nils-herrmann/clean_scirepeval huggingface dataset:
Geometry-Based Distributed Scene Representation With Omnidirectional Vision Sensors

This paper addresses the problem of efficient representation of scenes captured by distributed omnidirectional vision sensors. We propose a novel geometric model to describe the correlation between different views of a 3-D scene. We first approximate the camera images by sparse expansions over a dictionary of geometric atoms. Since the most important visual features are likely to be equivalently dominant in images from multiple cameras, we model the correlation between corresponding features in different views by local geometric transforms. For the particular case of omnidirectional images, we define the multiview transforms between corresponding features based on shape and epipolar geometry constraints. We apply this geometric framework in the design of a distributed coding scheme with side information, which builds an efficient representation of the scene without communication between cameras. The Wyner-Ziv encoder partitions the dictionary into cosets of dissimilar atoms with respect to shape and position in the image. The joint decoder then determines pairwise correspondences between atoms in the reference image and atoms in the cosets of the Wyner-Ziv image in order to identify the most likely atoms to decode under epipolar geometry constraints. Experiments demonstrate that the proposed method leads to reliable estimation of the geometric transforms between views. In particular, the distributed coding scheme offers similar rate-distortion performance as joint encoding at low bit rate and outperforms methods based on independent decoding of the different images.
Graph-based regularization for spherical signal interpolation

This paper addresses the problem of the interpolation of 2-d spherical signals from non-uniformly sampled and noisy data. We propose a graph-based regularization algorithm to improve the signal reconstructed by local interpolation methods such as nearest neighbour or kernel-based interpolation algorithms. We represent the signal as a function on a graph where weights are adapted to the particular geometry of the sphere. We then solve a total variation (TV) minimization problem with a modified version of Chambolle's algorithm. Experimental results with noisy and uncomplete datasets show that the regularization algorithm is able to improve the result of local interpolation schemes in terms of reconstruction quality.
Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal components. The MTP algorithm provides an adaptive representation for signals in any complete dictionary. The basic idea behind the MTP is to partition the dictionary into L quasi-disjoint subdictionaries. A k-term signal approximation is then iteratively computed, where each iteration leads to the selection of M ≤ L atoms based on thresholding. The MTP algorithm is shown to achieve competitive performance with the matching pursuit (MP) algorithm that greedily selects atoms one by one. This is due to efficient partitioning of the dictionary. At the same time, the computational complexity is dramatically reduced compared to MP due to the batch selection of atoms. We finally illustrate the performance of MTP in image and video compression applications, where we show that the suboptimal atom selection of MTP is largely compensated by the reduction in complexity compared with MP.
Quality-of-Experience-Aware Design in Next-Generation Wireless Networks [Guest Editorial]

With the exponential growth of video-based services, the development of video transmission systems, and the advancement of consumer video technologies, it is becoming increasingly important to improve visual applications in order to meet the quality expectations of end users. While monitoring and controlling QoS parameters of the video transmission system is important for achieving high video quality, it is more crucial to evaluate video quality directly from the users’ perspective, which is known as quality of experience (QoE), or user-level quality of service (QoS). QoE-based video quality assessment is difficult because user experience is subjective, and hence hard to quantify and measure.
Distance-based discretization of parametric signal manifolds

The characterization of signals and images in manifolds often lead to efficient dimensionality reduction algorithms based on manifold distance computation for analysis or classification tasks. We propose in this paper a method for the discretization of signal manifolds given in a parametric form. We present an iterative algorithm for the selection of samples on the manifold that permits to minimize the average error in the manifold distance computation. Experimental results with image appearance manifolds demonstrate that the proposed discretization algorithm outperforms baseline solutions based on random or regular sampling, both in terms of projection accuracy and image registration.
Anchor View Allocation for Collaborative Free Viewpoint Video Streaming

