IEEE Trans Image Process - A locally adaptive system for the fusion of objective quality measures.

Tópicos

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

Resumo

Objective measures to automatically predict the perceptual quality of images or videos can reduce the time and cost requirements of end-to-end quality monitoring. For reliable quality predictions, these objective quality measures need to respond consistently with the behavior of the human visual system (HVS). In practice, many important HVS mechanisms are too complex to be modeled directly. Instead, they can be mimicked by machine learning systems, trained on subjective quality assessment databases, and applied on predefined objective quality measures for specific content or distortion classes. On the downside, machine learning systems are often difficult to interpret and may even contradict the input objective quality measures, leading to unreliable quality predictions. To address this problem, we developed an interpretable machine learning system for objective quality assessment, namely the locally adaptive fusion (LAF). This paper describes the LAF system and compares its performance with traditional machine learning. As it turns out, the LAF system is more consistent with the input measures and can better handle heteroscedastic training data.

Resumo Limpo

object measur automat predict perceptu qualiti imag video can reduc time cost requir endtoend qualiti monitor reliabl qualiti predict object qualiti measur need respond consist behavior human visual system hvs practic mani import hvs mechan complex model direct instead can mimick machin learn system train subject qualiti assess databas appli predefin object qualiti measur specif content distort class downsid machin learn system often difficult interpret may even contradict input object qualiti measur lead unreli qualiti predict address problem develop interpret machin learn system object qualiti assess name local adapt fusion laf paper describ laf system compar perform tradit machin learn turn laf system consist input measur can better handl heteroscedast train data

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