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A convex optimization framework for video quality and resolution enhancement from multiple descriptions

Abstract : Transmission and compression technologies advancement over the past decade led to a shift of multimedia content towards cloud systems. Multiple copies of the same video are available through numerous distribution systems. Different compression levels, algorithms and resolutions are used to match the requirements of particular applications. As 4k display technologies are rapidly adopted, resolution enhancement algorithms are of vital importance. Current solutions do not take into account the particularities of different video encoders, while video reconstruction methods from compressed sources do not provide resolution enhancement. In this paper, we propose a multi source compressed video enhancement framework where each description can have a different compression level and resolution. Using a variational formulation based on a modern proximal dual splitting algorithm, we efficiently combine multiple descriptions of the same video. Two applications are proposed: combining two compressed Low Resolution (LR) descriptions of a video sequence into a High Resolution (HR) description and enhancing a compressed HR video using a LR compressed description. Tests are performed over multiple video sequences encoded with High Efficiency Video Coding (HEVC), at different compression levels and resolutions obtained through multiple down-sampling methods.
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https://hal.archives-ouvertes.fr/hal-01900417
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Andrei Purica, Benoit Boyadjis, Béatrice Pesquet-Popescu, Frédéric Dufaux, Cyril Bergeron. A convex optimization framework for video quality and resolution enhancement from multiple descriptions. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (4), pp.1661-1674. ⟨10.1109/TIP.2018.2880567⟩. ⟨hal-01900417⟩

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