nils-herrmann/clean_scirepeval huggingface dataset:
Cedar: An optimized network-aware solution for P2P video multicast

Video multicast is an important application for P2P networks, which can benefit from the load repartitioning and large scale distribution properties they offer. In this context, the Orchard algorithm was developed in 2007, with an aim to build a P2P system meeting video multicast requirements. However, Orchard suffers from a slow overlay construction and a potentially high end-to-end delay. In this work, we propose a new algorithm that we call Cedar. Based on Orchard, it integrates original functionalities to speed-up the tree construction and builds a topologically-aware overlay network in order to minimize end-to-end delay. Simulation results showed that Cedar fulfilled its requirements, as the tree construction speed was increased by at least 64%, and the end-to-end delays reduced from around 20 seconds to a maximum of 5 seconds. The Network Awareness functionality also reduced end-to-end delays by 33%.
A study of norms in convex optimization super-resolution from compressed sources

Advancements over the last decade in video acquisition and display technologies lead to a continuous increase of video content resolution. These aspects combined with the shift towards cloud multimedia services and the underway adoption of High Efficiency Video Coding standards (HEVC) create a lot of interest for Super-Resolution (SR) and video enhancing techniques. Recent works showed that proximal based convex optimization approaches provide a promising direction in video restoration. An important aspect in the definition of a SR model is the metric used in defining the objective function. Most techniques are based on the classical I2 norm. In this paper we further investigate the use of other norms and their behavior w.r.t. multiple quality evaluation metrics. We show that significant gains of up to 0.5 dB can be obtained when using different norms.
Estimation of quantization noise for adaptive-prediction lifting schemes

The lifting scheme represents an easy way of implementing the wavelet transform and of constructing new contentadapted transforms. However, the adaptive version of lifting schemes can result in strongly non-isometric transforms. This can be a major limitation, since all most successful coding techniques rely on the distortion estimation in the transform domain. In this paper we focus on the problem of evaluating the reconstruction distortion (due to quantization noise) in the wavelet domain when a non-isometric adaptive-prediction lifting scheme is used. The problem arises since these transforms are nonlinear, and so common techniques for distortion evaluation cannot be used in this case. We circumvent the difficulty by computing an equivalent time-varying linear filter, for which it is possible to generalize the distortion computation technique. In addition to the theoretical formulation of the distortion estimation, in this paper we provide experimental results proving the reliability of this estimation, and the consequent improvement of RD performance, thanks to a more effective resource allocation which can be performed in the transform domain.
Depth map coding with elastic contours and 3D surface prediction

Depth maps are typically made of smooth regions separated by sharp edges. Following this rationale, this paper presents a novel coding scheme where depth data is represented by a set of contours defining the various regions together with a compact representation of the values inside each region. The proposed coding scheme is based on elastic curves, which make possible to compactly represent the contours exploiting also the temporal consistency in different frames. A 3D surface prediction algorithm is then used to obtain an accurate estimation of the depth field from the coded contours and a subsampled version of the data. Finally, an ad-hoc coding strategy for the low resolution data and the prediction residuals is presented. Experimental results prove how the proposed approach is able to obtain a very high coding efficiency outperforming the HEVC coder at medium-low bitrates.
Disparity map estimation under convex constraints using proximal algorithms

In this paper, we propose a new approach for estimating depth maps of stereo images which are prone to various types of noise. This method, based on a parallel proximal algorithm, gives a great flexibility in the choice of the constrained criterion to be minimized, thus allowing us to take into account different types of noise distributions. Our main objective is to present an iterative estimation method based on recent convex optimization algorithms and proximal tools. Results for several error measures demonstrate the effectiveness and robustness of the proposed method for disparity map estimation even in the presence of perturbations.
Wyner-ziv coding for depth maps in multiview video-plus-depth

Three dimensional digital video services are gathering a lot of attention in recent years, thanks to the introduction of new and efficient acquisition and rendering devices. In particular, 3D video is often represented by a single view and a so called depth map, which gives information about the distance between the point of view and the objects. This representation can be extended to multiple views, each with its own depth map. Efficient compression of this kind of data is of course a very important topic in sight of a massive deployment of services such as 3D-TV and FTV (free viewpoint TV). In this paper we consider the application of distributed coding techniques to the coding of depth maps, in order to reduce the complexity of single view or multi view encoders and to enhance interactive multiview video streaming. We start from state-of-the-art distributed video coding techniques and we improve them by using high order motion interpolation and by exploiting texture motion information to encode the depth maps. The experiments reported here show that the proposed method achieves a rate reduction up to 11.06% compared to state-of-the-art distributed video coding technique.
ROI-based rate control using tiles for an HEVC encoded video stream over a lossy network

The growth in the use of high definition (HD) and above video resolutions streams has outstripped the rate at which network infrastructure has been deployed. Video streaming applications require appropriate rate control techniques that make use of the specific characteristics of the video content, such as the regions of interest (ROI). With the introduction of high efficiency video coding (HEVC) streams, we consider new coding features to make a novel ROI-based rate control (RC) algorithm. The proposed approach introduces tiling in a ROI-based rate control scheme. It aims at enhancing the quality of important regions (i.e. faces for a videoconferencing system) considering independently coded regions lying within an ROI and helps evaluating the ROI quality under poor channel conditions. Our work consists of two major steps. First, we designed a RC algorithm based on an independent processing of tiles of different regions. Second, we investigate the effect of ROI- and tile-based rate control algorithm on the decoded quality of the stream transmitted over a lossy channel.
Robust Video Coding Based on Multiple Description Scalar Quantization With Side Information

This paper addresses the problem of video compression for robust transmission on lossy Internet networks. The approach developed relies on multiple description coding (MDC) principles. Predictive multiple description video coding has already been considered for robust video transmission over lossy channels. However, MDC, when combined with motion-compensated prediction, is known to suffer from predictive mismatch: in presence of losses, the prediction signal available at the decoder may differ from the one used at the encoder. In this paper, we describe a video compression scheme based on MDC and Wyner-Ziv (WZ) coding. The input sequence is structured into groups of pictures which contain one key frame and one WZ frame. Each frame is first transformed with a wavelet transform and the resulting frequency bands are quantized with a multiple description scalar quantizer. Two balanced descriptions of the video input are thus generated. The quantization indexes of the WZ frames are coded with an low-density parity-check accumulate-based Slepian-Wolf (SW) coder. The lateral receivers first decode the received key frame lateral descriptions, and then construct the side information needed for decoding the corresponding WZ frame descriptions by motion-compensated interpolation. In the case where the two WZ data descriptions are received, the central decoder can perform a separate SW decoding of the two sequences of quantization indexes. A joint iterative decoding approach of the two WZ descriptions is also described which improve the central WZ data decoding performance, however, at the expense of increased decoding complexity. The influence of the proposed iterative decoding technique and of the amount of redundancy on the lateral and central rate-distortion performance of the algorithm is studied.
Higher and lower-order properties of the wavelet decomposition of self-similar processes

Self-similar processes have received increasing attention in the signal processing community, due to their wide applicability in modeling natural phenomena which exhibit "1/f" spectra and/or long-range dependence. On the other hand the wavelet decomposition became a very useful tool in describing nonstationary self-similar processes. In this paper we first investigate the existence and the properties of higher-order statistics of self-similar processes with finite variance. Then, we consider some self-similar processes with infinite variance and study the statistical properties of their wavelet coefficients.
Comparative study of scalar and vector quantization on different phase-shifting digital holographic data representations

For compression of digital holographic data, choosing an appropriate representation of the holographic information is always a main target. The effectiveness of the representation needs to be studied before applying further compression method for reducing storage and transmission burden. In this paper, we investigate the effectiveness of using amplitude-phase information of the complex object wave and the difference information between different phase-shifted holograms by scalar and vector quantization. The experiments show that the amplitude-phase representation is more suited when applying intra compression, whereas holograms are more suited for inter compression.
Improved view synthesis by motion warping and temporal hole filling

View synthesis received increasing attention over the last years, as it offers a wide range of practical applications like Free Viewpoint Television, 3D video, video gaming, etc. The main issues in view synthesis are the filling of disoccluded areas and the warping of real views. In this paper we propose a new hole filling method, it uses temporal correlations in the real views to extract information on disoccluded areas from different time instants in the synthetic view. We also propose a sub-pixel warping technique that takes into account depth and can be used for both the warping of the real view as well as for motion compensation. Our method is proved to bring gains of up to 0.31dB in average over several multiview test sequences.
Initialization, Limitation, and Predictive Coding of the Depth and Texture Quadtree in 3D-HEVC

The 3D video extension of High Efficiency Video Coding (3D-HEVC) exploits texture-depth redundancies in 3D videos using intercomponent coding tools. It also inherits the same quadtree coding structure as HEVC for both components. The current software implementation of 3D-HEVC includes encoder shortcuts that speed up the quadtree construction process, but those are always accompanied by coding losses. Furthermore, since the texture and its associated depth represent the same scene, at the same time instant and view point, their quadtrees are closely linked. In this paper, an intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the quadtrees. If depth is coded before the texture, the texture quadtree is initialized from the coded depth quadtree. Otherwise, the depth quadtree is limited to the coded texture quadtree. A 31% encoder runtime saving, a -0.3% gain for coded and synthesized views and a -1.8% gain for coded views are reported for the second method.
A fully scalable 3D subband video codec

Multimedia transmissions over heterogeneous networks require a high degree of flexibility from video compression systems. They are expected to be fully scalable, that is to say to be able to partly decode a video bitstream and to get a reconstruction quality proportional to the received amount of information. To achieve this functionality, we propose a video codec based on 2D+t subband decomposition. Groups of frames are first temporally filtered using motion compensation and then spatially decomposed with wavelets. The spatiotemporal coefficients are further scanned and compressed using a new SPIHT-like strategy, namely fully scalable zerotree coding, together with arithmetic encoding, which provides a combination of temporal, spatial and SNR scalability. In addition, scalable motion vector coding ensures a fully progressive bitstream.
View synthesis based on temporal prediction via warped motion vector fields

The demand for 3D content has increased over the last years as 3D displays are now widespread. View synthesis methods, such as depth-image-based-rendering, provide an efficient tool in 3D content creation or transmission, and are integrated in coding solutions for multiview video content such as 3D-HEVC. In this paper, we propose a view synthesis method that takes advantage of temporal and inter-view correlations in multiview video sequences. We use warped motion vector fields computed in reference views to obtain temporal predictions of a frame in a synthesized view and blend them with depth-image-based-rendering synthesis. Our method is shown to bring gains of 0.42dB in average when tested on several multiview sequences.
Wavelet based estimators for self-similar /spl alpha/-stable processes

The aim of this work is to propose wavelet based estimators for the self-similarity and stability parameters of stable self-similar processes. These estimators are simple both conceptually and practically, consisting of linear fits performed on the wavelet coefficients of stable self-similar processes. Their statistical performance are studied both theoretically, from the analysis of the statistical properties of the wavelet coefficients, and using numerical simulations.
A railroad detection algorithm for infrastructure surveillance using enduring airborne systems

Infrastructure surveillance is an important requirement for many companies. With the advancement of technology, drones can now provide an efficient tool for such applications. A possible future scenario is the automated surveillance of railroads. Whereas numerous algorithms that provide railroad detection exist, they have mainly focused either on satellite images or for small, low altitude drones which are unsuitable for our particular scenario. In this paper we propose a railroad detection algorithm tailored for large, high altitude enduring drones. More specifically, we use Hough Transform to detect lines and perform a line clustering in the Rho and Theta space. A score model is also proposed in order to identify the railroad. We test our method on several sequences supplied by Airbus Defense & Space and show our algorithm to provide a detection rate of 93.23% in average.
Using distributed source coding and depth image based rendering to improve interactive multiview video access

Multiple-views video is commonly believed to be the next significant achievement in video communications, since it enables new exciting interactive services such as free viewpoint television and immersive teleconferencing. However the interactivity requirement (i.e. allowing the user to change the viewpoint during video streaming) involves a trade-off between storage and bandwidth costs. Several solutions have been proposed in the literature, using redundant predictive frames, Wyner-Ziv frames, or a combination of them. In this paper, we adopt distributed video coding for interactive multiview video plus depth (MVD), taking advantage of depth image based rendering (DIBR) and depth-aided inpainting to fill the occlusion areas. To the authors' best knowledge, very few works in interactive MVD consider the problem of continuity of the playback during the switching among streams. Therefore we survey the existing solutions, we propose a set of techniques for MVD coding and we compare them. As main results, we observe that DIBR can help in rate reduction (up to 13.36% for the texture video and up to 8.67% for the depth map, wrt the case where DIBR is not used), and we also note that the optimal strategy to combine DIBR and distributed video coding depends on the position of the switching time into the group of pictures. Choosing the best technique on a frame-to-frame basis can further reduce the rate from 1% to 6%.
A Nonlocal Structure Tensor-Based Approach for Multicomponent Image Recovery Problems

