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1 A Novel Qualty Measure for Informaton Hdng n s KA Navas Aravnd M SasKumar M Asstant rofessor G student rofessor College Of Engneerng College of Engneerng Maran Engneerng College Trvandrum,Inda Trvandrum,Inda Trvandrum,Inda Abstract. Objectve qualty assessment has been wdely used n mage processng for decades and many researchers have been studyng e objectve qualty assessment meod based on Human Vsual System (HVS). Ths paper presents a new measure whch denotes e perceptual degradaton produced n an mage usng certan subjectvely evaluated weghng functons. Expermental analyss when carred out on dfferent sets of mages for dfferent levels of data hdng and under dfferent attacks shows at s new measure shows a hgh degree of acceptance w e subjectve analyss measure.. Introducton All sorts of data hdng algorms or meods have grown greatly n e past twenty years. However, besdes desgnng ese approaches, a very mportant and often neglected ssue s how to effectvely and precsely measure e perceptual qualty of an mage at has been modfed. In oer words, we need qualty metrcs to analyze e mage degradaton ntroduced by embeddng data.however, most popular measures such as MSE, SNR and SNR [] n e feld of mage codng and compresson, are objectve and are ndependent of e subjectve factors lke HVS (Human Vsual Systems). urng e last several decades, many researchers have tred to fnd a maematc model to smulate HVS characterstcs, and a great deal of effort has been made to develop new mage qualty assessment meods based on HVS. For example, Wen Xu and G. Hauske proposed to estmate e mage qualty based on segmentaton error measure [6]. Myahara et al had proposed a cture Qualty Scale (QS) based on e characterstcs of HVS and e structure and dstrbuton of dstorton []. In addton, oer vsual models based on vsual nterest were proposed too [,3,4]. Though many auors are stll usng SNR as a qualty measure, WSNR and SSIM are found more sutable to express e qualty of e data hdden mage because ey consder e HVS []. None of ese measures are auentc qualty/degradaton measure of mages. We made an attempt to devse a new degradaton measure especally sutable for data hdng applcatons We beleve at s new metrc wll be useful for e evaluaton of nformaton hdng algorms as well as general mage processng applcatons. The rest of e paper s organzed as follows. terature Revew s ncluded n secton. Secton 3 summarzes e proposed qualty evaluaton algorm. Expermental results on gray-scale mages are presented n secton 4 and conclusons are gven n secton 5.. Tterature revewt The qualty assessment of an mage after hdng s done to measure e amount of dstorton due to e data hdng. The best way to assess e qualty of data hdden mage s e subjectve qualty measurement, whch conssts of Mean Opnon Score (MOS) from a number of expert observers. The perceptual mage qualty measure should ncorporate e HVS characterstcs... eak sgnal to nose rato (SNR) SNR and MSE are e most wdely used objectve mage qualty/dstorton metrcs, but ey are not correlatng well w perceved qualty measurement. For calculaton of SNR, e cover mage s taken as e sgnal and e dfference between e embedded mage or degraded mage s taken as e nose. SNR uses peak power of e cover mage and e mean squared value of e error sgnal. However, certan portons of e cover mage can effectvely mask e presence of e hdden data. So only e error sgnals at are vsble to human eye need to be taken /0/$ IEEE