In free viewpoint video, a viewer can choose at will any camera angle or the so-called “virtual view” to observe a dynamic 3-D scene, enhancing his/her depth perception. The virtual view is synthesized using texture and depth videos of two anchor camera views via depth-image-based rendering (DIBR). We consider, for the first time, collaborative live streaming of a free viewpoint video, where a group of users may interactively pull and cooperatively share streams of different anchor views. There is a cost to access the anchor views from the live source, a cost to “reconfigure” the peer network due to a change in selected anchors during view switching, and a distortion cost due to the distance of the virtual views to the received anchor views at users. We optimize the anchor views allocated to users so as to minimize the overall streaming cost given by the access cost, reconfiguration cost, and view distortion cost. We first show that, if the reconfiguration cost due to view switching is negligible, the view allocation problem can be optimally and efficiently solved in polynomial time using dynamic programming. For the case of non-negligible reconfiguration cost, the problem becomes NP-hard. We thus present a locally optimal and centralized algorithm inspired by Lloyd's algorithm used in non-uniform scalar quantization. We further propose a distributed algorithm with convergence guarantee, where each peer group independently makes merge-and-split decisions with a well-defined fairness criteria. Simulation results show that our algorithms achieve low streaming cost due to its excellent anchor view allocation.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.
Network Coding of Rateless Video in Streaming Overlays

We present a system for collaborative video streaming in wired overlay networks. We propose a scheme that builds on both rateless codes and network coding in order to improve the system throughput and the video quality at clients. Our hybrid coding algorithm permits to efficiently exploit the available source and path diversity without the need for expensive routing nor scheduling algorithms. We consider specifically an architecture where multiple streaming servers simultaneously deliver video information to a set of clients. The servers apply Raptor coding on the video packets for error resiliency, and the overlay nodes selectively combine the Raptor coded video packets in order to increase the packet diversity in the system. We analyze the performance of selective network coding and describe its application to practical video streaming systems. We further compute an effective source and channel rate allocation in our collaborative streaming system. We estimate the expected symbol diversity at clients with respect to the coding choices. Then we cast a minmax quality optimization problem that is solved by a low-cost bisection based method. The experimental evaluation demonstrates that our system typically outperforms Raptor video streaming systems that do not use network coding as well as systems that perform decoding and encoding in the network nodes. Finally, our solution has a low complexity and only requires small buffers in the network coding nodes, which are certainly two important advantages toward deployment in practical streaming systems.
Joint Network and Rate Allocation for Simultaneous Wireless Applications

We address the problem of rate allocation and network/path selection for multiple users, running simultaneous applications over multiple parallel access networks. Our joint optimization problem consists of finding the appropriate application rate allocation and network parameters for each individual user, such that an overall quality metric is maximized. We compare our solution to other solutions based on throughput optimization strategies through extensive simulations, and we show the superiority of our approach. Furthermore, our solution proves to be more robust in dynamic systems, when clients can join/leave the access networks.
Analysis of Airborne LiDAR Point Clouds With Spectral Graph Filtering

Separation of ground and nonground measurements is an essential task in the analysis of light detection and ranging (LiDAR) point clouds; however, it is challenge to implement a LiDAR filtering algorithm that integrates the mathematical definition of various landforms. In this letter, we propose a novel LiDAR filtering algorithm that adapts to the irregular structure and 3-D geometry of LiDAR point clouds. We exploit weighted graph representations to analyze the 3-D point cloud on its original domain. Then, we consider airborne LiDAR data as an irregular elevation signal residing on graph vertices. Based on a spectral graph approach, we introduce a new filtering algorithm that distinguishes ground and nonground points in terms of their spectral characteristics. Our complete filtering framework consists of outlier removal, iterative graph signal filtering, and erosion steps. Experimental results indicate that the proposed framework achieves a good accuracy on the scenes with data gaps and classifies the nonground points on bridges and complex shapes satisfactorily, while those are usually not handled well by the state-of-the-art filtering methods.
Correlation-aware reconstruction of network coded sources

