PEER.REVIEWED lmageGomBression bythe JPEG Algorithm Jussi Lammi and TapaniSarjakoski Abstract compressthe whole image in a single compressionstep. A schemefor doing this in smaller parts is proposed.The geo- Image compression is a necessityfor the utilization of large of image compressionon full-color images digital images, e.g., digitized aerial color images. The lPEc metric effect IPEG is empirically studied. We wanted to test our assumption still-picturc compression algorithm is one alternative for car- that image compressiondoes not affect the image geom- rying out the image compression task. The pnc method itself JPEG etry if small compressionratios are used. and its suitability for photogrammetric work are studied, with special attention being paid to the geometric degrada- tion of digital images due to the compression process. In our JPEGlmage Compression Alsorithm experience, the pnc algorithm seems to be a good choice The following overview of the IPEGimage compressionalgo- for (1991) image compression. For color images, it gives a compression rithm is basedon articles by Wallace and L1ger et al. rutio of about 1:L0 without considerabledegrudation in the (1991).Those interestedin a detailed description of the IPEG visual or geometric quality of the image. algorithm are encouragedto examine the standard specifica- tions directly (at the moment of writing, the ISO/IECDIS 10s18-1(t99r) was availableto us). Introduction The /PEGstandard contains four modes of operation: se- Compressionof digital imagesis a necessityin applications quential encoding, progressiveencoding, lossless encoding, where many large images have to be archieved in a limited and hierarchical encoding.The sequentialand progressive storagespace or where digital imagesare transmitted over encodingsare based on the discretecosine transform. Both of narrow channels.The basic idea of image compressionis to these modes are lossy encoding techniques,i.e., some infor- remove redundancy from the image data. This is usually mation is lost during the compressionprocess. In the sequen- done by mapping the image to a set of coefficients.The re- tial mode, each image component is handled in a single sulting set is then quantized to a number of possiblevalues left-to-right,top-to-bottom scan while, in the progressive which are encodedby an appropriate coding method. Nowa- mode, the image is handled in multiple scans.In the lossless days, the most commonly used image compressionmethods encoding, the image is compressedso that the exact recovery are basedon the discretecosine transform (Rosenfeldand of the original image is guaranteed.The losslessmode of (Gray, Kak, lgaz; Wallace, 1991),on vector quantization Jrnc is based on a predictive method, The hierarchical mode 1984; Nasrabadiand King, 19BB),on differential pulse code encodesthe image at multiple spatial resolutionsusing either modulation (Gonzalezand Wintz, 1987),and on the use of the ncT-basedcompression or the losslessmode. image pyramids (Burt and Adelson, 1983; Mdkisara, 1991). Within the different modes of operation, a variety of en- The IPEGimage compressionalgorithm proposedby the coder/decoderpairs (so-calledcodecs) can be specified.AI- (lnnc) Joint PhotographicExperts Group offers a viable way though IPEGprovides a lot of possibilities for encoding,it of accomplishingthe image compressiontask. The JPEG also gives a "basic" compressionscheme-baseline sequen- group is an ISO/CCITT(International Standards Organization./ tial encoding-for straightforward use. In the following, we International ConsultativeCommittee for Telephone and Tel- shall concentrateon this method only. This is justified be- egraph)working group whose aim has been to develop an in- causebaseline encoding is sophisticatedenough for many ternational standardfor continuous-tonestill-picture com- applications, and it already explains the common idea be- (lso pression working group ITC1/SC2/WG10in collaboration hind most of the ;rec modes. Besides,the IPEGimplementa- with CCITTSGVIII). In the compressionmethod selectionpro- tions cunently on the market typically support only the cess-conducted by the JPEGcommittee-the discretecosine baseline sequentialencoding. (lcr)-based transform compressionwas chosen for standard The baseline encoding method of ;rnc contains three se- develooment. quential steps:forward discretecosine transform (rncr), The final ISo standard for;mc image compressionwill quantization,and Huffman coding (Figure 1), The processing be divided into two parts. Part 1 will specify the require- schemeis applied to a stream of B- by B-pixel blocks with B- ments and guidelines for the JPEGimage compression,and bits per pixel. The use of small image blocks takes into ac- Part 2 will contain the compliance