"2006-08-13+02:00"^^ . "Model vyu3\u00EDvaj\u00EDc\u00ED umilou neuronovou s\u00ED\u0165 pro perceptu\u00E1ln\u00ED hodnocen\u00ED obrazov\u00E9 kvality v kompresn\u00EDch algoritmech pro kompresi obrazu"@cs . . "0-8194-6394-9" . . . "San Diego" . . . . "A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms"@en . . "V tomto el\u00E1nkuje pops\u00E1n nov\u00FD po\u00EDstup predikce obrazov\u00E9 kvality. Vhodn\u00E9 metriky byly vybr\u00E1ny s ohledem na vlastnosti lidsk\u00E9ho visu\u00E1ln\u00EDho syst\u00E9mu (HVS). Tyto metriky jsou vypoe\u00EDt\u00E1ny porovn\u00E1n\u00EDm origin\u00E1ln\u00EDho a zkreslen\u00E9ho obrazu. Na poedzpracov\u00E1n\u00ED dat byla pou3ita pokroeil\u00E1 metoda (Mutual Information – MI, Principal Component Analysis – PCA) tak, aby odhad kvality z modelu byl bl\u00EDzk\u00FD subjektivn\u00EDm soudum pozorovatelu. Vektor zkresluj\u00EDc\u00EDch parametru je zpracov\u00E1n ANN, kde jsou tyto parametry zkombinov\u00E1ny a ureena predikce kvality. Parametry ANN byly nastaveny s pou3it\u00EDm subjektivn\u00EDch soudu skupinou pozorovatelu (MOS). Dobr\u00E1 prediken\u00ED schopnost modelu byla demonstrov\u00E1na ve smyslu vysok\u00E9 korelace mezi subjektivn\u00EDmi soudy MOS a v\u00FDstupem neuronov\u00E9 s\u00EDti. V z\u00E1viru el\u00E1nku je n\u00E1vrh implementace modelu do perceptu\u00E1ln\u00EDho kompresn\u00EDho algoritmu."@cs . "RIV/68407700:21230/06:03120527" . "21230" . . . "artificial neural network; image compression; image processing; image quality"@en . . . . . "RIV/68407700:21230/06:03120527!RIV07-GA0-21230___" . . . "P(GA102/05/2054), Z(MSM6840770014)" . "10"^^ . "463592" . "A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms" . . . . "SPIE" . "Model vyu3\u00EDvaj\u00EDc\u00ED umilou neuronovou s\u00ED\u0165 pro perceptu\u00E1ln\u00ED hodnocen\u00ED obrazov\u00E9 kvality v kompresn\u00EDch algoritmech pro kompresi obrazu"@cs . "1"^^ . "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." . "Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX" . "1"^^ . "631507-1 ; 631507-10" . "[04E8585DCBDA]" . "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."@en . "A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms"@en . "A model utilizing artificial neural network for perceptual image quality assessment in image compression algorithms" . . "Fliegel, Karel" . "Bellingham" .