Machine Learning-based Image Compression: Ready for Prime Time?

Wednesday, May 22, 4:55 PM - 5:25 PM
Summit Track: 
Technical Insights II
Mission City M1-M3

Computer Vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine-learned stages or end-to-end learning techniques where enough ground truth data is available. Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine learned compression techniques. This talk will examine practical aspects of deep learned image compression systems as compared with traditional approaches. We consider memory, computation and other aspects in addition to rate-distortion to see when ML-based compression should be considered or avoided. We also discuss approaches using a combination of machine learned and traditional techniques?


Michael Gormish

Research Manager, Clarifai

Michael Gormish is leading research at Clarifai, working on better machine learning for image classification, visual search and object detection. At a previous startup he was responsible for two search engines which connected images from mobile phones to AR experiences. His research as a computer vision scientist has included inventing algorithms used in video games, digital cinema, satellite and medical image acquisition. He led several aspects of the JPEG 2000 standardization and provided key inventions used in photocopiers, digital cameras, tablets and imaging services. Michael was named Ricoh Patent Master for being a co-inventor of over 100 US patents. He earned a Ph.D. degree in Electrical Engineering focused on image and data compression from Stanford University and has served the research community as an Associate Editor of the IEEE Signal Processing Magazine and the Journal of Electronic Imaging. Currently he is interested in changing the world via image understanding.

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