The deep learning revolution incited by the 2012 Alexnet paper has been transformative for the field of computer vision. Many problems which were severely limited using classical solutions are now seeing unprecedented success. The rapid proliferation of deep learning methods has lead to sharp increase in their use in consumer and embedded applications. One consequence of consumer and embedded applications is lossy multimedia compression which is required to engineer the efficient storage and transmission of data in these real-world scenarios. As such, there has been increased interest in a deep learning solution for multimedia compression which would allow for higher compression ratios and increased visual quality.

The deep learning approach to multimedia compression, so called Learned Multimedia Compression, involves computing a compressed representation of an image or video using a deep network for the encoder and the decoder. While these techniques have enjoyed impressive academic success, their industry adoption has been essentially non-existent. Classical compression techniques like JPEG and MPEG are too entrenched in modern computing to be easily replaced. In this dissertation we take an orthogonal approach and leverage deep learning to improve the compression fidelity of these classical algorithms. This allows the incredible advances in deep learning to be used for multimedia compression without threatening the ubiquity of the classical methods.

We begin by reviewing three completed works in this area. The first work, which is foundational, unifies the disjoint mathematical theories of compression and deep learning allowing deep networks to operate on compressed data directly. The second work shows how deep learning can be used to correct information loss in JPEG compression in the most general setting. This allows images to be encoded at high compression ratios while still maintaining visual fidelity. The third work examines how deep learning based inferencing tasks, like classification, detection, and segmentation, behave in the the presence of classical compression and how to mitigate performance loss. As in the previous work, this allows images to be compressed further but this time without accuracy loss on downstream learning tasks. We close with a survey of several proposed future works in this and related areas to be completed as part of the final dissertation

Examining Committee: 

 

                          Chair:               Dr. Abhinav Shrivastava              
                          Co-Chair:       Dr. Larry S. Davis
                          Dept rep:         Dr. Ramani Duraiswami 
                          Members:        Dr. Michael A. Isnardi 
                                                   Dr.  Dinesh Manocha