IEEE Trans Image Process - Side information and noise learning for distributed video coding using optical flow and clustering.

Tópicos

{ imag(1947) propos(1133) code(1026) }
{ method(984) reconstruct(947) comput(926) }
{ motion(1329) object(1292) video(1091) }
{ method(1969) cluster(1462) data(1082) }
{ framework(1458) process(801) describ(734) }
{ learn(2355) train(1041) set(1003) }
{ health(3367) inform(1360) care(1135) }
{ imag(2830) propos(1344) filter(1198) }
{ general(901) number(790) one(736) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ model(2341) predict(2261) use(1141) }
{ cost(1906) reduc(1198) effect(832) }
{ estim(2440) model(1874) function(577) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ spatial(1525) area(1432) region(1030) }
{ sampl(1606) size(1419) use(1276) }
{ detect(2391) sensit(1101) algorithm(908) }
{ imag(1057) registr(996) error(939) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ blood(1257) pressur(1144) flow(957) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ use(976) code(926) identifi(902) }
{ decis(3086) make(1611) patient(1517) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
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{ studi(1410) differ(1259) use(1210) }
{ perform(999) metric(946) measur(919) }
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{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
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{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ 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) }
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{ 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) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bj?ntegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

Resumo Limpo

distribut video code dvc code paradigm exploit sourc statist decod side reduc complex encod code effici dvc critic depend qualiti side inform generat accuraci nois model paper consid transform domain wynerziv tdwz code propos use optic flow improv side inform generat cluster improv nois model optic flow techniqu exploit decod side compens weak blockbas method use motioncompens generat side inform frame cluster introduc captur cross band correl increas local adapt nois model paper also propos techniqu learn previous decod wz frame differ techniqu combin calcul number candid soft side inform low densiti pariti check accumul decod propos decod side techniqu side inform nois learn sing integr tdwz scheme test sequenc propos sing codec robust improv code effici tdwz dvc wz frame use gop size db improv averag bjntegaard bitrat save achiev compar discov

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