nils-herrmann/clean_scirepeval huggingface dataset:
Alignment of uncalibrated images for multi-view classification

Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of images is necessary prior to distance computation. We propose a method for the registration of uncalibrated images that capture the same 3D scene or object. We model the depth map of the scene as an algebraic surface, which yields a warp model in the form of a rational function between image pairs. The warp model is computed by minimizing the registration error, where the registered image is a weighted combination of two images generated with two different warp functions estimated from feature matches and image intensity functions in order to provide robust registration. We demonstrate the flexibility of our alignment method by experimentation on several wide-baseline image pairs with arbitrary scene geometries and texture levels. Moreover, the results on multi-view image classification suggest that the proposed alignment method can be effectively used in graph-based classification algorithms for the computation of pairwise distances where it achieves significant improvements over distance computation without prior alignment.
Correlation-Aware Resource Allocation in Multi-Cell Networks

We propose a cross-layer strategy for resource allocation between spatially correlated sources in the uplink of multi-cell FDMA networks. Our objective is to find the optimum power and channel allocation to the different sources, in order to minimize the maximum distortion achieved in decoding any source data in the network. This problem is NP-hard and finding the optimal solution is not computationally feasible. We propose a three-step algorithm to be performed separately in each cell, which finds cross-layer resource allocation in simple steps. This method separates the problem into inter-cell resource management, grouping of sources for joint decoding, and intra-cell channel assignment. For each of these steps we propose methods that satisfy different design constraints and analyze them by simulations. We show that, while using correlation in compression and joint decoding can achieve 25% distortion reduction over independent decoding, the improvement grows to 37% when correlation is also utilized in resource allocation. This significant distortion reduction motivates further work in correlation-aware resource allocation. Overall, our solution is able to achieve a 60% decrease in 5 percentile distortion compared to independent allocation methods.
An improved foresighted resource reciprocation strategy for multimedia streaming applications

In this paper, we present a solution to efficient multimedia streaming applications over P2P networks based on the foresighted resource reciprocation strategy. We study several priority functions that can explicitly consider the timing constraints and the importance of each data segment in terms of multimedia quality, and successfully incorporate them into the foresighted resource reciprocation strategy. This enables peers to enhance their multimedia streaming capability. The simulation results confirm that the proposed approach outperforms existing algorithms such as tit-for-tat in BitTorrent and BiToS solutions.
Sampling-aware polar descriptors on the sphere

We present a new descriptor and feature matching solution for omnidirectional images. The descriptor builds on the log-polar planar descriptors, but adapts to the specific geometry and non-uniform sampling density of spherical images. We further propose a rotation-invariant matching method for the proposed descriptor that is particularly interesting for mobile devices. It permits to reduce the computational complexity in the detection phase by eliminating the orientation assignment and moving it to the feature matching step. We then use a criteria based on the Kullback-Leibler divergence in order to improve the feature matching performance. Experimental results with spherical images show that the new descriptors offer promising performance and improve on SIFT descriptors computed on the sphere or on tangent planes.
Structured Dimensionality Reduction for Additive Model Regression

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models are however not adapted to problems involving a large number (e.g., hundreds) of input variables, as they are prone to overfitting in addition to losing interpretability. In this paper, we introduce a novel framework for applying additive models to a large number of input variables. The key idea is to reduce the task dimensionality by deriving a small number of new covariates obtained by linear combinations of the inputs, where the linear weights are estimated with regard to the regression problem at hand. The weights are moreover constrained to prevent overfitting and facilitate the interpretation of the derived covariates. We establish identifiability of the proposed model under mild assumptions and present an efficient approximate learning algorithm. Experiments on synthetic and real-world data demonstrate that our approach compares favorably to baseline methods in terms of accuracy, while resulting in models of lower complexity and yielding practical insights into high-dimensional real-world regression tasks. Our framework broadens the applicability of additive models to high-dimensional problems while maintaining their interpretability and potential to provide practical insights.
Profit Optimization for Wireless Video Broadcasting Systems Based on Polymatroidal Analysis