Nonlocal total variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the structure tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the nonlocal variations, jointly for the different components, through various ℓ1,p-matrix-norms with p ≥ 1. To facilitate the choice of the hyperparameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented because of the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for color, multispectral, and hyperspectral images. The results demonstrate the interest of introducing a nonlocal ST regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods, such as the alternating direction method of multipliers.
A convex optimization approach for image resolution enhancement from compressed representations

Quality of experience in future home devices is foreseen to drastically increase, with the increase in image resolution. Displays with a horizontal resolution of 4K pixels are already appearing, and 8K Super-HiVision has already been demonstrated. Currently, only spatial upsampling of conventional HD format is performed to match the resolution of such displays. In this paper, we propose a novel method for high-quality up-conversion of legacy visual content in order to fit the screen resolution. More precisely, by assuming that we have various versions of the same image at standard resolution, encoded with different parameters, we try to reconstruct the high resolution image with higher quality than a simple interpolation. To this end, we adopt a variational formulation of the problem and construct a convex constrained criterion that incorporates both a fidelity term (linked to the acquisition process) and some a priori information. A recent primal-dual proximal algorithm is used to solve the associated minimization problem and simulation results show the good performance and behavior of the proposed approach.
TCP and Network Coding: Equilibrium and Dynamic Properties

This paper analyzes the impact on the stability of the TCP-Reno congestion control mechanism when a network coding (NC) layer is inserted in the TCP/IP stack. A model of the dynamics of the TCP-NC protocol combined with random early detection (RED) as active queue management mechanism is considered to study the network equilibrium and stability properties. The existence and uniqueness of an equilibrium point is demonstrated and characterized in terms of average throughput, loss rate, and queue length. Global stability is proved in absence of forward delay, and the effects of the NC redundancy factor and of the delay on the local stability of TCP-NC-RED are studied around the equilibrium. The fairness of TCP-NC with respect to TCP-Reno-like protocols is also studied. A version of TCP-NC with adaptive redundancy factor (TCP-NCAR) is also introduced. Results provided by the proposed model are compared to those obtained by simulation for N sources sharing a single link. TCP-NC-RED becomes unstable when delay or capacity increases, as TCP-Reno does, but also when the redundancy factor increases. Its stability region is characterized as a function of the redundancy factor. If TCP-NC and TCP-Reno share the same links, TCP-NC is fair with TCP-Reno-like protocols when no redundancy is added. Simulations show that TCP-NCAR is able to compensate losses on the wireless parts of the network.
Joint source-channel coding of scalable video on a Rayleigh fading channel

We present a joint source-channel coding scheme developed for transmitting scalable video on a Rayleigh fading channel. Our scheme consists of a motion compensated temporal subband decomposition applied on groups of frames, followed by a spatial wavelet transform of the temporal subbands and a vector quantization. The vector quantization method is based on a linear labelling by "maximum component diversity" lattice constellations which minimizes not only the channel distortion but at the same time the distortion of our video source. These lattice constellations, which are multidimensional rotations generated from totally real cyclotomic fields, increase the diversity order on a Rayleigh fading channel. In addition, a rate-compatible punctured convolutional code in combination with a perfect interleaving is applied, providing even higher diversity.
A 5-band Temporal Lifting Scheme for Video Surveillance

Three-dimensional (3D) wavelet coding schemes compression applications. The scalability property of such schemes is one of the most important issues for video surveillance systems. In this paper we introduce a new lifting-based method of temporal decomposition which provides a scalability factor of 5 in a motion-compensated subband video coding scheme. Depending on the sequence characteristics, motion model etc., this structure can provide high coding performance. Also, it gives a better leading to an improved temporal scalability. It addresses video surveillance applications, where the motion is very low in most cases
Convex optimization for frame-level rate allocation in MV-HEVC

Optimal rate allocation is among the most challenging tasks to perform in the context of multi-view video coding, because of the dependency between frames induced by motion compensation and depth image-based rendering. In this paper, using a recursive rate-distortion model that explicitly takes into account these dependencies, we approach the frame-level rate allocation as a convex optimization problem. Within this framework, we provide an efficient algorithm for exactly solving the above problem with recent convex optimization tools. Experiments on standard sequences demonstrate the interest of considering the proposed rate allocation method and confirm that our approach ensures a better performance (in ratedistortion sense) than the standard MV-HEVC rate control.