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Advancements over the last decade in video acquisition and display technologies lead to a continuous increase of video content resolution. These aspects combined with the shift towards cloud multimedia services and the underway adoption of High Efficiency Video Coding standards (HEVC) create a lot of interest for Super-Resolution (SR) and video enhancing techniques. Recent works showed that proximal...
Super Resolution (SR) addresses the problem of image and video upscaling. Most of the best performing SR methods do not take into account any compression prior into the degradation model. Consequently, compression artifacts can be undesirably amplified during SR. In the present work, we propose a novel HEVC-dedicated approach for embedding SR results into a domain that closely fits the compressed...
Optimal rate allocation is among the most challenging tasks to perform in the context of multi-view video coding, because of the dependency between frames induced by motion compensation and depth image-based rendering. In this paper, using a recursive rate-distortion model that explicitly takes into account these dependencies, we approach the frame-level rate allocation as a convex optimization problem...
Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the structure tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the nonlocal variations, jointly for the different components,...
Quality of experience in future home devices is foreseen to drastically increase, with the increase in image resolution. Displays with a horizontal resolution of 4K pixels are already appearing, and 8K Super-HiVision has already been demonstrated. Currently, only spatial upsampling of conventional HD format is performed to match the resolution of such displays. In this paper, we propose a novel method...
In this paper, we propose a new approach for estimating depth maps of stereo images which are prone to various types of noise. This method, based on a parallel proximal algorithm, gives a great flexibility in the choice of the constrained criterion to be minimized, thus allowing us to take into account different types of noise distributions. Our main objective is to present an iterative estimation...
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