  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 led to a 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. This dissertation takes an orthogonal approach and leverages 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.
  The key insight of this work is that methods which are motivated by first
principles, i.e., the underlying engineering decisions that were made when the
compression algorithms were developed, are more effective than general methods.
By encoding prior knowledge into the design of the algorithm, the flexibility,
performance, and/or accuracy are improved at the cost of generality...
