nils-herrmann/clean_scirepeval huggingface dataset:
Rate-Distortion Optimized Packet Scheduling Over Bottleneck Links

The loss and delay experienced by packets traveling along an Internet network path are mainly governed by the characteristics of a bottleneck link, such as available data rate and queue size. In this work, we propose a framework for rate-distortion optimized packet scheduling with adaptive rate control for media streaming over bandwidth-constrained bottleneck links. The framework computes optimal packet schedules while continuously adapting its instantaneous rate to the following three factors: the available data rate and the current queue size on the bottleneck link, and the congestion that packets transmitted under the schedules will create on the bottleneck link. Experimental results demonstrate that our framework does not lose in rate-distortion performance over rate-distortion optimized packet scheduling without strict rate control, while producing at the same time a much smoother instantaneous rate feeding the bottleneck queue. This in turn contributes to fairness to other flows sharing the bottleneck link and causes less variation in queue size, thereby avoiding queue overflow and unnecessarily long packet delays on the bottleneck link
Multiple description image coding with redundant expansions and optimal quantization

This paper addresses the problem of optimal rate allocation for multiple description coding with redundant signal expansions. In case of redundant descriptions, the quantization of the transform coefficients has clearly to be adapted to the importance of the basis functions, to the redundancy in the representation, and to the expected loss probability on the transmission channel. We derive a rate-distortion optimal solution for the scalar quantization of coefficients in redundant signal representations. The application of the optimal rate allocation to a typical image communication problem demonstrates performance gains with respect to scheme based on uniform quantization with fixed step size, and to solutions based on unequal error protection.
TV-regularized generation of planar images from omnicams

This paper addresses the problem of mapping images between different vision sensors. Such a mapping could be modeled as a sampling problem that has to encompass the change of geometry between the two sensors and the specific discretization of the real scene observed by the two different imaging systems. We formulate the problem in a general framework that can be cast as a minimization regularized problem with a linear operator, that applies to any image geometry. We then focus on the particular problem of the generation of planar images from omnidirectional images, in any viewing direction and for any size and resolution. In this regularized approach, the fidelity term is expressed in the original omnicam geometry and the regularization is based on Total Variation (TV) solved here with proximal methods. Experimental results demonstrate the superiority of this approach with respect to alternative schemes based on linear interpolation or TV in-painting.
Temporal and Inter-View Consistent Error Concealment Technique for Multiview Plus Depth Video

Multiview plus depth (MVD) is an emerging video format with many applications, including 3-D television and free viewpoint television. During the broadcast of a compressed MVD video, transmission errors may cause the loss of whole frames, resulting in significant degradation of video quality. Error concealment techniques have been widely used to deal with transmission errors in video communication. However, the existing solutions do not address the requirement that the reconstructed frames should be consistent with neighboring frames, i.e., corresponding pixels should have consistent color information. We propose a new consistency model for error concealment of MVD video that allows one to maintain a high level of consistency between frames of the same view (temporal consistency) and those of neighboring views (inter-view consistency). We then propose an algorithm that uses our model to implement concealment in a consistent way. Simulations with the reference software for the multiview video coding project of the joint video team of the ISO/IEC MPEG and ITU-T VCEG show that our method outperforms benchmark techniques, including a baseline approach based on the boundary matching algorithm, with respect to both reconstruction quality and view consistency.
Graph-Based Representation for Multiview Image Geometry

In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation.
Graph-based light field super-resolution

Light field cameras can capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post capture refocus, to depth estimation and image-based rendering. However, light field cameras exhibit a very limited spatial resolution, which should therefore be increased by computational methods. Off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they ignore its particular structure. A few super-resolution algorithms explicitly devised for light field data exist, but they exhibit significant limitations, such as the need to carry out an explicit disparity estimation step for one or several light field views. In this work we present a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the information in the different light field views is used to augment the spatial resolution of the whole light field. In particular, we show that coupling the multi-frame paradigma with a graph regularizer that enforces the light field structure permits to avoid the costly and challenging disparity estimation step. Our experiments show that the proposed method compares favorably to the state-of-the-art for light field super-resolution algorithms, both in terms of PSNR and visual quality.
P2P video streaming with inter-session network coding

