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Video Quality Assessment using Visual Attention Approach for Sign Language

Authors: Jaroslav Polec, Julia Kucerova, Darina Tarcsiova

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, Visual Attention, Saliency, objective video quality

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083173

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References:


[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.