency coefficients within a block table table Compressed file Quantization table Huffman table Compressed data 8b.15 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Psychovisually adapted quantization The quantization step is frequencydependent High frequency coefficients are quantized more coarsely because eye is less sensitive to high spatial frequencies The optimal quantization table is derived based on experiments with test persons i, j Vertical frequency i 4 6 Horizontal frequency j The percieved spatial frequency depends on the viewing distance this implies implicitely a fixed viewing distance frequency dependent quantization is not a good option when increasing image is desired (e.g., in medical applications) 8b.16

9 version: 5/1/7 Coding of DCcoefficients A. W. Pizurica, Philips, Universiteit Gent, x56 image, divided in 8x8 blocks DCcoefficients of each block, grouped in 3x3 image (here shown 8x enlarged) Neighboring DCcoefficients are still pretty much similar From these coefficients a DC image is formed and compressed with LJPEG 8b.17 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Zigzag scan and runlength coding Zerorun (11,) (8,3) (7,1) (4,) (,) meta symbol, coded by e.g. an (adapted) Huffman coder Endofblock The quantized HF coefficients disappear quickly with increasing frequency Because of typical concentration of image energy at low frequencies Because of stronger quantization of the coefficients at high frequencies To make use of this zeroruns and endofblock markers are introduced 8b.18

10 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Errors in highly compressed images Original DCT, 3x3 (compression factor 4) c b a At the borders of the blocks the error changes discontinuously Block distortion (a) The coefficients at highest frequencies are by quantization set to zero, as if an ideal lowpass filter was applied lowpass filtering effect: the image becomes blurred (b) Gibbseffect: wrinkles appear in the image (c) 8b.19 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, JPEG: Problems at high compression Block distortion: the borders of the blocks become visible Compression factor 7 (.3 bpp) 8b.

11 Subband coding and wavelet based image compression version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Subband coding wavelet coding Principle: transform image using (bi) orthogonal discrete wavelet transform (DWT) quantize the wavelet coefficients Make use of the dependencies between the wavelet coefficients using zero tree coding or significance coding Use a Huffman coder or an arithmetic coder to reduce the remaining statistical redundancy The JPEG standard Extension of the old JPEG standard Wavelet representation + significance coding Most important advantages Beter quality for very low and very high compression Very flexible: scalability in resolution, quality, spatial location and color component 8b.

12 version: 5/1/7 Scalability in quality A. W. Pizurica, Philips, Universiteit Gent, Time: s 56x56x8 Total transmission time over 33.6 modem without compression: s with compression: 13 s Progressive decompression: very good image after s Useful for navigation in big image sets Remark: compression technique in this demo does not use wavelets! 56x56x8 51x51x1 ISDN (18 kbit/s): 4 s 4 s 33.6 modem: s. min. 8b.3 version: 5/1/7 Scalability A. W. Pizurica, Philips, Universiteit Gent, Scalability means that by truncating the compressed file we get an image version the quality of which improves when we truncate less Important here is the order in which data packets are stored in the compressed file Scalability in accuracy: the most significant bits of all wavelet coefficiets in all subbands are stored first, then less significant bits follow Scalability in resolution: low and medium frequency wavelet bands are first saved with maximum quality before taking high frequency coefficients in the reconstruction Scalability in spatial location: all coefficients of a region of interest (e.g. the central part of the image) are saved first Scalability in color: the coefficients of the luminance component are saved first and the chrominance components follow after 8b.4

13 version: 5/1/7 Scalability: remarks A. W. Pizurica, Philips, Universiteit Gent, It is possible to switch from one type of scalability to another during compression: e.g. first resolution then quality The JPEG data stream consists of packets; in the header of these packets it is noted which information they contain By reordering the packets it is possible to get a different type of scalability without decoding and coding again Applications of scalability: Progressive transmission over channels with low bandwidth: while the bits are ariving the decoder calculates better and better versions of the compressed image When printing on a greyscale printer the chrominance packets can be omitted Generating thumbnails on a web page by truncating high quality images 8b.5 version: 5/1/7 Zerotree coding... A. W. Pizurica, Philips, Universiteit Gent, The coefficients in a tree correspond with the same position in the image And are result of the same type of filtering applied to a lowpass (and subsampled) version of the image 8b.6

14 version: 5/1/7... Zerotree coding A. W. Pizurica, Philips, Universiteit Gent, N The children of a coefficient with value zero are often also zero zero trees are replaced by a single symbol 8b.7 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Significance coding (JPEG) one precinct codeblock One resolution level is composed of 3 subbands Each resolution level is divided in precincts (regions): each region contains the coefficients from one spatial surrounding and from all 3 subbands of the resolution level Each region is split in a similar way into code blocks Code blocks are coded independently of one another method: context dependent encoding of bit planes, starting with the most significant bit plane k = image formed by kth bit of the absolute values of each coefficient in a code book 8b.8