We present a novel receiver-driven p2p system for delivery of multiple concurrent time constrained data streams in overlay networks. We propose an effective combination of rateless coding with intraand inter-session network coding to efficiently exploit the path diversity in the streaming overlay. Network nodes can decide to forward rateless coded packets or to code them in intra- or inter-session mode before transmission. The transmission strategy is determined based on the availability of data sources and the demands of the children nodes. Each network node solves independently a simple flow maximization problem in order to determine the optimal coding policy. The overall system is evaluated for various networks and the results outline the advantages of the proposed approach over intra-session network coding based schemes in terms of clients' satisfaction, innovative flow rate and decoding delay.
Playback delay and buffering optimization in scalable video broadcasting

The paper addresses the problem of optimizing the playback delay experienced by a population of heterogeneous clients in video streaming applications. We consider a typical broadcast scenario, where clients subscribe to different portions of a scalable video stream, depending on their capabilities. Clients share common network resources, whose limited rate directly drives the playback delays imposed on the different groups of receivers. We derive an optimization problem that targets a fair distribution of the playback delays among heterogeneous clients, as well as minimal buffer usage. A server-based scheduling strategy is then proposed that takes into account the properties of the targeted clients, the channel status, and the structure of the media encoding. A polynomial-time algorithm providing close to optimal results is introduced and it is shown to offer significantly reduced playback delays per client population, as compared to traditional scheduling strategies. At the same time, PSNR performance is not affected, which altogether leads to an overall improvement in the quality of service.
Batched patch caching for streaming media

This letter elaborates on a scheme that combines batch patching at an origin server and prefix/interval caching at an edge server receiving the clients' requests. We derive a cost function that factors in the aggregate backbone rate, the cache occupancy, and the disk bandwidth utilization. We define the optimal batched patch caching strategy as a function of the client request rate. Finally, we show how various strategies including full caching, no caching and pure prefix caching with no patching are optimal derivations of our scheme under different request rates. We demonstrate the benefits of our scheme compared to classical streaming strategies.
Contour approximation & depth image coding for virtual view synthesis

A depth image provides geometric information of a 3D scene, namely the shapes of physical objects captured from a particular viewpoint. This information is important for synthesizing images corresponding to different virtual camera viewpoints via depth-image-based rendering (DIBR). Since it has been shown that blurring of object contours in the depth images leads to bleeding artefacts in virtual images. The most effective way to compress depth images relies on edge-adaptive image codecs that preserve contours, which are losslessly coded as side information (SI). However, lossless coding of the exact object contours can be expensive. In this paper, we argue that the contours themselves can be suitably approximated to save bits, while the depth images piecewise smooth (PWS) characteristic stays preserved. Specifically, we first propose a metric that estimates contour coding rate based on edge statistics. Given an initial rate estimate, we then pro-actively approximate object contours in a way that guarantees rate reduction when coded using arithmetic edge coding (AEC) as SI. Given the sharp but approximated contours, we finally encode the image using an edge-adaptive image codec with graph Fourier transform (GFT) for edge preservation. We show in our experiments that by maintaining sharp but slightly inaccurate object contours, the resulting quality of virtual views synthesized via DIBR exceeds those synthesized using depth images compressed with edge-adaptive codecs that losslessly encode object contours as SI, in particular when the total coding rate budget is low. This confirms that optimized coding of depth images results in an effective tradeoff in the representation of contour and respective depth information.
Distributed Sensing of Noisy Signals by Thresholding of Redundant Expansions