This study addresses the problem of profit maximization between wireless service providers (WSPs) and content providers (CPs) in wireless broadcasting systems , while simultaneously providing high quality of experience for end-users (EUs). We first study the profit model in wireless broadcasting networks with a particular attention to the heterogeneous requirements of EUs, e.g., different display sizes and variable channel conditions. Then, we propose a profit formulation that describes the requirements of wireless service providers and content providers, as well as the satisfaction of EUs that essentially depends on video quality and service charges. We propose a new polymatroidal theoretic framework for maximizing the resulting three-side achievable profit through proper bandwidth allocation. Our framework exploits two particular structures, namely the underlying polymatroidal structure of the profit region and the contra- polymatroidal structure of the rate region. We then propose a profit maximization solution by finding a rate allocation vector on the sum-rate facet that satisfies the maximal achievable profit among the WSP, CPs, and EUs. Experiments on different broadcasting scenarios demonstrate the effectiveness of the proposed method. The WSP is capable of generating more revenues by applying the proposed approach to their marketing strategies while satisfying the demands from CPs and EUs.
Rate distorsion analysis in a disparity compensated scheme

This paper addresses the problem of rate distortion analysis in the context of multi-view image coding, where images are predicted via disparity compensation based on depth map. We first present an analytical model for the variance of the residual error in a predicted frame when the prediction is done with the help of a compressed depth map. This residual variance model presents a convenient expression that separates the different error origins (reference frame quantization, depth map coding, and motion activity). We then validate the novel analytical model by testing separately its different underlying hypotheses. Finally, we illustrate an application of our analytical model in a simple bit allocation problem where the objective is to determine the optimal distribution of a global bit budget among reference frame, depth map and disparity-compensated frame. We observe that the optimal allocation given by the analytical model corresponds in practice to the best rate distribution for high bitrate, which confirms the potential of the proposed model in the design of rate-controlled multi-view coding algorithms.
Price-Based Controller for Quality-Fair HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair manner. However, rate fairness is in general not equivalent to quality fairness as different video sequences might have different characteristics and resource requirements. In this work, we focus on this limitation and propose a novel controller for HAS clients that is able to reach quality fairness while preserving the main characteristics of HAS systems and with a limited support from the network devices. In particular, we adopt a price-based mechanism in order to build a controller that maximizes the aggregate video quality for a set of HAS clients that share a common bottleneck. When network resources are scarce, the clients with simple video sequences reduce the requested bitrate in favor of users that subscribe to more complex video sequences, leading to a more efficient network usage. The proposed controller has been implemented in a network simulator, and the simulation results demonstrate its ability to share the available bandwidth among the HAS users in a quality-fair manner.
Bayesian Early Mode Decision Technique for View Synthesis Prediction-Enhanced Multiview Video Coding

View synthesis prediction (VSP) is a coding mode that predicts video blocks from synthesised frames. It is particularly useful in a multi-camera setup with large inter-camera distances. Adding a VSP-based SKIP mode to a standard Multiview Video Coding (MVC) framework improves the rate-distortion (RD) performance but increases the time complexity of the encoder. This letter proposes an early mode decision technique for VSP SKIP-enhanced MVC. Our method uses the correlation between the RD costs of the VSP SKIP mode in neighbouring views and Bayesian decision theory to reduce the number of candidate coding modes for a given macroblock. Simulation results showed that our technique can save up to 36.20% of the encoding time without any significant loss in RD performance.
A non-stationary Hidden Markov Model of multiview video traffic

Multiview video is increasingly getting attention due to emerging applications such as 3DTV and immersive teleconferencing. In this paper, we present a non-stationary Hidden Markov Model (HMM) for characterizing the data rate of compressed multiview content. The states of the model correspond to different video activity levels and exhibit a Poisson state duration distribution. We derive a stable maximum likelihood algorithm for estimating the parameters of our multiview traffic model. Synthetic data generated by the model exhibits statistics that closely match those of actual multiview data. In addition, we demonstrate the high accuracy of the model in two multiview streaming applications by evaluating the frame loss rate of a constrained network buffer fed by actual and synthetic data.
3D face recognition using sparse spherical representations