15 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Significance coding (JPEG) Each bit plane is subdivided into stripes of height 4 Order of arithmetic coding Most significant bit planes first Inside of the bit plane: stripe per stripe Inside a stripe: column per column Sign bit is separately encoded and not described as bit plane The compressed data are grouped into layers and packets: a packet consists of a (variable) number of bit planes of a number of code blocks from the same one region contains data of a given spatial location and resolution a layer consists of a packet of each region at each resolution level; a layer does not necessarily contain all the bit planes contains data that increase the quality of the whole image 8b.9 version: 5/1/7 Bit plane coding A. W. Pizurica, Philips, Universiteit Gent, Each bit plane is separately encoded, but coding is adaptive and makes use of context information of more significant bit planes Definition: a given wavelet coefficient becomes significant (in the sense of significance coding ) when at least one of its already coded bits is 1 The coding of a bit plane conists of 3 steps: step 1, significance propagation: coding the bits with value k of the coefficients that are not significant yet, but have at least one neighbor that is already significant step, magnitude refinement : coding of the bits of the coefficients that became significant in the previous bit plane step 3, cleanup pass : coding all the remaining bits Remark: when the coefficient become significant for the first time, its sign is coded immediately 8b.3

16 version: 5/1/7 1=+11 Bit plane coding: example Coefficients Step clean up significance. refinement sign bit 3. clean up 1 1. significance 1+. refinement clean up 1. significance 1+. refinement clean up 7=111 bit plane 3: value 3 bit plane : value e bit plane 1: value 1 A. W. Pizurica, Philips, Universiteit Gent, no remaining coefficients no work for clean up bit plane : value Remark: for the first bit plane the steps 1 and are not performed; there are no significant coefficients yet at that point 8b.31 version: 5/1/7 A. W. Pizurica, Philips, Universiteit Gent, Philosophy behind bit plane coding Philosophy of bit plane coding: sorting the bits for an optimal adaptive operation of the arithmetic coder in step 1, 1 bits are very likely: significant coefficients = large coefficients, and these are spatially grouped in step, 1 bits are equally likely as bits in step 3, bits are likely, typically the smallest coefficients coded in this step In each of the 3 steps we have a different statistics Arithmetic coder works better when encoding bits of a given type after one another instead of mixing all the types 8b.3

17 version: 5/1/7 Remarks A. W. Pizurica, Philips, Universiteit Gent, Not discussed aspects Runlegth coding is also used in some steps The image is first divided into tiles; these are subimages that are coded independently of one another extra high level scalability Why significance coding and not zerotree coding in JPEG? In zerotree coding the subbands are jointly coded less possibilities for scalability and more complex implementation! 8b.33 version: 5/1/7 Wavelet coding versus JPEG... A. W. Pizurica, Philips, Universiteit Gent, JPEG, factor 4 JPEG, factor 4 SPIHT, factor 4 SPIHT= Set Partitioning in Hierarchical Trees = wavelets+zero tree coding SPIHT is much better than JPEG at low bit rates The new JPEGstandard JPEG contains a wavelet algorithm Original: 56 kbyte, 8 bit/pixel Compressed: 65 byte,. bit/pixel 8b.34

18 version: 5/1/7 Wavelet coding versus JPEG... A. W. Pizurica, Philips, Universiteit Gent, JPEG, factor 4 JPEG, factor 1 JPEG, factor JPEG, factor 4 JPEG, factor 1 JPEG, factor cannot be realised 8b.35 version: 5/1/7... Wavelet coding versus JPEG A. W. Pizurica, Philips, Universiteit Gent, JPEG, factor 4 JPEG, factor 4 SPIHT, factor 4 Original: 64 kbyte, 8 bit/pixel Compressed: 16 byte,. bit/pixel SPIHT and JPEG never cause block distrortion Small images compress obviously worse than big images it is not the resolution of the image but its content that that finally determines the number of bits for a given quality 8b.36

19 version: 5/1/7 Bibliography A. W. Pizurica, Philips, Universiteit Gent, Technical overview of JPEG JPEG software implementation (JasPer Project an opensource initiative to provide a free softwarebased reference implementation of the JPEG codec). 8b.37 version: 5/1/7 Commands A. W. Pizurica, Philips, Universiteit Gent, Conversion to jpeg factor 4: jasper input lena51x51.bmp outputformat jp output lena.jp O rate=.5 factor 1: jasper input lena51x51.bmp outputformat jp output lena.jp O rate=.1 Conversion from jpeg jasper input lena.jp outputformat pnm output lena.pnm Conversion to jpeg factor 4: convert quality 1 lena51x51.bmp lena.jpg factor 1: convert quality 3 lena51x51.bmp lena.jpg 8b.38

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