This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximant is sent to a central decoder that tries to recover the input signal by combining the multiple sensor outputs. We propose to use redundant dictionaries, and thresholding in the sensor nodes, in order to form sparse approximants of the noisy observations, with low computational complexity. We first show that the noise can actually be beneficial in the recovery of the correct components of the signal s, since it can advantageously perturb the naive thresholding scheme. Then we illustrate the benefit of multiple observations with uncorrelated noise. By careful reconstruction with a projection onto convex sets (POCS) strategy, each additional measurement actually helps to recover more and more components of the original signal, since it tends to isolate the common part in all observations. Experimental results demonstrate the interesting recovery performance of our distributed sensing system. They show that a few observations, represented by a small number of components, are able to provide a good approximation of the signal, even in very noisy conditions
Conditions for recovery of sparse signals correlated by local transforms

This paper addresses the problem of correct recovery of multiple sparse correlated signals using distributed thresholding. We consider the scenario where multiple sensors capture the same event, but observe different signals that are correlated by local transforms of their sparse components. In this context, the signals do not necessarily have the same sparse support, but instead the support of one signal is built on local transforms of the atoms in the sparse support of another signal. We establish the sufficient condition for the correct recovery of such correlated signals using independent thresholding of the multiple signals. The condition is relevant in scenarios where low complexity processing such as thresholding is needed, for example in sensor networks. The validity of the derived recovery condition is confirmed by experimental results in noiseless and noisy scenarios.
Finite length performance of random MAC strategies

The Internet of Things (IoT) is fueling innovation in nearly every part of our lives. From smart homes, cars, and cities, the Internet of Things is creating a more convenient, secure, intelligent, and personalized experience. While for any final user this IoT vision is a substantial innovation step, for communication providers is a compelling thread with massive number of devices connected to the Internet. Multiple connected devices sharing common wireless resources might create interference if they access the channel simultaneously. Medium access control protocols generally regulate the access of the devices to the shared channel to limit signal interference. In particular, irregular repetition slotted ALOHA (IRSA) techniques can achieve high-throughput performance when interference cancellation methods are adopted to recover from collisions. In this work, we study the finite length performance of IRSA schemes by building on the analogy between successive interference cancellation and iterative belief-propagation on erasure channels. We use a novel combinatorial derivation based on the matrix-occupancy theory to compute the error probability and we validate our method with simulation results.
Deformable block-based motion estimation in omnidirectional image sequences

This paper presents an extension of block-based motion estimation for omnidirectional videos, based on a translational object motion model that accounts for the spherical geometry of the imaging system. We use this model to design a new algorithm to perform block matching in sequences of panoramic frames that are the result of the equirectangular projection. Experimental results demonstrate that significant gains can be achieved with respect to the classical exhaustive block matching algorithm in terms of accuracy of motion prediction. In particular, average quality improvements up to approximately 6 dB in terms of Peak Signal to Noise Ratio (PSNR), 0.043 in terms of Structural SIMilarity index (SSIM), and 2 dB in terms of spherical PSNR, can be achieved on the predicted frames.
Joint source/FEC rate selection for quality-optimal MPEG-2 video delivery

This paper deals with the optimal allocation of MPEG-2 encoding and media-independent forward error correction (FEC) rates under a total given bandwidth. The optimality is defined in terms of minimum perceptual distortion given a set of video and network parameters. We first derive the set of equations leading to the residual loss process parameters. That is, the packet loss ratio (PLR) and the average burst length after FEC decoding. We then show that the perceptual source distortion decreases exponentially with the increasing MPEG-2 source rate. We also demonstrate that the perceptual distortion due to data loss is directly proportional to the number of lost macroblocks, and therefore decreases with the amount of channel protection. Finally, we derive the global set of equations that lead to the optimal dynamic rate allocation. The optimal distribution is shown to outperform classical FEC scheme, thanks to its adaptivity to the scene complexity, the available bandwidth and to the network performance. Furthermore, our approach holds for any standard video compression algorithms (i.e., MPEG-x, H.26x).
Growth Codes: Intermediate Performance Analysis and Application to Video

