Video Quality Assessment in Experimental Retinal Imaging
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120035" target="_blank" >RIV/00216305:26220/16:PU120035 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Video Quality Assessment in Experimental Retinal Imaging
Popis výsledku v původním jazyce
This paper is focused on video quality assessment of experimental retinal video image sequences with two different approaches (automatic and expert analysis). Methods utilizing both video quality evaluations are presented and described within the paper. Evaluations consider the most frequent retinal imaging artefacts (contrast distortion, noise and blurriness). Automatic evaluating algorithm estimates the image quality based on three different parameters characterizing the image over time (signal to noise ratio - SNR, blurriness quality evaluation - BQE and contrast quality evaluation - CQE). Four different experts performed the evaluation with their subjective image perceiving and assess the degree of noise, blurriness and contrast in the images over time. The reliability of proposed algorithm for automatic retinal image video quality assessment seems to be correct for all three parameters. Medians of SNR as well as the medians of CQE are decreasing with decreasing image quality evaluated with 4 expe
Název v anglickém jazyce
Video Quality Assessment in Experimental Retinal Imaging
Popis výsledku anglicky
This paper is focused on video quality assessment of experimental retinal video image sequences with two different approaches (automatic and expert analysis). Methods utilizing both video quality evaluations are presented and described within the paper. Evaluations consider the most frequent retinal imaging artefacts (contrast distortion, noise and blurriness). Automatic evaluating algorithm estimates the image quality based on three different parameters characterizing the image over time (signal to noise ratio - SNR, blurriness quality evaluation - BQE and contrast quality evaluation - CQE). Four different experts performed the evaluation with their subjective image perceiving and assess the degree of noise, blurriness and contrast in the images over time. The reliability of proposed algorithm for automatic retinal image video quality assessment seems to be correct for all three parameters. Medians of SNR as well as the medians of CQE are decreasing with decreasing image quality evaluated with 4 expe
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2016
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů