nils-herrmann/clean_scirepeval huggingface dataset:
Distributed signal processing with graph spectral dictionaries

We study the distributed processing of graph signals that are well represented by graph spectral dictionaries. We first analyze the impact of quantization noise in the distributed computation of polynomial dictionary operators that are commonly used in various signal processing tasks. We show that the impact of quantization depends on the graph geometry and on the structure of the spectral dictionaries. Then, we focus on the problem of distributed sparse signal representation that can be solved with an iterative soft thresholding algorithm. We define conditions on the dictionary structure to ensure the convergence of the distributed algorithm and finally propose a dictionary learning solution that permits to control the robustness to quantization noise. Experimental results for reconstruction and denoising of both synthetic and practical signals illustrate the tradeoffs that exist between accurate signal representation and robustness to quantization error in the design of dictionaries operators in distributed graph signal processing.
Online Resource Inference in Network Utility Maximization Problems

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resources efficiently. Network Utility Maximization (NUM) provides a framework to optimize the rate allocation when network resources are limited. Unfortunately, in the scenario where the amount of available resources is not known a priori, classical NUM solving methods do not offer a viable solution. To overcome this limitation we design an overlay rate allocation scheme that attempts to infer the actual amount of available network resources while coordinating the users rate allocation. Due to the general and complex model assumed for the congestion measurements, a passive learning of the available resources would not lead to satisfying performance. The coordination scheme must then perform active learning in order to speed up the resources estimation and quickly increase the system performance. By adopting an optimal learning formulation we are able to balance the tradeoff between an accurate estimation, and an effective resources exploitation in order to maximize the long term quality of the service delivered to the users.
Progressive quantization in distributed average consensus

We consider the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. In particular, we exploit the increasing correlation between the exchanged values throughout the iterations of the consensus algorithm in order to design a novel quantization scheme, particularly efficient at low bit rates. We implement a low complexity, uniform quantizer in each sensor, where refined quantization is achieved by progressively reducing the quantization intervals with the convergence of the consensus algorithm. We propose a recurrence relation for computing the quantization parameters that depend on the network topology and the communication rate. Finally, simulation results demonstrate the effectiveness of the progressive quantization scheme that leads to the consensus solution even at low communication rate.
Low bit-rate compression of omnidirectional images

Omnidirectional images represent a special type of images that are captured by vision sensors with a 360-degree field of view. This work targets the compression of such images by taking into account their particular geometry. We first map omnidirectional images to spherical ones and then perform sparse image decomposition over a dictionary of geometric atoms on the 2D sphere. A coder based on Matching Pursuit and adaptive quantization is finally proposed for efficient compression of omnidirectional images. The experiments demonstrate that the proposed system outperforms JPEG2000 coding of unfolded images. Since most omnidirectional sensors can be parametrized with a spherical camera model, the proposed method is generic with respect to different sensor constructions.
Distributed Streaming via Packet Partitioning

We propose a system for adaptive streaming from multiple servers to a single receiver over separate network paths. Based on incoming packets, the receiver estimates the available bandwidth on every path and returns this information to the servers. An optimization algorithm is designed that enables the servers to independently partition the media packets among them according to the bandwidth information and such that the resulting video quality at the receiver is maximized. To this end, the algorithm takes advantage of a source pruning technique that preprocesses the media stream ahead of time. Simulation results demonstrate that the proposed streaming framework provides superior performance over a conventional transmission scheme that performs proportional packet scheduling based only on the available network bandwidth. Due to its low-complexity aspect, the framework is suitable for practical implementations of adaptive and efficient distributed streaming systems
Key view selection in distributed multiview coding

Multiview image and video systems with large number of views lead to new problems in data representation, transmission and user interaction. In order to reduce the data volumes, most distributed multiview coding schemes exploit the inter-view redundancies at the decoder side, using view synthesis from key views. In the situation where many views are considered, the two following questions become fundamental: i) how many key views have to be chosen for keeping a good reconstruction quality with reasonable coding cost? ii) where to place them optimally in the multiview sequences? We propose in this paper an algorithm for selecting the key views in a distributed multiview coding scheme. Based on a novel metric for the correlation between the views, we formulate an optimization problem for the positioning of the key views such that both the distortion of the reconstruction and the coding rate cost are effectively minimized. We then propose a new optimization strategy based on shortest path algorithm that permits to determine both the optimal number of key views and their positions in the image set. We experimentally validate our solution in a practical distributed multiview coding system and we show that considering the 3D scene geometry in the key view positioning brings significant rate-distortion improvements compared to distance-based key view selection as it is commonly done in the literature.
Color image scalable coding with matching pursuit

