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Alan Bovik has a storied career. An American engineer, vision scientist and Primetime Emmy Award winner, Bovik holds the Cockrell Family Endowed Regents Chair in Engineering at The University of Texas at Austin, where he has been the Director of the Laboratory for Image and Video Engineering for more than three decades. He is also the recent winner of the IEEE Fourier Award for Signal Processing, which is sponsored by the IEEE Signal Processing Society and the IEEE Circuits and Systems Society.
What stands out most about Bovik is not just his accolades – it’s the passion he has for his field and the vibrant approach he takes while teaching it. The IEEE Signal Processing Society (SPS) caught up with Bovik this month to learn more about his research, his thoughts on signal processing’s “next big thing,” and advice he’d give young professionals.
This interview has been edited for length and clarity.
SPS: How did you enter the field? What piqued your interest?
Alan Bovik: Back in college as an undergraduate, I was very interested in control system theory, so I thought I'd go to grad school and study that topic. I started doing research on stepper motors, which control printers and scanners with great precision. They're quite useful, but I found it really boring, actually. At the same time, I was taking these amazing classes in digital signal processing and image processing. They appealed to me immediately as I'm a very visual person. I went to talk to Dave Munson, and later Tom Huang, and I started working with them – two of the most famous people in signal processing ever. Since then, I've been studying image processing for 40 years.
SPS: What do you currently do?
Bovik: I'm a professor at the University of Texas at Austin. I've been here since 1984 doing what professors do: teaching and research. I teach image and video processing, and I've been doing that since I started my job. It's what I love, and it's why I get up in the morning: to work with students. A big part of teaching is my work with graduate students. My lab is funded by amazing companies like Netflix, Oculus, YouTube and Facebook to create perceptual video processing models that are being used to effect the viewing experiences of hundreds of millions of people every day. I feel very lucky.
SPS: Could you explain more about the particular research you’re conducting?
Bovik: We are designing quality models that can affect the entire video work flow of these companies. For example, suppose that you're watching Netflix and you order something like, I don't know, pick one of the famous Netflix programs…
SPS: Orange Is the New Black. We'll go with that one.
Bovik: That's a good one, okay. Orange Is the New Black. When you order that video, then about 15 or so versions of it will appear in the Cloud, at different levels of video compression, ready to be streamed depending on the bandwidth conditions. If there's a lot of bandwidth available, you can send a really high-quality video segment; if it's not very good bandwidth, then you send a lower quality one. The way Netflix decides how to do that is by perceptually optimizing the quality of each of the encodes at each bitrate. We have been very happy to be part of that kind of work.
SPS: What are you most passionate about in your field?
Bovik: Well, I have a niche area that I research: perception-based video processing. When I first started working in it, there were very few people working in the area. I really believe that the perceptual aspects of image and video processing – and I would say signal processing, in general, including things like speech and audio – are terrifically important. For me, the visual brain is the ultimate receiver of pictures and videos, so the more we learn about it, then the better we can deliver enjoyable videos. I'm most passionate about learning about the latest advances in visual neuroscience and trying to create ways to bring them into video system design and other image processing applications.
SPS: What is one signal processing technology you believe people should know right now?
Bovik: I do have something in mind that I've been thinking about myself recently. It’s called Generative Adversarial Networks. This is well-known and red hot in the AI community and the neural network community, but it hasn't really been applied much in the digital signal processing field. I think it really has limitless possibilities in DSP to create models and algorithms having a complexity we haven't otherwise been able to understand or put together. I think Generative Adversarial Networks will find a big role in DSP sometime in the near future.
SPS: Do you see that as an opportunity for students potentially looking to find their niche?
Bovik: Yes. I'm guessing that students are probably more aware of these than professors like me, who at times plod along using the old ways, and this is just the new way. I believe I learn more from my students than they learn from me. In my opinion, students are going to be aware of this new direction and they’ll be leading the pack.
SPS: What advice would you give to a young engineer exploring a career in signal processing?
Bovik: You should read the materials, transactions and magazines, but you should also follow your own creative instincts and interests. Don't just follow the pack. If everybody's working on some hot topic, like today's deep learning, then I have to tell you, the important work in that field is probably already done. If you want to do something big, then do something that people aren't all working on. If it's fundamental and unsolved and you're interested, then it's probably a good problem. Find the ones that interest you and don't just follow the pack. Eventually, your work may become important.
SPS: How important do you feel networking is and professional associations are to young students and professionals in general?
Bovik: I think they're especially important for students and young professionals. In my own experience, I know, and have become good friends with so many people today in the signal processing field that I met when I was young. I came up with these individuals as a young engineer and professor and we shared the same experiences of seeking funding and figuring out what to do. We often chat when the field is changing, or when something in the community needs to be addressed, or if a student is in need. It’s a support structure you can’t replace.
SPS: Do you have any additional advice?
Bovik: Be sure you’ve entered the field for the right reasons and that you work on problems for the right reasons. You have to love your work or you shouldn't be doing it. You’ve got one life to live, and you should be doing it, not for the money or because somebody else wants you to be something, but so you can be yourself. And, if you have the talent, you can be that as a signal processing engineer.