IEEE Trans Image Process - Blind image quality assessment: a natural scene statistics approach in the DCT domain.

Tópicos

{ model(3404) distribut(989) bayesian(671) }
{ assess(1506) score(1403) qualiti(1306) }
{ featur(3375) classif(2383) classifi(1994) }
{ method(1557) propos(1049) approach(1037) }
{ imag(2830) propos(1344) filter(1198) }
{ learn(2355) train(1041) set(1003) }
{ model(2341) predict(2261) use(1141) }
{ process(1125) use(805) approach(778) }
{ measur(2081) correl(1212) valu(896) }
{ problem(2511) optim(1539) algorithm(950) }
{ use(976) code(926) identifi(902) }
{ data(1737) use(1416) pattern(1282) }
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{ signal(2180) analysi(812) frequenc(800) }
{ motion(1329) object(1292) video(1091) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
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{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
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{ patient(1821) servic(1111) care(1106) }
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Resumo

We develop an efficient, general-purpose, blind/noreference image quality assessment (NR-IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.

Resumo Limpo

develop effici generalpurpos blindnorefer imag qualiti assess nriqa algorithm use natur scene statist nss model discret cosin transform dct coeffici algorithm comput appeal given avail platform optim dct comput approach reli simpl bayesian infer model predict imag qualiti score given certain extract featur featur base nss model imag dct coeffici estim paramet model util form featur indic perceptu qualiti featur use simpl bayesian infer approach predict qualiti score result algorithm name bliindsii requir minim train adopt simpl probabilist model score predict given extract featur test imag qualiti score maxim probabl empir determin infer model chosen predict qualiti score imag test live iqa databas bliindsii shown correl high human judgment qualiti level competit popular ssim index

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