This paper addresses the problem of 3D face recognition using spherical sparse representations. We first propose a fully automated registration process that permits to align the 3D face point clouds. These point clouds are then represented as signals on the 2D sphere, in order to take benefit of the geometry information. Simultaneous sparse approximations implement a dimensionality reduction process by subspace projection. Each face is typically represented by a few spherical basis functions that are able to capture the salient facial characteristics. The dimensionality reduction step preserves the discriminant facial information and eventually permits an effective matching in the reduced space, where it can further be combined with LDA for improved recognition performance. We evaluate the 3D face recognition algorithm on the FRGC v.1.0 data set, where it outperforms classical state-of-the-art solutions based on PCA or LDA on depth face images.
Optimized MVC Prediction Structures for Interactive Multiview Video Streaming

The Multiview Video Coding (MVC) standard efficiently compresses multiview video by considering spatial, temporal and interview correlations. This letter studies the impact of the MVC interview prediction structure on both the transmission and the overall coding rates for an interactive multiview video streaming system, considering both unicast and multicast scenarios, with the user interactive behavior represented by some view-popularity model. We propose a method to identify the optimal prediction structure minimizing the visual distortion, given some storage and link capacities constraints. Simulation results confirm that the optimal prediction structure results from a non-trivial tradeoff between the system constraints, the transmission model and the views' popularity.
Delay-based overlay construction in P2P video broadcast

We consider streaming video content over an overlay network of peer nodes. Each of the nodes employs a mesh-pull mechanism to organize the download of data units from its neighbours. We propose a novel algorithm for constructing the distribution overlay, where peers are arranged in neighbourhoods that exhibit similar latency values from the origin media server. Such an organization increases data sharing between neighbours in broadcast applications and reduces the play-out latency at a peer. Each of the nodes in the overlay is further equipped with a packet scheduling procedure that requests data units from neighbours in the order of their importance and their popularity within the neighbourhood. Finally, requesting peers share the upload bandwidth of a sending peer in proportion to their transmission rate to that peer in order to discourage free-riding in the system. Our simulation results show that the proposed mesh construction procedure provides improved performance in terms of frame-freeze and playback latency relative to a conventional approach where peer neighbours are selected at random. Corresponding gains in video quality for the media presentation are also registered due to the improved continuity of the playback experience.
Approximation of pattern transformation manifolds with parametric dictionaries

The construction of low-dimensional models explaining high-dimensional signal observations provides concise and efficient data representations. In this paper, we focus on pattern transformation manifold models generated by in-plane geometric transformations of 2D visual patterns. We propose a method for computing a manifold by building a representative pattern such that its transformation manifold accurately fits a set of given observations. We present a solution for the progressive construction of the representative pattern with the aid of a parametric dictionary, which in turn provides an analytical representation of the data and the manifold. Experimental results show that the patterns learned with the proposed algorithm can efficiently capture the main characteristics of the input data with high approximation accuracy, where the invariance to the geometric transformations of the data is accomplished due to the transformation manifold model.
Learning of structured graph dictionaries

We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that their structure is dependent on the graph information and its spectral representation. We first show how operators can be defined for capturing different spectral components of signals on graphs. We then propose a dictionary learning algorithm built on a sparse approximation step and a dictionary update function, which iteratively leads to adapting the structured dictionary to the class of target signals. Experimental results on synthetic and natural signals on graphs demonstrate the efficiency of the proposed algorithm both in terms of sparse approximation and support recovery performance.
Packet scheduling in multicamera capture systems

