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ZTE Communications ›› 2013, Vol. 11 ›› Issue (1): 17-26.

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Estimating Reduced-Reference Video Quality for Quality-Based Streaming Video

Luigi Atzori, Alessandro Floris, Giaime Ginesu, and Daniele D. Giusto   

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari 09123, Italy
  • 收稿日期:2013-01-08 出版日期:2013-03-25 发布日期:2013-03-25
  • 作者简介:Luigi Atzori (l.atzori@diee.unica.it) has been an assistant professor at the University of Cagliari, Italy, since 2000. His main research interest is service management in next generation networks, with particular attention to QoS, service-oriented networking, bandwidth management, and multimedia networking. He has published more than 80 journal articles and refereed conference papers. Dr. Atzori has received the Telecom Italia award for an outstanding Master’s thesis in telecommunications and has been awarded a Fulbright Scholarship (11/2003-05/2004) to work on video streaming in the Department of Electrical and Computer Engineering, University of Arizona. He is a senior member of the IEEE, a member of the IEEE Multimedia Communications Committee (MMTC), and co-chair of the MMTC IG on Quality of Experience. He has been the editor for Wireless Networks Journal, published by ACM/Springer, and guest editor of IEEE Communications Magazine, Monet Journal, and Signal Processing: Image Communications.

    Alessandro Floris (alessandro.floris@diee.unica.it) received his MSc degree in electronic engineering from the University of Cagliari in 2011. He was awarded a CNIT research grant from June 2011 to June 2012. He is currently research fellow in the Department of Electric and Electronic Engineering (DIEE), University of Cagliari, Italy. His research interests are QoE estimation for multimedia streaming using reduced-reference approaches.

    Giaime Ginesu (g.ginesu@diee.unica.it) received his MSc degree in electronic engineering in 2001. His thesis was on thermal image processing and pattern recognition. In 2005, he received his PhD degree in electronic engineering from the University of Cagliari, Italy. In 2001, he worked at the Institute for Telecommunications, Technical University of Braunschweig, Germany. There he worked on thermographic image processing with Professor V. Maergner. In 2003, he was a visiting scholar at the Rensselaer Polytechnic Institute, New York, and worked on volumetric data coding with Professor W. A. Pearlman. He is currently an adjunct professor at the University of Cagliari, Italy. Since 2007, he has been involved in ICT project management at the DG for Technological Innovation, Regione Autonoma della Sardegna. His research interests include signal processing, standards, and transmission. He is a member of IEEE.

    Daniele D. Giusto (ddgiusto@unica.it) has been a full professor of telecommunications at the University of Cagliari and director of CNIT Multimedia Communications Lab since 2002. His research interests include image and video processing and coding, multimedia systems, digital television, pictorial databases, and personal communications. Professor Giusto is a senior member of IEEE, the recipient of the 1993 AEI Ottavio Bonazzi Best Paper Award, and co-recipient of the 1998 IEEE Chester Sall Best Paper Award. Since 1999, he has been the head of the Italian delegation in the ISO-JPEG Standardization Committee. In 2007, he was appointed to the IEEE Standard Activities board (RevCom).

Estimating Reduced-Reference Video Quality for Quality-Based Streaming Video

Luigi Atzori, Alessandro Floris, Giaime Ginesu, and Daniele D. Giusto   

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari 09123, Italy
  • Received:2013-01-08 Online:2013-03-25 Published:2013-03-25
  • About author:Luigi Atzori (l.atzori@diee.unica.it) has been an assistant professor at the University of Cagliari, Italy, since 2000. His main research interest is service management in next generation networks, with particular attention to QoS, service-oriented networking, bandwidth management, and multimedia networking. He has published more than 80 journal articles and refereed conference papers. Dr. Atzori has received the Telecom Italia award for an outstanding Master’s thesis in telecommunications and has been awarded a Fulbright Scholarship (11/2003-05/2004) to work on video streaming in the Department of Electrical and Computer Engineering, University of Arizona. He is a senior member of the IEEE, a member of the IEEE Multimedia Communications Committee (MMTC), and co-chair of the MMTC IG on Quality of Experience. He has been the editor for Wireless Networks Journal, published by ACM/Springer, and guest editor of IEEE Communications Magazine, Monet Journal, and Signal Processing: Image Communications.

    Alessandro Floris (alessandro.floris@diee.unica.it) received his MSc degree in electronic engineering from the University of Cagliari in 2011. He was awarded a CNIT research grant from June 2011 to June 2012. He is currently research fellow in the Department of Electric and Electronic Engineering (DIEE), University of Cagliari, Italy. His research interests are QoE estimation for multimedia streaming using reduced-reference approaches.

    Giaime Ginesu (g.ginesu@diee.unica.it) received his MSc degree in electronic engineering in 2001. His thesis was on thermal image processing and pattern recognition. In 2005, he received his PhD degree in electronic engineering from the University of Cagliari, Italy. In 2001, he worked at the Institute for Telecommunications, Technical University of Braunschweig, Germany. There he worked on thermographic image processing with Professor V. Maergner. In 2003, he was a visiting scholar at the Rensselaer Polytechnic Institute, New York, and worked on volumetric data coding with Professor W. A. Pearlman. He is currently an adjunct professor at the University of Cagliari, Italy. Since 2007, he has been involved in ICT project management at the DG for Technological Innovation, Regione Autonoma della Sardegna. His research interests include signal processing, standards, and transmission. He is a member of IEEE.

    Daniele D. Giusto (ddgiusto@unica.it) has been a full professor of telecommunications at the University of Cagliari and director of CNIT Multimedia Communications Lab since 2002. His research interests include image and video processing and coding, multimedia systems, digital television, pictorial databases, and personal communications. Professor Giusto is a senior member of IEEE, the recipient of the 1993 AEI Ottavio Bonazzi Best Paper Award, and co-recipient of the 1998 IEEE Chester Sall Best Paper Award. Since 1999, he has been the head of the Italian delegation in the ISO-JPEG Standardization Committee. In 2007, he was appointed to the IEEE Standard Activities board (RevCom).

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摘要： Reduced-reference (RR) video-quality estimators send a small signature to the receiver. This signature comprises the original video content as well as the video stream. RR quality estimation provides reliability and involves a small data payload. While significant in theory, RR estimators have only recently been used in practice for quality monitoring and adaptive system control in streaming-video frameworks. In this paper, we classify RR algorithms according to whether they are based on a) modeling the signal distortion, b) modeling the human visual system, or c) analyzing the video signal source. We review proposed RR techniques for monitoring and controlling quality in streaming video systems.

关键词: reduced-reference quality estimation, video streaming, adaptive rate control

Abstract: Reduced-reference (RR) video-quality estimators send a small signature to the receiver. This signature comprises the original video content as well as the video stream. RR quality estimation provides reliability and involves a small data payload. While significant in theory, RR estimators have only recently been used in practice for quality monitoring and adaptive system control in streaming-video frameworks. In this paper, we classify RR algorithms according to whether they are based on a) modeling the signal distortion, b) modeling the human visual system, or c) analyzing the video signal source. We review proposed RR techniques for monitoring and controlling quality in streaming video systems.

Key words: reduced-reference quality estimation, video streaming, adaptive rate control