In this paper, we consider the problem of decoding network coded correlated data when the decoder does not receive sufficient information for exact decoding. We propose an iterative decoding algorithm based on belief propagation that efficiently exploits the data correlation and provides approximate reconstruction of the sources when conventional decoding methods fail. The dependencies among the sources are captured by means of a factor graph. A simple noise model is used in order to describe the pairwise source relationships. The decoding decision is based on MAP estimates that are inferred by message passing over the underlying factor graph. Performance evaluation of the proposed decoding algorithm on correlated data sets consisting of video sequences confirms the efficiency of the proposed algorithm. Simulation results show that high quality reconstruction can be achieved even if significant amount of network coded information is missing at the decoder.
Low-Complexity Adaptive Streaming via Optimized A Priori Media Pruning

Source pruning is performed whenever the data rate of the compressed source exceeds the available communication or storage resources. In this paper, we propose a framework for rate-distortion optimized pruning of a video source. The framework selects which packets, if any, from the compressed representation of the source should be discarded so that the data rate of the pruned source is adjusted accordingly, while the resulting reconstruction distortion is minimized. The framework relies on a rate-distortion preamble that is created at compression time for the video source and that comprises the video packets' sizes, interdependencies and distortion importance. As one application of the pruning framework, we design a low-complexity rate-distortion optimized ARQ scheme for video streaming. In the experiments, we examine the performance of the pruning framework depending on the employed distortion model that describes the effect of packet interdependencies on the reconstruction quality. In addition, our experimental results show that the enhanced ARQ technique provides a significant performance gain over a conventional system for video streaming that does not take into account the different importance of the individual video packets. These gains are achieved without an increase in packet scheduling complexity, which makes the proposed technique suitable for online R-D optimized streaming
Minimum Distance between Pattern Transformation Manifolds: Algorithm and Applications

Transformation invariance is an important property in pattern recognition, where different observations of the same object typically receive the same label. This paper focuses on a transformation-invariant distance measure that represents the minimum distance between the transformation manifolds spanned by patterns of interest. Since these manifolds are typically nonlinear, the computation of the manifold distance (MD) becomes a nonconvex optimization problem. We propose representing a pattern of interest as a linear combination of a few geometric functions extracted from a structured and redundant basis. Transforming the pattern results in the transformation of its constituent parts. We show that, when the transformation is restricted to a synthesis of translations, rotations, and isotropic scalings, such a pattern representation results in a closed-form expression of the manifold equation with respect to the transformation parameters. The MD computation can then be formulated as a minimization problem whose objective function is expressed as the difference of convex functions (DC). This interesting property permits optimally solving the optimization problem with DC programming solvers that are globally convergent. We present experimental evidence which shows that our method is able to find the globally optimal solution, outperforming existing methods that yield suboptimal solutions.
Pattern Detection by Distributed Feature Extraction

This paper presents a distributed algorithm for the detection of patterns or their transformed versions, in noisy images. The proposed method projects the observed signal onto a redundant and structured dictionary of functions, which are distributed among general purpose vision sensors. Each of the sensors then approximates the projections on its own part of the dictionary, and transmits that short information to a central fusion center. The pattern detection problem is then cast to a parameter estimation problem, where the parameters of the geometric transformation of the pattern of interest are sought, instead of the pattern itself. The parameters of the transformation are estimated by introducing a score function over the parameter space. Such an approach allows the fusion center to directly work in the space of features computed by the sensors, without need for signal reconstruction. It advantageously provides a generic approach, where the processing of the image is directly driven by the detection task. Experimental results indicate the effectiveness of the proposed method and its resiliency to noise in the observation.
Curvature analysis of pattern transformation manifolds

Transformation manifolds are quite attractive for image analysis applications that require transformation invariance properties. The geometric structure of a transformation manifold has a profound influence on the design of processing algorithm, and the curvature is a major parameter in the characterization of the manifold geometry. We propose here a procedure for the computation of an upper bound for the maximum principal curvature of a pattern transformation manifold. We provide an analytical formulation of the curvature bound and show that the numerical computation of this bound is mostly dependent on the rotation parameters. Experimental results indicate that the curvature bound of the manifold has considerable dependence on the spatial complexity and smoothness of the generating pattern. Moreover, experiments with discretization of manifolds suggest that the curvature of the manifold is likely to affect the accuracy of compact representation and sampling algorithms.
EXIT-Based Side Information Refinement in Wyner–Ziv Video Coding

