Monthly Archives: February 2014

Dr. Hui Yong Kim, a senior researcher of Broadcasting and Telecommunications Media Research Laboratory of Electronics and Telecommunications Research Institute (ETRI) and also a former adjunct professor at University of Science and Technology (UST) in Korea, visited Media Communication Lab since September 2013. He generously spent some of his short time with us to share his research.

Could you give us a brief introduction about your research experience?

I would like to start from my PhD research experience. I worked on visual surveillance project during my PhD years, and the project required object detection and segmentation from videos. By separating background and foreground, I compressed detected objects and background differently to increase quality of objects area. After graduation, I joined a start-up company because I wanted to get a whole picture of how the real devices are developed. There, I mainly developed H.264 real-time codec software for several video communication systems, such as IP video phone and other consumer products. Because I was a manager in multimedia team, I also needed to care other issues like middle-wares, communication protocols, audio codecs, graphical user interfaces, and even mechanical designs. After few years, I decided to move to ETRI to continue my research path. In ETRI, I participated and contributed to developments of several international standards, including MPEG Multimedia Application Formats and High Efficiency Video Coding (HEVC). During the process, I developed more than 70 patent algorithms.

Could you also talk about your research interest?

My research interests include video/image signal processing and compression for realistic video services, such as UHDTV and 3DTV. They are called as “realistic media”, which implies making things real to human. My research goal is how to maximize visual reality to the user efficiently under [...]

The Asia-Pacific Signal and Information Processing Association (APSIPA) is a non-profit organization with the following objectives as its mission:
1) Providing education, research and development exchange platforms for both academia and industry
2) Organizing common-interest activities for researchers and practitioners
3) Facilitating collaboration with region-specific focuses and promoting leadership for worldwide events
4) Disseminating research results and educational material via publications, presentations, and electronic media
5) Offering personal and professional career opportunities with development information and networking
The idea of APSIPA was born in Hawaii in 2007. It was formally incorporated as “Asia Pacific Signal and Information Processing Association Limited” in Hong Kong on July 23, 2009. Dr. Sadaoki Furui was elected as the first President and all other BoG members were elected at the BoG meeting held on the first day of APSIPA ASC 2009, October 4, 2009. Professor C.-C. Jay Kuo succeeded Dr. Furui to become the 2nd President of APSIPA in 2013 December.
The APSIPA Board of Governs (BoG) has set up a goal to reach 400 APSIPA Friend Labs and 5000 e-membership before the end of 2014. The membership drive consists 4 phases with 3 months per phase. By the end of Phase I (1/31/2014), APSIPA had 112 Friend Labs and 2002 e-members. The target goal (100 Friend Labs and 2000 e-members) was reached. This success was attributed to the joint and tireless of APSIPA leaders. All MCL members have worked together to promote APSIPA and recruit APSIPA new e-members.

Hyunsuk Ko successfully passed his Qualifying exam on Jan 28th. The title is “A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System”. His Qual Exam Committee includes: Jay Kuo (Chair), Antonio Ortega, Keith Jenkins, Sandy Sawchuk and Aiichiro Nakano (Outside Member).

The design of a novel ParaBoost (parallel-boosting) stereoscopic image quality assessment (PBSIAQ) system based on machine learning is presented in his work. The PBSIQA system consists of two stages. In the first stage, they classify multiple distortions into multiple types, and develop a set of individual quality scorers targeting at specific distortion types and working in parallel in the first stage. In the second stage, to obtain the best overall performance against the whole database, they adopt the parallel boosting idea from machine learning. Extensive experimental results are provided to demonstrate that the superb performance of the proposed PBSIQA scorer, which outperforms existing stereo image quality assessment (SIQA) indices by a significant margin.