teststaken. Accordins to count the fact that the correlation between adjacentpixels is the ISo Technical Program igss, the current Draft Interni- usually high in imagesof natural scenes.Decompression is tional Standardof the JPEG(DIS 109181 is expectedto be pub- achievedby following the processingsteps in the opposite lished as an International Standard US10918) before the end direction: Huffman decoding,dequantization, and inverse of year 1993. discretecosine transform (IDCT). This paper gives an overview of the IPEGimage compres- sion algorithm, which is still quite new to the photogram- Photogrammetric Engineering & Remote Sensing, metric community. As the digital imagesused in photogram- Vol. 0t, No. 10, October 1995, pp. 1.261.-1.266. metric work tend to be very large, it is not reasonableto 0099-1112l95/61 10-1261$3.00/0 Finnish GeodeticInstitute, Departmentof Cartographyand O 1995 American Society for Photogrammetry Geoinformatics,Geodeetinrinne 2, O243OMasala, Finland. and RemoteSensing PE&RS PEER.REVIEWED ARTICTE DiscleteCosine Transform The B- by B-pixel block is transformedinto a set of basis-sig- nal amplitudes (64 coefficients)by the two-dimensional dis- crete cosine transform. The two-dimensional ncr of the B by B discretefunction f[x.v) is quantized F(u,v) Figure2. The coeffi- cient are orderedinto a "zig- zag" sequence. : 1"(,r"@ Z,P- f(ry)cost*#]no] .", 1q#] (1) and the correspondinginverse is f(x,y) 1+\-, , f(zx+r)u"tl fQy+t1wrl :: L L c(u)c{v) F{ u'v)cosL 4uov-o 16 J"utL 16 J though the IpEc proposal specifiestwo entropy coding meth- (2) ods (Huffman and arithmetic coding), the baieline compressionuses Huffman coding only. where c(u), c(v) : I/t/i for u, v: 0, and c(u), c(v) : 1 for u, Colorlmate Compression Note that the DCTsimply maps the image block from one The baseline sequentialcoding is representationto another-in principle, it does not lose data. for 8-bit images,but it can be applrgd to color imagesas well. The color image compres- However, in transformation,the energy of the image block is sion is done by compressing compactedinto a few coefficients, multiple channelsone by one (non-interleavedorder) or by an approachin which all channels from the data unit (S- by S-pixel Quantization block) are com- pressedbefore proceedingto the next unit (interleaved After the FDCT,each cell in the 64-elementcoeffrcient matrix or- der). Although the baselinemethod is quantized to a correspondingvalue in the predetermined compressescolor images presented by any color model, it is best for quantization table. This is applied by dividing each DCTcoef- images that are in color spacessuch as YUV (y for luminance, UV for chromi- ficient (F(u,v))by the correspondingquantization element nance) in which the color components (Q(u,v))and rounding the result of division the in- are independent.As to nearest the teger:i.e., chrominancevalues need not to be consideied as fre- quently as luminance values, the spatial resolution of the U and v componentscan be decreased.This explains why Fa(u,v): (3) roundlffi] color imagescan be compressedwith a better ratio than grey- scale images. The quantization step performs the major and lossy part of Subsamplingof the chrominance channelsis done in the the compressionin the pEG algorithm. Here, the number of spatial domain, e.g.,by leaving every other pixel away in the different coefficient values is reduced and the number of line and/or column direction. If imagesare -ompressedin zero value coefficientsis increased. the interleaved order, the data units have to be combined to quantization, After the quantized coefficient for the zero such groups that the encoding can be done independently- for frequency in both dimensions (so-calledDC coefficient) is en- each group. Grouping is necessarybecause chrominance coded as the differencefrom the DCterm of the previous channelshave to be composedof B by B blocks also after block. Finally, all coefficientsare ordered into a "zig-zag" se- subsampling.Next, the baselineencoding schemeis applied quence (Figure 2). This ordering makes entropy coding easier to all channels. by placing the low-frequency coefficientsbefore the high-fre- quency coefffcients. Compressionof Large Digital lma(es Decompressionof large digital imagesis time-consuming. EntropyCoding Large imagesare also rather cumbersometo handle as a The final step in the lcr-based compressionis entropy cod- whole. It is also true that, in many applications, only part of ing. Here, the quantized coefficientsare encoded to a more the image is required immediately. An approach in which compact form by using the statisticalstructure of data. Al- the original image is divided into tiles "large enough" to be lompre.ssed t$'ffi!fiff- {[-J rquannanonr I utroons I rmsedara Figure1. BaselineJpEc