Video Quality Assessment using Visual Attention Approach for Sign Language
Visual information is very important in human perception
of surrounding world. Video is one of the most common ways to
capture visual information. The video capability has many benefits
and can be used in various applications. For the most part, the
video information is used to bring entertainment and help to relax,
moreover, it can improve the quality of life of deaf people. Visual
information is crucial for hearing impaired people, it allows them to
communicate personally, using the sign language; some parts of the
person being spoken to, are more important than others (e.g. hands,
face). Therefore, the information about visually relevant parts of the
image, allows us to design objective metric for this specific case. In
this paper, we present an example of an objective metric based on
human visual attention and detection of salient object in the observed
scene.
[1] M. Mardiak, Video quality assessment, (in Slovak), Slovak University of
Technology. Bratislava, 2012.
[2] D. Tarcsiova, Pedagogics of hearing-impaired, (in Slovak), MABAG spol.
s r. o., Bratislava, 2008.
[3] ITU-R, Methodology for the subjective assessment of the quality of
television pictures, International Telecommunication Union Radiocommunication
Sector, Tech. Rep. BT.500-11, 2002.
[4] F. Makan, Elektroacoustics, (in Slovak), Slovak University of Technology.
Bratislava, 1995.
[5] P. Heribanova, J. Polec, S. Ondrusova, M. Hostovecky, Intelligibility of
cued speech on video, World Academy of Science, Engineering and
Technology, Iss. 79, pp. 492-496, 2011.
[6] ITU-T, Objective perceptual video quality measurement techniques for
digital cable television in the presence of a full reference, Recommendation
J.144, 2004.
[7] Z. Wang, L. Lu, and A. C. Bovik, Video quality assessment based on
structural distortion measurement, Signal Process. Image Commun. 19,
2004, pp. 121132, 2004.
[8] Ch. Li, A. C. Bovik, Content-weighted video quality assessment using
a three-component image model, Journal of Electronic Imaging, 19(1),
011003-1-9, , 2010.
[9] Goldstein E. B.: Cognitive Psychology: Connecting Mind, Research and
Everyday Experience. ISBN-10: 0495095575 ISBN-13: 9780495095576,
Thomson/Wadsworth, 2008.
[10] J. Kucerova, Saliency Map Augmentation with Facial Detection, CESCG
2011, Proceedings of the 15th Central European Seminar on Computer
Grapgics. - Vienna : Institute of Computer Graphics and Algorithms,
ISBN 978-3-9502533-7, Pages 61-66, 2011.
[11] Y. Hu et al., Adaptive Local Context Suppression of Multiple Cues for
Salient Visual Attention Detection. In IEEE International conference on
multimedia and expo, Pages 1-4, 2005.
[12] E. Sikudova, Comparison of color spaces for face detection in digitized
paintings, In:Spring Conference on Computer Graphics : SCCG 2007 :
Conference Proceedings. Bratislava : Comenius University, ISBN 978-
80-223-2292-8, Pages 135-140, 2007.
[13] F. Xiao, DCT-based Video Quality Evaluation, MSU Graphics and Media
Lab (Video Group), 2000.
[14] Ch. Li, A. C. Bovik, Content-partitioned structural similarity index for
image quality assessment, Signal Processing: Image Communication, 25
(2010)517526.
[1] M. Mardiak, Video quality assessment, (in Slovak), Slovak University of
Technology. Bratislava, 2012.
[2] D. Tarcsiova, Pedagogics of hearing-impaired, (in Slovak), MABAG spol.
s r. o., Bratislava, 2008.
[3] ITU-R, Methodology for the subjective assessment of the quality of
television pictures, International Telecommunication Union Radiocommunication
Sector, Tech. Rep. BT.500-11, 2002.
[4] F. Makan, Elektroacoustics, (in Slovak), Slovak University of Technology.
Bratislava, 1995.
[5] P. Heribanova, J. Polec, S. Ondrusova, M. Hostovecky, Intelligibility of
cued speech on video, World Academy of Science, Engineering and
Technology, Iss. 79, pp. 492-496, 2011.
[6] ITU-T, Objective perceptual video quality measurement techniques for
digital cable television in the presence of a full reference, Recommendation
J.144, 2004.
[7] Z. Wang, L. Lu, and A. C. Bovik, Video quality assessment based on
structural distortion measurement, Signal Process. Image Commun. 19,
2004, pp. 121132, 2004.
[8] Ch. Li, A. C. Bovik, Content-weighted video quality assessment using
a three-component image model, Journal of Electronic Imaging, 19(1),
011003-1-9, , 2010.
[9] Goldstein E. B.: Cognitive Psychology: Connecting Mind, Research and
Everyday Experience. ISBN-10: 0495095575 ISBN-13: 9780495095576,
Thomson/Wadsworth, 2008.
[10] J. Kucerova, Saliency Map Augmentation with Facial Detection, CESCG
2011, Proceedings of the 15th Central European Seminar on Computer
Grapgics. - Vienna : Institute of Computer Graphics and Algorithms,
ISBN 978-3-9502533-7, Pages 61-66, 2011.
[11] Y. Hu et al., Adaptive Local Context Suppression of Multiple Cues for
Salient Visual Attention Detection. In IEEE International conference on
multimedia and expo, Pages 1-4, 2005.
[12] E. Sikudova, Comparison of color spaces for face detection in digitized
paintings, In:Spring Conference on Computer Graphics : SCCG 2007 :
Conference Proceedings. Bratislava : Comenius University, ISBN 978-
80-223-2292-8, Pages 135-140, 2007.
[13] F. Xiao, DCT-based Video Quality Evaluation, MSU Graphics and Media
Lab (Video Group), 2000.
[14] Ch. Li, A. C. Bovik, Content-partitioned structural similarity index for
image quality assessment, Signal Processing: Image Communication, 25
(2010)517526.
@article{"International Journal of Information, Control and Computer Sciences:63542", author = "Julia Kucerova and Jaroslav Polec and Darina Tarcsiova", title = "Video Quality Assessment using Visual Attention Approach for Sign Language", abstract = "Visual information is very important in human perception
of surrounding world. Video is one of the most common ways to
capture visual information. The video capability has many benefits
and can be used in various applications. For the most part, the
video information is used to bring entertainment and help to relax,
moreover, it can improve the quality of life of deaf people. Visual
information is crucial for hearing impaired people, it allows them to
communicate personally, using the sign language; some parts of the
person being spoken to, are more important than others (e.g. hands,
face). Therefore, the information about visually relevant parts of the
image, allows us to design objective metric for this specific case. In
this paper, we present an example of an objective metric based on
human visual attention and detection of salient object in the observed
scene.", keywords = "sign language, objective video quality, visual attention,
saliency", volume = "6", number = "5", pages = "722-6", }