Authors: T. Maugey, G. Petrazzuoli, P. Frossard, M. Cagnazzo, B. Pesquet-Popescu

Title: Reference view selection in DIBR-based multiview coding,  accepted in IEEE Transactions on Image Processing (J15)

Abstract: Augmented reality, interactive navigation in 3D scenes, multiview video and other emerging multimedia applications require large sets of images hence larger data volumes and increased resources compared to traditional video services. The significant increase of the number of images in multiview systems leads to new challenging problems in data representation and data transmission to provide high quality of experience on resource-constrained environments. In order to reduce the size of the data, different multi view video compression strategies have been proposed recently. Most of them use the concept of reference or key views that are used to estimate other images when there is high correlation in the dataset. In such coding schemes, the two following questions become fundamental: i) how many reference views have to be chosen for keeping a good reconstruction quality under coding cost constraints? ii) where to place these key views in the multiview dataset? As these questions are largely overlooked in the literature, we study the reference view selection problem and propose an algorithm for the optimal selection of reference views in multiview coding systems. Based on a novel metric that measures the similarity between the views, we formulate an optimization problem for the positioning of the reference views such that both the distortion of the view reconstruction and the coding rate cost are minimized. We solve this new problem with a shortest path algorithm that determines both the optimal number of reference views and their positions in the image set. We experimentally validate our solution in a practical multiview distributed coding system and in the standardized 3D-HEVC multi view coding scheme. We show that considering the 3D scene geometry in the reference view positioning problem brings significant rate-distortion improvements and outperforms traditional coding strategy that simply selects key frames based on the distance between cameras.