IEEE Trans Image Process - Additive log-logistic model for networked video quality assessment.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
{ model(2341) predict(2261) use(1141) }
{ system(1976) rule(880) can(841) }
{ assess(1506) score(1403) qualiti(1306) }
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{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
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{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
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{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
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{ activ(1452) weight(1219) physic(1104) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ patient(2837) hospit(1953) medic(668) }
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Resumo

Modeling subjective opinions on visual quality is a challenging problem, which closely relates to many factors of the human perception. In this paper, the additive log-logistic model (ALM) is proposed to formulate such a multidimensional nonlinear problem. The log-logistic model has flexible monotonic or nonmonotonic partial derivatives and thus is suitable to model various uni-type impairments. The proposed ALM metric adds the distortions due to each type of impairment in a log-logistic transformed space of subjective opinions. The features can be evaluated and selected by classic statistical inference, and the model parameters can be easily estimated. Cross validations on five Telecommunication Standardization Sector of International Telecommunication Union (ITU-T) subjectively-rated databases confirm that: 1) based on the same features, the ALM outperforms the support vector regression and the logistic model in quality prediction and, 2) the resultant no-reference quality met-ric based on impairment-relevant video parameters achieves high correlation with a total of 27 216 subjective opinions on 1134 video clips, even compared with existing full-reference quality metrics based on pixel differences. The ALM metric wins the model competition of the ITU-T Study Group 12 (where the validation databases are independent with the training databases) and thus is being put forth into ITU-T Recommendation P.1202.2 for the consent of ITU-T.

Resumo Limpo

model subject opinion visual qualiti challeng problem close relat mani factor human percept paper addit loglogist model alm propos formul multidimension nonlinear problem loglogist model flexibl monoton nonmonoton partial deriv thus suitabl model various unityp impair propos alm metric add distort due type impair loglogist transform space subject opinion featur can evalu select classic statist infer model paramet can easili estim cross valid five telecommun standard sector intern telecommun union itut subjectivelyr databas confirm base featur alm outperform support vector regress logist model qualiti predict result norefer qualiti metric base impairmentrelev video paramet achiev high correl total subject opinion video clip even compar exist fullrefer qualiti metric base pixel differ alm metric win model competit itut studi group valid databas independ train databas thus put forth itut recommend p consent itut

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