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Towards Hybrid-Optimization Video Coding
Video coding is a mathematical optimization problem of rate and distortion
essentially. To solve this complex optimization problem, two popular video
coding frameworks have been developed: block-based hybrid video coding and
end-to-end learned video coding. If we rethink video coding from the
perspective of optimization, we find that the existing two frameworks represent
two directions of optimization solutions. Block-based hybrid coding represents
the discrete optimization solution because those irrelevant coding modes are
discrete in mathematics. It searches for the best one among multiple starting
points (i.e. modes). However, the search is not efficient enough. On the other
hand, end-to-end learned coding represents the continuous optimization solution
because the gradient descent is based on a continuous function. It optimizes a
group of model parameters efficiently by the numerical algorithm. However,
limited by only one starting point, it is easy to fall into the local optimum.
To better solve the optimization problem, we propose to regard video coding as
a hybrid of the discrete and continuous optimization problem, and use both
search and numerical algorithm to solve it. Our idea is to provide multiple
discrete starting points in the global space and optimize the local optimum
around each point by numerical algorithm efficiently. Finally, we search for
the global optimum among those local optimums. Guided by the hybrid
optimization idea, we design a hybrid optimization video coding framework,
which is built on continuous deep networks entirely and also contains some
discrete modes. We conduct a comprehensive set of experiments. Compared to the
continuous optimization framework, our method outperforms pure learned video
coding methods. Meanwhile, compared to the discrete optimization framework, our
method achieves comparable performance to HEVC reference software HM16.10 in
PSNR
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