Edge computing is an emerging architecture in 5G networks where computing power is provided at the edge of the fixed network, to be as close as possible to the end users. Computation offloading, better communication latency, and reduction of traffic in the core network are just some of the possible benefits. However, the Quality of Experience (QoE) depends significantly on the network performance of the user device towards the edge server vs. cloud server, which is not known a priori and may generally change very fast, especially in heterogeneous, dense, and mobile deployments. Building on the emergence of standard interfaces for the installation and operation of thirdparty edge applications in a mobile network, such as the MultiAccess Edge Computing (MEC) under standardization at the European Telecommunications Standards Institute (ETSI), we propose MECPerf, a tool for user-driven network performance measurements. Bandwidth and latency on different network segments are measured and stored in a central repository, from where they can be analyzed, e.g., by application and service providers without access to the underlying network management services, for run-time resource optimization.

This paper presents a new method to perform unsupervised band selection (UBS) with hyperspectral data. The method provides a probabilistic clustering approach. The band images are clustered in the image space by computing their posterior class probability. Then, for each cluster, the band exhibiting the highest probability of belonging to it is selected as cluster exemplar. More particularly, the proposed method falls into information-maximization clustering methods, where the posterior class probability is modeled and the parameters of the models are derived by maximizing the information between the data and the unknown cluster labels. In this context, we propose a new image representation for hyperspectral images, based on the first- and second-order statistics of multiple image features. We refer to this representation as multiple-feature local statistical descriptors (MLSD). The descriptors are computed with respect to regular grids, and a special pixel selection procedure reduces the number of samples within each block of the grid. A kernel-based model that embeds the MLSD is then proposed for the posterior class probability. The model is finally optimized according to an information-maximization criterion. We conduct several experiments to determine the best parameters for the proposed approach and compare the latter with other state-of-the-art UBS methods. Quantitative evaluations show that, by employing our band selection method, higher performance in terms of classification accuracy and endmember extraction can be achieved in comparison with the state of the art.

This paper presents a new approach for unsupervised band selection in the context of hyperspectral imaging. The hyperspectral band selection (HBS) task is considered as a clustering problem: bands are clustered in the image space; one representative image is then kept for each cluster, to be part of the set of selected bands. The proposed clustering method falls into the family of information-maximization clustering, where mutual information between data features and cluster assignments is maximized. Inspired by a clustering method of this family, we adapt it to the HBS problem and extend it to the case of multiple image features. A pixel selection step is also integrated to reduce the spatial support of the feature vectors, thus mitigating the curse of dimensionality. Experiments with different standard data sets show that the bands selected with our algorithm lead to higher classification performance, in comparison with other state-of-the-art HBS methods.

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.

Cardiac Phase-resolved Blood-Oxygen-Level Dependent (CP-BOLD) MRI provides a unique opportunity to image an ongoing ischemia at rest. However, it requires post-processing to evaluate the extent of ischemia. To address this, here we propose an unsupervised ischemia detection (UID) method which relies on the inherent spatio-temporal correlation between oxygenation and wall motion to formalize a joint learning and detection problem based on dictionary decomposition. Considering input data of a single subject, it treats ischemia as an anomaly and iteratively learns dictionaries to represent only normal observations (corresponding to myocardial territories remote to ischemia). Anomaly detection is based on a modified version of One-class Support Vector Machines (OCSVM) to regulate directly the margins by incorporating the dictionary-based representation errors. A measure of ischemic extent (IE) is estimated, reflecting the relative portion of the myocardium affected by ischemia. For visualization purposes an ischemia likelihood map is created by estimating posterior probabilities from the OCSVM outputs, thus obtaining how likely the classification is correct. UID is evaluated on synthetic data and in a 2D CP-BOLD data set from a canine experimental model emulating acute coronary syndromes. Comparing early ischemic territories identified with UID against infarct territories (after several hours of ischemia), we find that IE, as measured by UID, is highly correlated (Pearson's r=0.84) with respect to infarct size. When advances in automated registration and segmentation of CP-BOLD images and full coverage 3D acquisitions become available, we hope that this method can enable pixel-level assessment of ischemia with this truly non-invasive imaging technique.