2 as nose for vsual qualty assessment. The mean square error s M N MSE = ( F(x,y) (x,y) ) () MN x = y = F and represents e host mage and embedded mage respectvely of sze M N. The SNR s defned as 55 SNR = 0 log () 0 MSE.. Weghted eak sgnal to nose rato (WSNR) Based on e fact at human eye s less senstve to changes n textured areas an n smoo areas, WSNR has anoer parameter at takes n to account e texture of e mage [5]. The formula for WSNR s shown below 55 WSNR = 0log (3) 0 NVFX MSE Nose Vsblty Functon (NVF) uses a Gaussan model to estmate e amount of texture content n any part of an mage []. In regons w edges and texture NVF wll have a value greater an 0 whle n smoo regons e value of NVF wll be greater an. NVF = NORM (4) + δ Where δ s e lumnance varance for e and NORM s e normalzaton functon.3. Structural Smlarty Measure (SSIM) qualty assessment based on SSIM s based on e fact at e HVS s hghly adapted to extract structural nformaton from e vewng feld. A measure of structural nformaton change can provde a good approxmaton to perceved mage dstorton. The SSIM metrc compares two mages n an overlappng -wse fashon. Each par of correspondng s s compared for lumnance, contrast and structural smlarty w e result combned over all s to gve smlarty measure n e range (0,). The lumnance comparson s a functon of correspondng s mean ntensty, and e contrast comparson s a functon of correspondng s standard devaton []. The structural comparson s computed as e correlaton coeffcent of e two s. The product of e lumnance, contrast and structural comparson functons are en taken as e combned smlarty. S = f l x, y C x, y,s x, y (5) ( ( ) ( ) ( )) 3. roposed meod factor of perceptble degradaton produced n a pxel s a functon of neghborng pxels and ts poston wn e mage. Ths s e basc dea nvolved n s algorm.. et F and represents e orgnal and degraded mages respectvely of sze M N.. vde e mages nto s. et FB B and B B represents e F and respectvely. 3. Horzontal Analyss. Here FB B(x,y) s analyzed n a row wse manner. of 3.. The mean value of each x s F (x) = F ; (6) y= for every x varyng from to. 3.. The average devaton of each x s (x) = ( F F ; () y= for every x varyng from to The perceptual weghng functon BB (x) based on B B(x) s estmated as (x) k + (x) = ; () Where K s an ndex at s subjectvely evaluated and s approxmated by e expresson (x) K = floor ; (9) The postonal weghng functon appled on each pxel FBB(x,y) s subjectvely evaluated and s approxmated by ( y)( x ) For every x, y 4 ( y)( x ) For every 5 x, y (x,y) + = (0) = () 3.5. The perceptual degradaton of by horzontal analyss s gven by H () = (x) E ; () x = y = where E = F Ths algorm ams at comng up w a measure whch denotes e perceptual degradaton produced. The

3 4. Vertcal Analyss. Here FB B s analyzed n a column wse manner. 4.. The mean value of each x s F (y) = F ; (3) x= for every y varyng from to. 4.. The average devaton of each x s (y) = ( F F (y)) ; (4) x= for every y varyng from to Same as 3.3 and The perceptual degradaton of by vertcal analyss s gven by V () = (y) E ; (5) y = x = where E = F 5. agonal Analyss. Here FB B(x,y) s analyzed n dagonal-wse. Here for maematcal smplcty e two dmensonal s transformed n to one dmensonal. The analyss s carred out separately for upper dagonal and lower dagonal sectons F A B where t vares from to M N 5.. The mean value of each dagonal n e upper dagonal secton s A u (x + 9n) ) ; (6) For each x varyng from to, provded x + 9n 64; 5.. The mean value of each dagonal n e lower secton A l (x + 9n + ) ) ; () n = 5.3. The average devaton of each dagonal n upper secton s u (x + 9n) A u ; () For each x varyng from to 5.4. The average devaton of each dagonal n upper secton s l (x + 9n + ) A u ; (9) n = For each x varyng from to 5.5. Here e perceptual weghng functon defned n 3.3 s obtaned for bo B u Band B lb. et t be B ub(x) and B lb(x) respectvely 5.6. The two dmensonal postonal weghng functon BB(x, y) s transformed n to B B(t) where t vares from to The perceptual degradaton of by dagonal analyss n upper secton s gven by u () = u (x)( A B ) ; (0) where α = x + 9n for each x varyng from to 5..The perceptual degradaton of by dagonal analyss n lower secton s gven by l () = l(x) ( A B ) ; () n = where α = x + 9n + for each x varyng from to The degradaton by dagonal analyss s gven by () = u () + l () () 6. The overall perceptual degradaton produced n () = () + V () + H () (3). The perceptual degradaton of e entre mage s gven by C = ()B() (4) = where C=MN/64 and B() s a weghng functon based on e poston of e n e mage and s approxmated as B = B (x)b (y) (5) Where ( N 0.3 (N 6x) ) B (x) = (6) N ( M 0.3 (M 6y) ) B (y) = () M B(x, y) B

