nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs

Today's work in the sensor networks community focuses on collecting and processing data from specific networks with associated base stations. One of the most important requirements in these networks is minimizing resource usage such as processing power and storage size on sensor nodes. Resource constraints in the sensor nodes can be divided into four categories: energy, communication, storage and computational power. In this paper, we present an efficient deployment of sensors with possibility of accessing most recent data through information obtained from ERP (enterprise resource planning) systems' re-configuration models. In this scheme the probability of losing any precious data or events would be minimized. In other words, our main focus in this paper is using ERP or high level distributed decision making systems' processed data or prediction models to reduce resource usage needed on sensor nodes. In the area of integration of sensors and ERPs or distributed decision making systems, very little work has been reported. However, those reported in the literature, mostly address relaying data from sensors to ERPs and tend to largely ignore issues that come with sensor resource constraints. Also they don't make use of the information processed and generated by ERPs in the cloud environment to optimize power consumption and transmission frequency of sensors, which is our aim.
Panda: A protocol-assisted network decoding algorithm

With random linear network coding, mixed packets contain in their headers information about the coding operations performed on the original packets to allow their recovery by the receiver. This introduces an overhead that could be significant if the packet size is relatively small w.r.t. the size of the generation. In this paper, we propose to remove the part of the added header related to the network coding coefficients and to consider network decoding as a source separation problem. This problem is addressed using a maximum a posteriori estimation technique. It exploits some a priori information related to the content of the headers added to the original packets by the upper layers of the protocol stack, before network coding. Experiments show that, despite the fact that traditional source separation techniques are completely inadequate to handle this scenario, the proposed approach is able to recover all packets within streams of thousands of generations without a single decoding error.
Content adaptive gop size control with feedback channel suppression in distributed video coding

This paper presents a novel algorithm for content adaptive GOP size control in distributed video coding. The GOP size is dynamically varied along the sequence, depending on motion activity. Automatic mode selection allows the system to switch between H.264 intra-coding and Wyner-Ziv coding modes to optimize the overall performance. Furthermore, the encoder determines a suitable compression ratio for the Wyner-Ziv frames without the need for a feedback channel. Simulation results show significant improvement in the average system performance, compared to fixed GOP Wyner-Ziv and H.264 intra-coding.
Background simplification for ROI-oriented low bitrate video coding

Low-bitrate video compression is a challenging task, particularly with the increasing complexity of video sequences. Re-shaping video data before its compression with modern hybrid encoders has provided interesting results in the low and ultra-low bit rate domains. In this work, we propose a novel saliency guided preprocessing approach, which combines adaptive re-sampling and background texture removal, to achieve efficient ROI-oriented compression. Evaluated with HEVC, we show that our solution improves the ROI encoding over a wide range of resolutions and bit rates whilst maintaining a high background intelligibility level.
Depth coding and perceived quality for 3D video

Multi-view video plus depth is emerging as the most flexible format for 3D video representation, as witnessed by the current standardization efforts by ISO and ITU. The depth information allows synthesizing virtual view points, and for its compression various techniques have been proposed. We make a preliminary investigation of the effects on the synthesized views of two different approaches: object-based and block-based, from a perceptual point of view.
Depth-Based Multiview Distributed Video Coding

Multiview distributed video coding (DVC) has gained much attention in the last few years because of its potential in avoiding communication between cameras without decreasing the coding performance. However, the current results are not matching the expectations mainly due to the fact that some theoretical assumptions are not satisfied in the current implementations. For example, in distributed source coding the encoder must know the correlation between the sources, which cannot be achieved in the traditional DVC systems without having a communication between the cameras. In this work, we propose a novel multiview distributed video coding scheme in which the depth maps are used to estimate the way two views are correlated with no exchanges between the cameras. Only their relative positions are known. We design the complete scheme and further propose a rate allocation algorithm to efficiently share the bit budget between the different components of our scheme. Then, a rate allocation algorithm for depth maps is proposed in order to maximize the quality of synthesized virtual views. We show, through detailed experiments, that our scheme significantly outperforms the state-of-the-art DVC system.
Introduction to the Issue on Visual Signal Processing for Wireless Networks