The paper presents a new scalable and highly flexible color image coder based on a matching pursuit expansion. The matching pursuit algorithm provides an intrinsically progressive stream and the proposed coder allows us to reconstruct color information from the first bit received. In order to capture edges in natural images efficiently, the dictionary of atoms is built by translation, rotation and anisotropic refinement of a wavelet-like mother function. This dictionary is moreover invariant under shifts and isotropic scaling, thus leading to very simple spatial resizing operations. This flexibility and adaptivity of the MP coder makes it appropriate for asymmetric applications with heterogeneous end user terminals.
Distributed Representation of Geometrically Correlated Images With Compressed Linear Measurements

This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or the camera positioning. It concentrates on the problem where images are encoded with compressed linear measurements. We propose a geometry-based correlation model that describes the common information in pairs of images. We assume that the constitutive components of natural images can be captured by visual features that undergo local transformations (e.g., translation) in different images. We first identify prominent visual features by computing a sparse approximation of a reference image with a dictionary of geometric basis functions. We then pose a regularized optimization problem in order to estimate the corresponding features in correlated images that are given by quantized linear measurements. The correlation model is thus given by the relative geometric transformations between corresponding features. We then propose an efficient joint decoding algorithm that reconstructs the compressed images such that they are consistent with both the quantized measurements and the correlation model. Experimental results show that the proposed algorithm effectively estimates the correlation between images in multiview data sets. In addition, the proposed algorithm provides effective decoding performance that advantageously compares to independent coding solutions and state-of-the-art distributed coding schemes based on disparity learning.
New dictionary and fast atom searching method for matching pursuit representation of displaced frame difference

Matching pursuit decomposes a signal into a linear expansion of functions selected from a redundant dictionary, isolating the signal structures that are coherent with respect to a given dictionary. In this paper we focus on the Matching Pursuit representation of the displaced frame difference (dfd). In particular, we introduce a new dictionary for matching pursuit that efficiently exploits the signal structures of the dfd. We also propose a fast strategy to find the atoms exploiting the maximum of the absolute value of the error in the motion predicted image and the convergence of the MSE with the rotation of the atoms. Results show that the fast strategy is quite robust when compared to exhaustive search techniques and it improves the results of a suboptimal search strategy based on a genetic algorithm.
In-Network View Synthesis for Interactive Multiview Video Systems

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.
Distributed Packet Scheduling of Multiple Video Streams over Shared Communication Resources

We consider the problem of distributed packet selection and scheduling for multiple video streams sharing a communication channel. An optimization framework is proposed to enable the multiple senders to coordinate their packet transmission schedules, such that the overall quality over the video clients is maximized. The framework relies on rate-distortion information that is used to characterize a video packet and that consists of two quantities: the size of the packet in bits, and its importance for the reconstruction quality of the corresponding stream. Using the framework, each of the senders allocates to its own video packets a share of the bandwidth available on the communication channel, that is proportional to the relative importance of these packets. Thereby, a decentralized streaming strategy is provided that allows for trading-off rate and distortion, not only within a single video stream, but also across different streams. Simulation results demonstrate that, for the difficult case of scheduling non-scalably encoded video streams, our framework substantially outperforms a conventional streaming system that does not consider the relative importance of the video packets. The gains in performance reach up to 8 dB in both streaming scenarios under examination, namely adaptation to random packet loss and simultaneous adaptation to packet loss and available bandwidth
Redundancy in non-orthogonal transforms

Compression efficiency is mainly driven by redundancy of the overcomplete set of functions chosen for non-orthogonal signal decompositions. Redundancy is an important criteria in the design of dictionaries, whose size only provides a first indication without however taking into account the distribution of the atoms. This paper provides a new formulation for the structural redundancy of an overcomplete set of functions. The structural redundancy factor directly drives the energy compaction properties of non-orthogonal transforms in frame expansion or matching pursuit.
L1 regularized super-resolution from unregistered omnidirectional images

In this paper, we address the problem of super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We propose an l1 regularized total least squares normminimization method for joint registration and reconstruction with better stabilization and denoising. Experimental results show that regularization offers a quality improvement of up to 1dB. In addition, it reduces the number of low resolution images that are necessary to reconstruct a high resolution image at a target quality.
Multitask Additive Models With Shared Transfer Functions Based on Dictionary Learning

