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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F06%3A03120527%21RIV07-GA0-21230___
rdf:type
skos:Concept n20:Vysledek
dcterms:description
V tomto elánkuje popsán nový poístup predikce obrazové kvality. Vhodné metriky byly vybrány s ohledem na vlastnosti lidského visuálního systému (HVS). Tyto metriky jsou vypoeítány porovnáním originálního a zkresleného obrazu. Na poedzpracování dat byla pou3ita pokroeilá metoda (Mutual Information – MI, Principal Component Analysis – PCA) tak, aby odhad kvality z modelu byl blízký subjektivním soudum pozorovatelu. Vektor zkreslujících parametru je zpracován ANN, kde jsou tyto parametry zkombinovány a ureena predikce kvality. Parametry ANN byly nastaveny s pou3itím subjektivních soudu skupinou pozorovatelu (MOS). Dobrá predikení schopnost modelu byla demonstrována ve smyslu vysoké korelace mezi subjektivními soudy MOS a výstupem neuronové síti. V záviru elánku je návrh implementace modelu do perceptuálního kompresního algoritmu. In this paper, we present a novel approach to predict the perceived image quality. Properties of the Human Visual System (HVS) were exploited to select a set of suitable metrics. These metrics are extracted while comparing the reference and distorted image. Mutual Information (MI) and Principal Component Analysis (PCA) were used to obtain an optimal set of objective features that best describe the perceived image quality in respect to subjective scores from human observers. The impairment feature vector is for¬warded to the Artificial Neural Network (ANN) where the features are combined and the predicted quality score is computed. Parameters of the ANN are adjusted using Mean Opinion Scores (MOS) obtained from the group of assessors. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings. Possible incorporation of the model into a perceptual image-coding algorithm is proposed. In this paper, we present a novel approach to predict the perceived image quality. Properties of the Human Visual System (HVS) were exploited to select a set of suitable metrics. These metrics are extracted while comparing the reference and distorted image. Mutual Information (MI) and Principal Component Analysis (PCA) were used to obtain an optimal set of objective features that best describe the perceived image quality in respect to subjective scores from human observers. The impairment feature vector is for¬warded to the Artificial Neural Network (ANN) where the features are combined and the predicted quality score is computed. Parameters of the ANN are adjusted using Mean Opinion Scores (MOS) obtained from the group of assessors. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings. Possible incorporation of the model into a perceptual image-coding algorithm is proposed.
dcterms:title
Model vyu3ívající umilou neuronovou síť pro perceptuální hodnocení obrazové kvality v kompresních algoritmech pro kompresi obrazu A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms
skos:prefLabel
A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms Model vyu3ívající umilou neuronovou síť pro perceptuální hodnocení obrazové kvality v kompresních algoritmech pro kompresi obrazu
skos:notation
RIV/68407700:21230/06:03120527!RIV07-GA0-21230___
n3:strany
631507-1 ; 631507-10
n3:aktivita
n16:P n16:Z
n3:aktivity
P(GA102/05/2054), Z(MSM6840770014)
n3:dodaniDat
n10:2007
n3:domaciTvurceVysledku
n19:8983763
n3:druhVysledku
n8:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
463592
n3:idVysledku
RIV/68407700:21230/06:03120527
n3:jazykVysledku
n7:eng
n3:klicovaSlova
artificial neural network; image compression; image processing; image quality
n3:klicoveSlovo
n15:artificial%20neural%20network n15:image%20compression n15:image%20quality n15:image%20processing
n3:kontrolniKodProRIV
[04E8585DCBDA]
n3:mistoKonaniAkce
San Diego
n3:mistoVydani
Bellingham
n3:nazevZdroje
Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX
n3:obor
n17:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n5:GA102%2F05%2F2054
n3:rokUplatneniVysledku
n10:2006
n3:tvurceVysledku
Fliegel, Karel
n3:typAkce
n13:WRD
n3:zahajeniAkce
2006-08-13+02:00
n3:zamer
n9:MSM6840770014
s:numberOfPages
10
n22:hasPublisher
SPIE
n6:isbn
0-8194-6394-9
n14:organizacniJednotka
21230