4 SNR WSNR SSIM ROOSE METHO Varance Medcal Mltary Medcal Mltary Medcal Mltary Medcal Mltary Table: Qualty VS Gaussan Nose Varance. The % erceptual egradaton s gven by 0.9 % ercpeg = X00 () Max where Max s e overall perceptual degradaton correspondng to (x,y ) = 0 and f goes above Max en t s scaled between 90 and 00 based on e dfference. 4. Expermental results The expermental analyss were carred out on -bt gray scale medcal mages, mltary mages and natural mages. The effect of Gaussan nose on e medcal mage s depcted n fgure. The varaton of qualty measure w e varance of Gaussan nose usng already exstng measures and e perceptual degradaton obtaned usng e proposed meod s shown n Table. Fgure.: Gaussan nose Varance VS erceptual egradaton for Medcal and Mltary mages The varaton of perceptual degradaton n an mage w embeddng capacty s shown n Fgure.3 for bo spatal and frequency doman. Table reveals e change n qualty measure as functon of e amount of data been embedded for oer qualty measures. Here SSIM measure of 0.4 when bts were substtuted s found to be better an e measure when Gaussan nose varance was 0.0. Ths was not true w our subjectve analyss Here t s seen at e perceptual degradaton caused n spatal doman s more an at n e frequency doman for e same amount of data embedded Fgure :Medcal mages affected by Gaussan Nose of dfferent varance From Fgure and Fgure t s seen at e proposed meod gves a measure whch s closely related to human vson. In e case of WSNR e change appears to be more or less a constant. Fgure 3: erceptual degradaton VS capacty

5 6 EURASI No. SNR WSNR SSIM ROOSE METHO of Bts Spatal oman oman Spatal oman oman Spatal oman oman Spatal oman oman Table : Qualty measures VS Capacty The fgure 4 represents e two degraded verson of e medcal mage shown n Fgure.About 9% of e responses obtaned by subjectve analyss were pontng to e frst mage as of hgher qualty. The WSNR value obtaned by our analyss reveal a hgher value to e second mage compared to e frst mage. Ths happens to be n contradctory to our subjectve analyss. SSIM and e proposed meod closely match w e subjectve analyss. But e dfference n smlarty measures obtaned n e two cases usng SSIM s very small compared w e perceptual degradaton obtaned by our meod Fgure 4: egraded mages 6. References [] Claudo M prvtera, awrence W.stark, Algorms for efnng Vsual Regons of Interest Comparson w Eye Fxaton, IEEE trans on AMI, Vol., No.9, pp , 000. []. Navas K. A, Sreevdya S, Saskumar M, A benchmark for medcal mage watermarkng, 4 Internatonal workshop on systems, sgnals and mage processng and conference focused on speech and rocessng, Multmeda Communcaton and servces IWSSI-00 and ECSIMCS00, Marbor, Slovena, pp49-5, -30 June 00. [3] Wang Zhou, Bovk. A. C. Wavelet-based foveated mage qualty measurement for regon of nterest mage codng rocessng. roceedngs. Vol.. pp.9 9, 00. [4] Wang Kong-qao, Shen an-sun, Xng Xn. A Qualty Assessment Meod of Based on Vsual Interests. Journal of and Graphcs. pp ,000. [6] Wen Xu and G. Hauske, cture Qualty Evaluaton Based on Error Segmentaton, SIE vol.304, pp , 994. [] M. Myahara, K. Kotan and V. Algaz Objectve Rep cture Qualty Scale (QS) for Codng, TechCIIC, Unversty of Calforna, avs,996. [] Fan Zhang and Honghn Zhang, Wavelet oman Watermarkng Capacty Analyss, n roc. of SIE, vol.563, pp , Februaary Conclusons Ths paper proposes a new qualty measure whch gves e total perceptual degradaton produced n an mage as a result of data hdng and varous attacks happenng on e communcaton channel. Ths measure has been expermentally compared w e avalable qualty measures such SNR, WSNR and SSIM for dfferent class of mages under dfferent condtons and s found to be closely matched w e subjectve measures