R ECENT development of mobile computing technology and wireless communications brought into focus a new paradigm of visual signal transmission via wireless channels and networks. With the emergence of smartphones and continued growth of laptops, netbooks, and tablets, there is a huge increase in a number of mobile devices capable of supporting new image and video applications. According to recent estimates, more than two thirds of all communication network traffic is currently consumed by visual signals and this share is still persistently growing with the development of multi-view and 3D video technology. In addition, interactivity of visual content transmission has become an essential ingredient of modern multimedia communication systems, where uplink channel requirements are also augmented to enable real-time connection between users and video encoders. However, conveying visual information to mobile devices over cellular or mobile broadband networks encounters many challenges, such as limited channel bandwidth, constrained computational and energy resources, high required quality and reliability, tight latency requirements, etc. These problems lie at the heart of the ongoing research in wireless video. In this special issue, most manuscripts tackle a range of networking aspects of wireless video transmission, such as streaming, resource and power allocation and adaptive bit rate control. For that reason, Pudlewski et al. analyze state-of-the-art of wireless video communication in their tutorial paper with insight into the multi-layer network architecture. They also look into a cross-layer design perspective and its potential to enhance the video experience perceived by the end user in future wireless video applications and associated devices. Among the other papers, the first group shifts the focus toward considering delivered visual quality to the end user when allocating the wireless network resources across multi-user population. The second group of manuscripts analyzes the resource and power allocation optimization problem while addressing other criteria, such as transmission delay or network utility. In the third group of publications, coding schemes are analyzed and modified to provide multiple description and scalable coding for wireless video transmission to unmanned aerial vehicles or for free viewpoint video applications. Finally, in the fourth group, video processing applications, such as face recognition or video summarization, are developed for wireless sensor network platforms. Quality of service (QoS) and quality of experience (QoE) are often used as primary metrics for measuring the efficiency of transmission performance in multimedia network delivery. In particular, in wireless video networks, QoE can be expressed as objective or subjective metric reflecting the perceived video content either based on mathematical equations (e.g., PSNR or SSIM) or using more sophisticated tools and procedures to quantitatively express the end user’s level of experience. These metrics are then used for optimizing the transmission perfor-
Stochastic fractal models for image processing

Our study of fractal landscapes departs from the simplest but yet effective model of fractional Brownian motion and explores its two-dimensional (2-D) extensions. We focus on the ability to introduce anisotropy in this model, and we are also interested in considering its discrete-space counterparts. We then move towards other multifractional and multifractal models providing more degrees of freedom for fitting complex 2-D fields. We note that many of the models and processing are implemented in FracLab, a software MATLAB/Scilab toolbox for fractal processing of signals and images.
Distortion evaluation in transform domain for adaptive lifting schemes

In this paper we study the problem of evaluating the reconstruction distortion in the wavelet domain when adaptive lifting schemes (ALS) are used for the direct and inverse transform. The distortion evaluation is necessary in order to perform efficient resource allocation over the transform coefficients. ALS is a non-linear transformation, which prevents using common techniques for distortion evaluation. However we show the equivalence of this non-linear scheme with a time-varying linear filter, and we generalize the distortion computation technique to it. Experiments show that the proposed method allows a reliable estimation of the distortion in the transform domain. This results in improved coding performance.
Multi-view video streaming over wireless networks with RD-optimized scheduling of network coded packets

Multi-view video streaming is an emerging video paradigm that enables new interactive services, such as free viewpoint television and immersive teleconferencing. However, it comes with a high bandwidth cost, as the equivalent of many single-view streams has to be transmitted. Network coding (NC) can improve the performance of the network by allowing nodes to combine received packets before retransmission. Several works have shown NC to be beneficiai in wireless networks, but the delay introduced by buffering before decoding raises a problem in real-time streaming applications. Here, we propose to use Expanding Window NC (EWNC) for multi-view streaming to allow immediate decoding of the received packets. The order in which the packets are included in the coding window is chosen via RD-optimization for the current sending opportunity. Results show that our approach consistently outperforms both classical NC applied on each view independently and transmission without NC.
Four-band linear-phase orthogonal spatial filter bank for subband video coding

In wavelet-based scalable video coding schemes, temporal interframe redundancy is exploited by applying a temporal wavelet transform along the motion trajectories. A spatial decomposition of the temporal subbands is further performed to take advantage of the spatial redundancy of the filtered frames. However, most of the t+2D video codecs do not take into account the very different spatial characteristics of the temporal subband frames and use indifferently the same spatial 9/7 biorthogonal transform to decompose them. In this paper, we present a spatial transform based on a four-band filter bank, whose frequency selectivity properties are shown to be more suited to represent detail frames. We give the analytical form of a linear-phase, orthogonal and regular four-band filter bank and we show by experimental results conducted on video sequences that significant improvements in terms of PSNR can be obtained using the proposed filter bank to decompose the detail frames.
Improving Intra mode coding in H.264/AVC through block oriented transforms

