  The dissertation proposes the use of a multi-objective optimization framework
for designing and selecting among enhanced GOP configurations in video
compression standards. The proposed methods achieve fine optimization over a
set of general modes that include: (i) maximum video quality, (ii) minimum
bitrate, (iii) maximum encoding rate (previously minimum encoding time mode)
and (iv) can be shown to improve upon the YouTube/Netflix default encoder mode
settings over a set of opposing constraints to guarantee satisfactory
performance. The dissertation describes the implementation of a codec-agnostic
approach using different video coding standards (x265, VP9, AV1) on a wide
range of videos derived from different video datasets. The results demonstrate
that the optimal encoding parameters obtained from the Pareto front space can
provide significant bandwidth savings without sacrificing video quality. This
is achieved by the use of effective regression models that allow for the
selection of video encoding settings that are jointly optimal in the encoding
time, bitrate, and video quality space. The dissertation applies the proposed
methods to x265, VP9, AV1 and using new GOP configurations in x265, delivering
over 40% of the optimal encodings in two standard reference videos.
