Tetsuya Shimamura received his B.E., M.E. and PhD degrees in Electrical Engineering from
Keio University in Japan, in 1986, 1988 and 1991, respectively. In 1991, he joined Saitama
University in Japan, where he is currently a Professor of Graduate School of Science and
He was Head of Department of Information and Computer Sciences at Saitama University in 2012 and 2013, and Dean of Information Technology Center in 2014 and 2015. In 1995 and 1996, he joined Loughborough University, UK, and The Queen’s University of Belfast, UK, respectively, as a visiting Professor.
His research interests are in digital signal processing and its applications to speech, audio, image and communication systems. A various range of research is covered such as speech analysis, speech enhancement, image quality assessment, image restoration, wireless communication, sensor network and cognitive radio. He has published over 100 refereed journal articles and 260 international conference proceedings papers. He is an author or co-author of eight books, and a member of the organizing committee of several international conferences.
He has received IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Gold Paper Award, in 2012, WSEAS International Conference on Multimedia Systems and Signal Processing, Best Paper Award, in 2013, and IEEE IFOST, Best Paper Award, in 2014. Also, he is a recipient of Journal of Signal Processing, Best Paper Award, in 2013, 2015, and 2016, and Yahagi Commemorative Award of Journal of Signal Processing, in 2018. He is an IEEE senior member.
A Bridge Between Speech and Image Processing: Image Quality Assessment
In many cases, we need to measure the quality of an image. It is conducted by human eyes in principle, but a fair assessment of the quality of an image requires many opinions or scores from many people. Instead of such a subjective assessment, an objective assessment, which is judged by a computer automatically, is convenient and desired in our practical world. This kind of image quality assessment (IQA), objective IQA, is becoming popular and one of the hot topics in image processing. In this talk, some recent ideas found at Prof. Shimamura’s Lab are revealed, which are based on a combination of two or three metrices to measure the quality of an image. Originally, these ideas come from the field of speech processing. How to measure the quality of a speech signal has been also discussed up to now in the field of speech processing. At Prof. Shimamura’s Lab, a wide range of signal processing techniques are covered and used. Some methods for IQA introduced in this talk are products from a bridge between speech and image processing mentioned above. Additionally, the use of a convolutional neural network (CNN) for IQA is also shown, the type of which is called blind IQA or non-reference IQA, because we do not use the original image to make an assessment. The CNN approach was also devised at Prof. Shimamura’s Lab and press-released last year in Japan. A short video will be shown to conform how accurately the approach provides the IQA scores.