This paper presents a novel example-based single-image superresolution procedure that upscales to high-resolution (HR) a given low-resolution (LR) input image without relying on an external dictionary of image examples. The dictionary instead is built from the LR input image itself, by generating a double pyramid of recursively scaled, and subsequently interpolated, images, from which self-examples are extracted. The upscaling procedure is multipass, i.e., the output image is constructed by means of gradual increases, and consists in learning special linear mapping functions on this double pyramid, as many as the number of patches in the current image to upscale. More precisely, for each LR patch, similar self-examples are found, and, because of them, a linear function is learned to directly map it into its HR version. Iterative back projection is also employed to ensure consistency at each pass of the procedure. Extensive experiments and comparisons with other state-of-the-art methods, based both on external and internal dictionaries, show that our algorithm can produce visually pleasant upscalings, with sharp edges and well reconstructed details. Moreover, when considering objective metrics, such as Peak signal-to-noise ratio and Structural similarity, our method turns out to give the best performance.

This paper presents a new method to construct a dictionary for example-based super-resolution (SR) algorithms. Example-based SR relies on a dictionary of correspondences of low-resolution (LR) and high-resolution (HR) patches. Having a fixed, prebuilt, dictionary, allows to speed up the SR process; however, in order to perform well in most cases, we need to have big dictionaries with a large variety of patches. Moreover, LR and HR patches often are not coherent, i.e. local LR neighborhoods are not preserved in the HR space. Our designed dictionary learning method takes as input a large dictionary and gives as an output a dictionary with a “sustainable” size, yet presenting comparable or even better performance. It firstly consists of a partitioning process, done according to a joint k-means procedure, which enforces the coherence between LR and HR patches by discarding those pairs for which we do not find a common cluster. Secondly, the clustered dictionary is used to extract some salient patches that will form the output set.

This paper presents a new nonnegative dictionary learning method, to decompose an input data matrix into a dictionary of nonnegative atoms, and a representation matrix with a strict ℓ0-sparsity constraint. This constraint makes each input vector representable by a limited combination of atoms. The proposed method consists of two steps which are alternatively iterated: a sparse coding and a dictionary update stage. As for the dictionary update, an original method is proposed, which we call K-WEB, as it involves the computation of k WEighted Barycenters. The so designed algorithm is shown to outperform other methods in the literature that address the same learning problem, in different applications, and both with synthetic and “real” data, i.e. coming from natural images.

In this paper we present a super-resolution (SR) method for upscaling low-resolution (LR) video sequences, that relies on the presence of periodic high-resolution (HR) key frames, and validate it in the context of video compression. For a given LR intermediate frame, the HR details are retrieved patch-by-patch by taking sparse linear combinations of patches found in the neighbor key frames. The performance of the video SR algorithm is assessed in a scheme where only some key frames from an original HR sequence are directly encoded; the remaining intermediate frames are down-sampled to LR and encoded as well, with a possibly different quantization parameter. SR is then finally employed to upscale these frames. For comparison, we consider the best case where the whole original HR sequence is encoded. With respect to this case, our SR-based approach is shown to bring a certain gain for low bit-rates (consistent when all frames are encoded independently), i.e. when a poor encoding can actually benefit of the special processing of the intermediate frames, so proving that video SR can be an useful tool in realistic scenarios.

International audience

This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses Semi-Nonnegative Matrix Factorization (SNMF). Each low-resolution (LR) input patch is approximated by a linear combination of nearest neighbors taken from a dictionary. This dictionary stores low-resolution and corresponding high-resolution (HR) patches taken from natural images and is thus used to infer the HR details of the super-resolved image. The entire neighbor embedding procedure is carried out in a feature space. Features which are either the gradient values of the pixels or the mean-subtracted luminance values are extracted from the LR input patches, and from the LR and HR patches stored in the dictionary. The algorithm thus searches for the K nearest neighbors of the feature vector of the LR input patch and then computes the weights for approximating the input feature vector. The use of SNMF for computing the weights of the linear approximation is shown to have a more stable behavior than the use of LLE and lead to significantly higher PSNR values for the super-resolved images.