IEEE Trans Image Process - Image quality assessment by visual gradient similarity.

Tópicos

{ imag(2830) propos(1344) filter(1198) }
{ perform(999) metric(946) measur(919) }
{ model(2656) set(1616) predict(1553) }
{ visual(1396) interact(850) tool(830) }
{ ehr(2073) health(1662) electron(1139) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ model(3404) distribut(989) bayesian(671) }
{ assess(1506) score(1403) qualiti(1306) }
{ compound(1573) activ(1297) structur(1058) }
{ imag(1947) propos(1133) code(1026) }
{ learn(2355) train(1041) set(1003) }
{ search(2224) databas(1162) retriev(909) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ research(1218) medic(880) student(794) }
{ implement(1333) system(1263) develop(1122) }
{ detect(2391) sensit(1101) algorithm(908) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ take(945) account(800) differ(722) }
{ error(1145) method(1030) estim(1020) }
{ extract(1171) text(1153) clinic(932) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ import(1318) role(1303) understand(862) }
{ spatial(1525) area(1432) region(1030) }
{ monitor(1329) mobil(1314) devic(1160) }
{ age(1611) year(1155) adult(843) }
{ cost(1906) reduc(1198) effect(832) }
{ can(774) often(719) complex(702) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

A full-reference image quality assessment (IQA) model by multiscale visual gradient similarity (VGS) is presented. The VGS model adopts a three-stage approach: First, global contrast registration for each scale is applied. Then, pointwise comparison is given by multiplying the similarity of gradient direction with the similarity of gradient magnitude. Third, intrascale pooling is applied, followed by interscale pooling. Several properties of human visual systems on image gradient have been explored and incorporated into the VGS model. It has been found that Stevens' power law is also suitable for gradient magnitude. Other factors such as quality uniformity, visual detection threshold of gradient, and visual frequency sensitivity also affect subjective image quality. The optimal values of two parameters of VGS are trained with existing IQA databases, and good performance of VGS has been verified by cross validation. Experimental results show that VGS is competitive with state-of-the-art metrics in terms of prediction precision, reliability, simplicity, and low computational cost.

Resumo Limpo

fullrefer imag qualiti assess iqa model multiscal visual gradient similar vgs present vgs model adopt threestag approach first global contrast registr scale appli pointwis comparison given multipli similar gradient direct similar gradient magnitud third intrascal pool appli follow interscal pool sever properti human visual system imag gradient explor incorpor vgs model found steven power law also suitabl gradient magnitud factor qualiti uniform visual detect threshold gradient visual frequenc sensit also affect subject imag qualiti optim valu two paramet vgs train exist iqa databas good perform vgs verifi cross valid experiment result show vgs competit stateoftheart metric term predict precis reliabl simplic low comput cost

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