Additive models form a widely popular class of regression models that represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is their interpretability: the transfer functions provide visual means for inspecting the models and identifying domain-specific relations between inputs and outputs. However, in large-scale problems involving the prediction of many related tasks, learning independently additive models results in a loss of model interpretability, and can cause overfitting when training data is scarce. We introduce a novel multitask learning approach that provides a corpus of accurate and interpretable additive models for a large number of related forecasting tasks. Our key idea is to share transfer functions across models in order to reduce the model complexity and ease the exploration of the corpus. We establish a connection with sparse dictionary learning and propose a new efficient fitting algorithm that alternates between sparse coding and transfer function updates. The former step is solved via an extension of orthogonal matching pursuit, whose properties are analyzed using a novel recovery condition that extends existing results in the literature. The latter step is addressed using a traditional dictionary update rule. Experiments on real-world data demonstrate that our approach compares favorably to baseline methods while yielding an interpretable corpus of models, revealing structure among the individual tasks and being more robust when training data is scarce. Our framework therefore extends the well-known benefits of additive models to common regression settings possibly involving thousands of tasks.
Extended Layered Depth Image Representation in Multiview Navigation

Emerging applications in multiview streaming look for providing interactive navigation services to video players. The user can ask for information from any viewpoint with a minimum transmission delay. The purpose is to provide user with as much information as possible with least number of redundancies. The recent concept of navigation segment representation consists of regrouping a given number of viewpoints in one signal and transmitting them to the users according to their navigation path. The question of the best description strategy of these navigation segments is however still open. In this paper, we propose to represent and code navigation segments by a method that extends the recent layered depth image (LDI) format. It consists of describing the scene from a viewpoint with multiple images organized in layers corresponding to the different levels of occluded objects. The notion of extended LDI comes from the fact that the size of this image is adapted to take into account the sides of the scene also, in contrary to classical LDI. The obtained results show a significant rate-distortion gain compared to classical multiview compression approaches in navigation scenario.
Graph-Based Compression of Dynamic 3D Point Cloud Sequences

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.
A performance study of the tangent distance method in transformation-invariant image classification

A common problem in image analysis is the transformation-invariant estimation of the similarity between a query image and a set of reference images representing different classes. This typically requires the comparison of the distance between the query image and the transformation manifolds of the reference images. The tangent distance algorithm is a popular method that estimates the manifold distance by employing a linear approximation of the transformation manifolds. In this paper, we present a performance analysis of the tangent distance method in image classification applications for general transformation models. In particular, we characterize the misclassification error in terms of the geometric properties of the individual manifolds such as their curvature, as well as their relative properties such as the separation between them. We then extend our results to a multi-scale analysis where the images are smoothed with a low-pass filter and study the effect of smoothing on the misclassification error. Our theoretical results are confirmed by experiments and may find use in the selection of algorithm parameters in multiscale transformation-invariant image analysis methods.
Image alignment with rotation manifolds built on sparse geometric expansions

In this paper we discuss the problem of alignment of patterns under arbitrary rotation. When a generic image pattern is geometrically transformed, it typically spans a (possibly nonlinear) manifold in a high dimensional space. When the pattern of interest is given by a sparse approximation over a structured dictionary of geometric atoms, we show that the rotation manifold can be expressed analytically as a function of the transformation parameters. At the same time, its high order derivatives are also given in a closed form when the pattern is represented as a sparse linear combination of a few differentiable basis functions. In this framework, the alignment problem is formulated as the minimization of the distance between the reference pattern and the manifold, which boils down to a nonlinear least squares optimization problem. We propose to solve this problem by a Newton-type method, whose solution is facilitated by the analytical expressions of the manifold derivatives. We further derive a global optimization heuristic algorithm based on Newton, and provide sufficient conditions for computing the global minimizer. Experimental results demonstrate the effectiveness of the proposed methodology for image alignment and rotation invariant pattern recognition.
Multiview image coding using graph-based approach

In this paper, we design a new approach for coding the geometry information in a multiview image scenario. As an alternative to depth-based schemes, we propose a representation that captures the dependencies between pixels in different frames in the form of connections in a graph. In our approach it is possible to directly perform compression by simplifying the graph, which provides a more direct control of the effect of coding on geometry representation. Our method leads to more accurate view synthesis, when compared to conventional lossy coding of depth maps operating at the same bit rate.
Performance bounds for sensor data gathering by coding in finite fields

We address the problem of data gathering in adhoc networks. We propose a novel framework where sensor signals are quantized and mapped to a finite field. The network nodes then combine the data from different sensors to form messages that are transmitted towards a receiver. The receiver gathers different messages and reconstructs the original signal. We study the dependence of the signal reconstruction error on the quantization and network parameters. We further compute a bound on the reconstruction error for sparse sensor signals that depends on the number of messages gathered by the receiver. We validate our results with simulations in line array and tree-based sensor networks and show that our new framework leads to effective signal reconstruction with limited transmission costs.