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Publication# In-Network View Synthesis for Interactive Multiview Video Systems

Abstract

In multiview applications, camera views can be used as reference views to synthesize additional virtual viewpoints, allowing users to freely navigate within a 3D scene. However, bandwidth constraints may restrict the number of reference views sent to clients, limiting the quality of the synthesized viewpoints. In this work, we study the problem of in-network reference view synthesis aimed at improving the navigation quality at the clients. We consider a distributed cloud network architecture, where data stored in a main cloud is delivered to end users with the help of cloudlets, i.e., resource-rich proxies close to the users. We argue that, in case of limited bandwidth from the cloudlet to the users, re-sampling at the couldlet the viewpoints of the 3D scene (i.e., synthesizing novel virtual views in the cloudlets to be used as new references to the decoder) is beneficial compared to mere subsampling of the original set of camera views. We therefore cast a new reference view selection problem that seeks the subset of views minimizing the distortion over a view navigation window defined by the user under bandwidth constraints. We prove that the problem is NP-hard, and we propose an effective polynomial time algorithm using dynamic programming to solve the optimization problem under general assumptions that cover most of the multiview scenarios in practice. Simulation results confirm the performance gain offered by virtual view synthesis in the network.

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NP-completeness

In computational complexity theory, a problem is NP-complete when: It is a decision problem, meaning that for any input to the problem, the output is either "yes" or "no". When the answer is "yes", this can be demonstrated through the existence of a short (polynomial length) solution. The correctness of each solution can be verified quickly (namely, in polynomial time) and a brute-force search algorithm can find a solution by trying all possible solutions.

NP-hardness

In computational complexity theory, NP-hardness (non-deterministic polynomial-time hardness) is the defining property of a class of problems that are informally "at least as hard as the hardest problems in NP". A simple example of an NP-hard problem is the subset sum problem. A more precise specification is: a problem H is NP-hard when every problem L in NP can be reduced in polynomial time to H; that is, assuming a solution for H takes 1 unit time, Hs solution can be used to solve L in polynomial time.

Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.

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