In an H.264 video encoder, the important task of intra coding is performed in the residual domain after a spatial transform has been applied to the block data. Although the intra prediction is very efficient and shows the ability to capture some orientations of the image, after this spatial prediction the intra residual may still show regular structures. To take this possible regularity into account, especially orientation, we propose a preprocessing stage that selects the best rotation to be applied to the block among a set of predefined rotations. To this end, we utilize a rate-distortion algorithm. We further propose to calculate the real orientation of the block to decrease the amount of computation. Experimental results show that compared to the standard intra-prediction mode of H.264, the proposed method can gain up to 1 dBs
Methods and Tools for Wavelet-Based Scalable Multiview Video Coding

A wavelet-based multiview video coding scheme is presented in this paper. It uses a 4-D wavelet transform, which is composed of a 1-D temporal wavelet transform, namely motion compensated temporal filtering, a 1-D view-directional wavelet transform, namely disparity compensated view filtering and a 2-D spatial wavelet transform. Since the presented framework can make use of the inherent scalability properties of the wavelet transforms involved, it allows full scalability of the coded bitstream in the temporal, view, spatial, and quality dimensions. Coding performance close to the H.264/advanced video coding based standard multiview video codec is shown. Enhancements of the view transform, in order to better account for brightness and color variations across views are introduced. Additionally, the use of a signal adaptive anisotropic wavelet packet (WP) transform as a generalization of WP transforms for the spatial decomposition is proposed. Both enhancements lead to a decrease of bit rate of up to 11% compared with the baseline version of the codec.
Compressed sensing of multiview images using disparity compensation

Compressed sensing is applied to multiview image sets and inter-image disparity compensation is incorporated into image reconstruction in order to take advantage of the high degree of inter-image correlation common to multiview scenarios. Instead of recovering images in the set independently from one another, two neighboring images are used to calculate a prediction of a target image, and the difference between the original measurements and the compressed-sensing projection of the prediction is then reconstructed as a residual and added back to the prediction in an iterated fashion. The proposed method shows large gains in performance over straightforward, independent compressed-sensing recovery. Additionally, projection and recovery are block-based to significantly reduce computation time.
Distance Dependent Depth Filtering in 3D Warping for 3DTV

Depth image-based rendering (DIBR) is the process of synthesizing some new "virtual" views from one "real" view and the associated per-pixel depth information. The most important problem in this process is to deal with the newly exposed areas (holes) appearing in the virtual images. One common solution to decrease the number of holes is to pre-process the depth map, before the warping. In this paper, we present a new filtering technique for depth image-based rendering. In order to reduce or completely remove the newly exposed areas an efficient smoothing is necessary for the sharp depth changes near object boundaries. In the meantime it is useless to filter the smooth areas in the depth map. Our solution is based on a weighted Gaussian filter taking into account the distance to the contours. By this way, the geometric distortions and the computation time are reduced compared to a uniform filtering of the depth map. We present some results in the context of creation of stereoscopic views for 3D TV.
Building adaptive 2D wavelet decompositions by update lifting

The paper discusses a method for the construction of nonlinear 2D wavelet decompositions using an adaptive update lifting scheme. A very interesting aspect is that the decomposition does not require any bookkeeping, i.e., it is nonredundant, but that it, nevertheless, allows perfect reconstruction. The major ingredient of the construction is the so-called decision map which triggers the choice of the update filter. Another interesting point is the possibility of better preserving edges, even at low resolutions.
Improved update operators for lifting-based motion-compensated temporal filtering

Motion-compensated temporal filtering (MCTF) is an essential ingredient of the recently developed wavelet-based scalable video coding schemes. Lifting implementation of these decompositions represents a versatile tool for spatiotemporal optimizations, and several improvements have already been proposed in this framework. In this paper, we propose new spatiotemporal update operators based either on weighted averages of multiple connected pixels or on nonlinear filtering of these pixels. We demonstrate the improved performance of the proposed operators, both theoretically and experimentally.
Super-resolution of HEVC videos via convex optimization