The accuracy of the side information (SI) is critical in the performance of distributed video coding algorithms. The SI is typically built at a decoder based on the reconstructed data and on channel coding parity bits transmitted by the encoder. The optimal encoding rate is generally difficult to compute precisely due to the dynamics of video content with varying correlation. Effective methods for the refinement of imprecise SI are therefore important for improved decoding quality. In this paper, we propose to exploit the intrinsic property of channel coding algorithms in Wyner-Ziv video coding. The SI is refined via both the information-plane and the parity-plane bits, which rapidly increases the accuracy of refined SI. We use extrinsic information transfer chart analysis in order to estimate the variations of the mutual information in the iterative decoding. In particular, we characterize mutual information variations for punctured regular and irregular rate-compatible low-density parity-check codes. Tracking the mutual information changes permits to decrease the coding rate of the information and parity bitstreams, while preserving the decoding quality. Simulation results confirm that our method improves on the decoding quality of recent distributed video coding algorithms, especially for high-motion sequences or at high-coding rate regimes.
Polynomial Filtering for Fast Convergence in Distributed Consensus

In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. The convergence rate of such distributed algorithms typically depends on the network topology and the weights given to the edges between neighboring sensors, as described by the network matrix. In this paper, we propose to accelerate the convergence rate for given network matrices by the use of polynomial filtering algorithms. The main idea of the proposed methodology is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate. Such an algorithm is equivalent to periodic updates in each of the sensors by aggregating a few of its previous estimates. We formulate the computation of the coefficients of the optimal polynomial as a semidefinite program that can be efficiently and globally solved for both static and dynamic network topologies. We finally provide simulation results that demonstrate the effectiveness of the proposed solutions in accelerating the convergence of distributed consensus averaging problems.
Graph-based representation and coding of multiview geometry

We propose a new approach for describing the geometry information of multiview image representations. Rather than transmitting the raw geometry of the scene, under the form of depth information, we build a graph that represents the connections between corresponding pixels in different views in a multiview image set. The graph starts with the reference image and recursively represents the next levels (i.e., images) by storing the new pixels (those that cannot be derived from the previous image) and their connections to the lower level. The decoder uses these connections to recover the multiple images. In addition to being natural and more easily controlled, the proposed graph-based representation can be compressed more efficiently than depth images. This new representation offers promising perspectives for effective and flexible coding in multiview imaging.
Accelerating Distributed Consensus Using Extrapolation

In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature attack this problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. In this letter, we propose the use of extrapolation methods in order to accelerate distributed linear iterations. The extrapolation methods are guaranteed to converge in a finite number of steps, upper bounded by the number of sensors. In particular, we show that the Scalar Epsilon Algorithm (SEA) can accelerate vector sequences produced by distributed linear iterations, with no communication overhead and without knowledge of the full network topology. We provide simulation results that demonstrate the validity and effectiveness of the proposed scheme.
AMISP: a complete content-based MPEG-2 error-resilient scheme

We address a new error-resilient scheme for broadcast quality MPEG-2 video streams to be transmitted over lossy packet networks. A new scene-complexity adaptive mechanism, namely Adaptive MPEG-2 Information Structuring (AMIS) is introduced. AMIS modulates the number of resynchronization points (i.e., slice headers and intra-coded macroblocks) in order to maximize the perceived video quality, assuming that the encoder is aware of the underlying packetization scheme, the packet loss probability (PLR), and the error-concealment technique implemented at the decoding side. The end-to-end video quality depends both on the encoding quality and the degradation due to data loss. Therefore, AMIS constantly determines the best compromise between the rate allocated to encode pure video information and the rate aiming at reducing the sensitivity to packet loss. Experimental results show that AMIS dramatically outperforms existing structuring techniques, thanks to its efficient adaptivity. We then extend AMIS with a forward-error-correction (FEC)-based protection algorithm to become AMISP. AMISP triggers the insertion of FEC packets in the MPEG-2 video packet stream. Finally, the performances of the AMISP scheme in an MPEG-2 over RTP/UDP/IP scenario are evaluated.