Growth codes are a subclass of Rateless codes that have found interesting applications in data dissemination problems. Compared to other Rateless and conventional channel codes, Growth codes show improved intermediate performance which is particularly useful in applications where partial data presents some utility. In this paper, we investigate the asymptotic performance of Growth codes using the Wormald method, which was proposed for studying the Peeling Decoder of LDPC and LDGM codes. Compared to previous works, the Wormald differential equations are set on nodes' perspective which enables a numerical solution to the computation of the expected asymptotic decoding performance of Growth codes. Our framework is appropriate for any class of Rateless codes that does not include a precoding step. We further study the performance of Growth codes with moderate and large size codeblocks through simulations and we use the generalized logistic function to model the decoding probability. We then exploit the decoding probability model in an illustrative application of Growth codes to error resilient video transmission. The video transmission problem is cast as a joint source and channel rate allocation problem that is shown to be convex with respect to the channel rate. This illustrative application permits to highlight the main advantage of Growth codes, namely improved performance in the intermediate loss region.
Distributed Coding of Spherical Images with Jointly Refined Decoding

This work addresses the coding of 3-dimensional scenes, as captured by distributed vision sensors with catadioptric cameras. Spherical images allow for avoiding distortion due to the common euclidian assumption in the representation of the plenoptic function. We consider here low complexity encoding of the sensor outputs, in a framework where the cameras could be placed anywhere in the scene, and where the sensors do not communicate to each other. Since multiple spherical images of the same scene most probably provide a redundant representation, we propose to have different compression ratios for different cameras, in order to reduce the overhead of information. The decoder performs a joint decoding of the multiples images, by motion estimation, and joint refinement by consistent inverse quantization. It is finally shown that, even in the absence of any information about the scene or the position of the cameras, the proposed scheme offers improved performance with respect to an independent encoding of the spherical images, especially at low coding rate
Multi-graph learning of spectral graph dictionaries

We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that provide sparse representations for classes of signals, which share common spectral characteristics but reside on the vertices of different graphs. In particular, we concentrate on graph atoms that are constructed on polynomials of the graph Laplacian. Such a design permits to abstract from the precise graph topology and to design dictionaries that can be trained and eventually used on different graphs. We cast the dictionary learning problem as an alternating optimization problem where the dictionary and the sparse representations of training signals are updated iteratively. Experimental results on synthetic graph signals representing common processes on graphs show that our dictionaries are able to capture the important components in graph signals. Further experiments on traffic data confirm the benefits of our dictionaries in the sparse approximation of signals capturing traffic bottlenecks.
Discretization of Parametrizable Signal Manifolds

Transformation-invariant analysis of signals often requires the computation of the distance from a test pattern to a transformation manifold. In particular, the estimation of the distances between a transformed query signal and several transformation manifolds representing different classes provides essential information for the classification of the signal. In many applications, the computation of the exact distance to the manifold is costly, whereas an efficient practical solution is the approximation of the manifold distance with the aid of a manifold grid. In this paper, we consider a setting with transformation manifolds of known parameterization. We first present an algorithm for the selection of samples from a single manifold that permits to minimize the average error in the manifold distance estimation. Then we propose a method for the joint discretization of multiple manifolds that represent different signal classes, where we optimize the transformation-invariant classification accuracy yielded by the discrete manifold representation. Experimental results show that sampling each manifold individually by minimizing the manifold distance estimation error outperforms baseline sampling solutions with respect to registration and classification accuracy. Performing an additional joint optimization on all samples improves the classification performance further. Moreover, given a fixed total number of samples to be selected from all manifolds, an asymmetric distribution of samples to different manifolds depending on their geometric structures may also increase the classification accuracy in comparison with the equal distribution of samples.