Super Resolution (SR) addresses the problem of image and video upscaling. Most of the best performing SR methods do not take into account any compression prior into the degradation model. Consequently, compression artifacts can be undesirably amplified during SR. In the present work, we propose a novel HEVC-dedicated approach for embedding SR results into a domain that closely fits the compressed observation. Our main contribution is the inclusion of HEVC syntax (block size, quantization parameters etc.) into the degradation model. A recent convex optimization approach is used to solve the associated minimization problem. Over a wide range of resolutions and bitrates, we show that our method improves the results obtained with state of the art SR.
Temporal prediction and differential coding of motion vectors in the MCTF framework

Motion compensated temporal filtering (MCTF) is used in wavelet video coding schemes to remove temporal redundancy. Current extensions to MCTF use bi-directional filtering and multiple reference frames to improve the coding performance significantly. However, these extensions lead to an additional cost of coding extra sets of motion vectors. In this paper we propose the use of temporal prediction to reduce the bits needed to code motion vectors. We describe several temporal prediction methods, and highlight the advantages of each scheme. We also use MV prediction during motion estimation, i.e. change the search center and the search range based on the temporal prediction, and describe the tradeoffs to be made between rate, distortion, and complexity. We perform several experiments to illustrate the gains of using temporal prediction, and identify the content dependent nature of such results.
Uniform motion-compensated 5/3 filterbank for subband video coding

Motion-compensated temporal filtering is an essential ingredient of recently developed wavelet-based scalable video coding schemes. Lifting implementation of these decompositions represents a versatile tool for spatio-temporal optimizations and numerous improvements have thus been proposed. In this paper, we propose an alternative structure for the temporal prediction in the 5/3 filterbank. It significantly reduces the ghosting artefacts in the temporal approximation subband frames, providing a higher quality scalability and improved compression performance, for an equivalent complexity.
Dense disparity map representations for stereo image coding

Research in stereo image coding has focused on the disparity estimation/ compensation process to exploit the cross-view redundancies. Most of the reported methods use a classical block-based technique in order to estimate the disparity field. However, this estimation technique does not always provide an accurate disparity map, which may affect the disparity compensation step. In this paper, we propose to use an estimation method that produces a dense and smooth disparity map. Then, on the one hand, this map is segmented and efficiently coded by exploiting the high correlation between neighboring disparity values. On the other hand, we integrate the disparity information into a vector lifting scheme for stereo image coding. Experimental results indicate that the proposed coding scheme outperforms the conventional methods employing a block-based disparity estimation.
Adaptive M-band hierarchical filterbank for compliant temporal scalability in H.264 standard

The paper presents a solution of temporal scalability for video encoded H.264/MPEG-4 AVC bitstreams. Achieved through the concept of adaptive M-band hierarchical filterbanks, the temporal scalability is performed thanks to the application of a frame shuffling operation which allows backward compatibility with the standard to be kept. Simulation results show that this scalability is obtained with no degradation in terms of subjective and objective quality.
Joint Source-Channel Wyner-Ziv Coding in Wireless Video Sensor Networks

In this paper, we show an application of Wyner-Ziv video coding in a wireless video sensor network. A rate estimation technique that permits to determine the compression rate without the need of a feedback channel is proposed. This technique allows the system to assign unequal transmission rates for different video sensors. It employs a cross-layer approach that takes into account the amount of motion in each of the captured video scenes and the transmission conditions of each sensor. Simulation results show an improvement in the average system performance obtained with the proposed rate allocation method compared to the traditional rate allocation technique where all sensors are assigned an equal bandwidth. This improvement will motivate further research on the implementation of Wyner-Ziv video coding in Wireless Video Sensor Networks.
Wavelet-based multi-view video coding with joint best basis wavelet packets

An approach to scalable multi-view video coding with joint best basis wavelet packets is examined in this paper. A 4-D wavelet transform is used to decorrelate the multi-view video data temporally, view-directionally, and spatially for efficient scalable compression. Motion compensated temporal filtering (MCTF) is used for temporal, and disparity compensated view filtering (DCVF) for view- directional decomposition. Adaptive wavelet packets as a generalized wavelet decomposition are presented for spatial decomposition. Two algorithms to find the best basis wavelet packets are evaluated and compared with classical dyadic wavelet transform: a low complexity entropy based best basis search and a search algorithm in a rate-distortion framework. In both cases, the joint best basis is determined for a group of frames rather than for each frame individually. Therefore, the rate to spend for the tree description is minimal while advantage is taken of the similarity of frames within a temporal-view-directional subband.