In multiview video services, multiple cameras acquire the same scene from different perspectives, which results in correlated video streams. This generates large amounts of highly redundant data, which need to be properly handled during encoding and transmission of the multi-view data. In this work, we study coding and transmission strategies in multicamera sets, where correlated sources need to be sent to a central server through a bottleneck channel, and eventually delivered to interactive clients. We propose a dynamic correlation-aware packet scheduling optimization under delay, bandwidth, and interactivity constraints. A novel trellis-based solution permits to formally decompose the multivariate optimization problem, thereby significantly reducing the computation complexity. Simulation results show the gain of the proposed algorithm compared to baseline scheduling policies.
Structured sparse coding for image denoising or pattern detection

Sparsity has been one of the major drives in signal processing in the last decade. Structured sparsity has also lately emerged as a way to enrich signal priors towards more meaningful and accurate representations. In this paper we propose a new structured sparsity signal model that allows for the decomposition of signals into structured molecules. We define the molecules to be linear combinations of atoms in a dictionary and we create a decomposition scheme that allows for their identification in noisy signals while being robust to small errors in the internal molecule structure. We show the effectiveness of our scheme for recovering and identifying corrupted or occluded signals on both synthetic and real data.
Fast distortion-buffer optimized streaming of multimedia

This paper presents a distortion optimized streaming algorithm for on-demand streaming of multimedia. Given the pre-encoded packets of a multimedia stream, we propose a fast algorithm for selecting an appropriate subset of these packets such that the overall client distortion is minimized. This minimization is performed within the rate constraints imposed by the communication channel. In particular, at each transmission opportunity, the proposed approach uses a linear-time algorithm to select the best packet to transmit through the minimization of the expected client distortion. The time complexity of the algorithm is reduced through a factorization of the streaming policy into simpler terms and performing a greedy optimization to select the packet. Inevitably, this in itself leads to sub-optimal results. To alleviate the adverse impact of the greedy optimization, the cost function is penalized with the expected buffer occupancy at the end of the epoch of the optimization. We pose this problem as a Lagrangian minimization. We demonstrate the efficacy of the proposed approach through empirical evaluation.
QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Caching at mobile edge servers can smooth temporal traffic variability and reduce the service load of base stations in mobile video delivery. However, the assignment of multiple video representations to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. Therefore, it is important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) for users. To address this, we study a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different rate-distortion (R–D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints of the edge servers, but also to the transmission and initial startup delay constraints of the users. We formulate the proposed optimization problem as an integer linear program to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algorithm provides some design guidelines for the edge servers to select the cached representations in practice based on both the video popularity and content information.
Toward One Symbol Network Coding Vectors

In this paper, we propose a novel design for network coding vectors that limits the overhead information. Network coding vectors contain information regarding the operations the packets have undergone in the network nodes. They are used at the decoder side to invert coding operations and recover the data. We propose to reduce the size of this side information with the use of Vandermonde-like generator matrices at the sources. These matrices permit to describe the coding operations performed on packets with only one symbol. We analytically investigate the limitations arising from such design constraints. Interestingly, we find that the feasible generation size is upper bounded by log2 q in Galois field Fq of size q as this is the maximum packet diversity allowed by the employed generator matrices. In addition, we show that network coding nodes should only perform addition operations in order to maintain the properties of the coding vectors. We finally discuss the benefits and limitations of the proposed coding vectors in practical systems.
Forward Error Correction for Multipath Media Streaming

We address the problem of joint optimal rate allocation and scheduling between media source rate and error protection rate in scalable streaming applications over lossy multipath networks. Starting from a distortion representation of the received media information at the client, we propose a novel optimization framework in which we analyze the performance of the most relevant forward error correction and scheduling techniques. We describe both optimal and heuristic algorithms that find solutions to the rate allocation and scheduling problem, and emphasize the main characteristics of the compared techniques. Our results show that efficient unequal error protection schemes improve the quality of the streaming process. At the same time we emphasize the importance of priority scheduling of the information over the best available network paths, which outperforms traditional first-in-first-out models or network flooding mechanisms.