nils-herrmann/clean_scirepeval huggingface dataset:
A data-driven solution for abandoned object detection: Advantages of multiple types of diversity

The automated detection of abandoned objects is a quickly developing and widely researched field in video processing with specific application to automated surveillance. In the recent years, a number of approaches have been proposed to automatically detect abandoned objects. However, these techniques require prior knowledge of certain properties of the object such as its shape and color, to classify the foreground objects as abandoned object. The performance of tracking-based approaches degrades in complex scenes, i.e., when the abandoned object is occluded or in the case of crowding. In this paper, we propose a data-driven approach based on independent component analysis (ICA) for object detection. We demonstrate the success of the proposed ICA-based methodology with examples of videos with complex scenarios. We also show that algorithm choice plays an important role in performance, in particular when multiple types of diversities are taken into account and demonstrate the importance of order selection.
A distortion evaluation framework in 3D video view synthesis

Demand for 3D content increased over the last years and more attention was dedicated to methods used in 3D content creation and transmission. View synthesis methods introduce localized artifacts when creating new virtual views. Evaluating these methods require therefore a different approach, in order to identify and emphasize synthesis artifact prone areas, while diminishing the impact, of other types of artifacts, such as those produced by quantization during the video coding. In this paper, we propose an artifact evaluation technique which was shown to provide a better differentiation of synthesis methods, while additional information can be extrapolated from the results about the spatial localization of typical synthesis artifacts and their impact on overall image quality.
On a Hashing-Based Enhancement of Source Separation Algorithms Over Finite Fields With Network Coding Perspectives

Blind Source Separation (BSS) deals with the recovery of source signals from a set of observed mixtures, when little or no knowledge of the mixing process is available. BSS can find an application in the context of network coding, where relaying linear combinations of packets maximizes the throughput and increases the loss immunity. By relieving the nodes from the need to send the combination coefficients, the overhead cost is largely reduced. However, the scaling ambiguity of the technique and the quasi-uniformity of compressed media sources makes it unfit, at its present state, for multimedia transmission. In order to open new practical applications for BSS in the context of multimedia transmission, we have recently proposed to use a non-linear encoding to increase the discriminating power of the classical entropy-based separation methods. Here, we propose to append to each source a non-linear message digest, which offers an overhead smaller than a per-symbol encoding and that can be more easily tuned. Our results prove that our algorithm is able to provide high decoding rates for different media types such as image, audio, and video, when the transmitted messages are less than 1.5 kilobytes, which is typically the case in a realistic transmission scenario.
Dense disparity estimation in a multi-view distributed video coding system

Distributed video coding (DVC) is a recent paradigm which aims at transferring part of the coding complexity fromthe encoder to the decoder. The performance of such a coding scheme strongly depends on the capacity to estimate correlation at the decoder and, consequently, on the side information quality. In this paper we consider a multi-view DVC framework and propose a very efficient dense disparity estimation technique for side information construction, based on a variational formulation. The simulation results show that our approach clearly outperforms the existing methods for inter-view side-information generation.
Adaptive video streaming with long term feedbacks

This paper proposes a video streaming system optimizing resource utilization when the media server only disposes of long term feedbacks from the client. Based on a partial knowledge of the network, we developed a scheduling algorithm that exploits the scalable video coding (SVC) properties to estimate packets importance and that takes into account packet delay dependencies to better anticipate congestion situations. Compared to more conventional streaming systems, experimental results show that our approach allows to better face network condition degradation like bandwidth reduction or packet error rate increase.
Multiview Plus Depth Video Coding With Temporal Prediction View Synthesis

Multiview video (MVV) plus depths formats use view synthesis to build intermediate views from existing adjacent views at the receiver side. Traditional view synthesis exploits the disparity information to interpolate an intermediate view by considered inter-view correlations. However, temporal correlation between different frames of the intermediate view can be used to improve the synthesis. We propose a new coding scheme for 3-D High Efficiency Video Coding (HEVC) that allows us to take full advantage of temporal correlations in the intermediate view and improve the existing synthesis from adjacent views. We use optical flow techniques to derive dense motion vector fields (MVF) from the adjacent views and then warp them at the level of the intermediate view. This allows us to construct multiple temporal predictions of the synthesized frame. A second contribution is an adaptive fusion method that judiciously selects between temporal and inter-view prediction to eliminate artifacts associated with each prediction type. The proposed system is compared against the state-of-the-art view synthesis reference software 1-D Fast technique used in 3-D HEVC standardization. Three intermediary views are synthesized. Gains of up to 1.21-dB Bjontegaard Delta peak SNR are shown when evaluated on several standard MVV test sequences.
Redundancy adaptation scheme for network coding with TCP

To address the inability of the standard TCP protocol to distinguish between losses due to congestion and random packet losses on the noisy channel, this paper proposes an algorithm to dynamically adjust the redundancy factor R of the TPC/NC protocol proposed by Sundararajan et al. by adding some additional functionalities to the original network coding layer. We define a loss differentiation scheme to adjust R, based on the Vegas Loss Predictor and the collective feedback information of ACKs and duplicates ACKs, which are both indicators of the network condition. In that way the source adjusts R based on the network conditions, avoiding unnecessary TCP rate reduction linked to packet losses due to transmission impairments and preventing the network from entering in a congestion state. The TPC/NC implementation with our adaptive scheme is full-duplex, and can manage multiple TCP connections simultaneously. Simulation results over realistic network scenarios using OPNET Modeler show that our adaptive scheme in conjunction with the standard TCP/NC produces better TCP-throughputs than the standard TCP/NC, TCP-Reno, TPC New Reno, and TCP Reno with SACKS.
Optimal rate allocation in multi-user Wyner-Ziv video coding systems with coded Key frames

In this paper, we present a rate-distortion analysis for a distributed video coding system, which allows for the selection of a suitable quantization parameter for the H.264 coded key frames, under rate constraints. On the other hand, an adaptive rate allocation and quantization algorithm with a frame dropping strategy is used for the transmission of Wyner-Ziv frames in a multi-user scenario, without the need for a permanent feedback channel. The available bandwidth is dynamically shared between Wyner-Ziv and Key frames. Turbo-codes are used for the joint source-channel coding of the Wyner-Ziv frames and for the protection of the H.264 bitstream from channel errors. Simulation results show the superior performance of the proposed technique, compared to traditional systems with fixed quantization parameters and compression rates.
Spatio-temporal saliency based on rare model

In this paper, a new spatio-temporal saliency model is presented. Based on the idea that both spatial and temporal features are needed to determine the saliency of a video, this model builds upon the fact that locally contrasted and globally rare features are salient. The features used in the model are both spatial (color and orientations) and temporal (motion amplitude and direction) at several scales. To be more robust to moving camera a module computes the global motion and to be more consistent in time, the saliency maps are combined together after a temporal filtering. The model is evaluated on a dataset of 24 videos split into 5 categories (Abnormal, Surveillance, Crowds, Moving camera, and Noisy). This model achieves better performance when compared to several state-of-the-art saliency models.
Wavelet Packet Decompositions for the Analysis of 2-D Fields with Stationary Fractional Increments

Nonstationary two-dimensional random fields with wide-sense stationary fractional increments are introduced using a fractional order for their increments in two directions of space. Some of their properties are studied and their value in modeling nonstationary, possibly anisotropic, fields is shown via examples. A separable wavelet packet analysis proves to be a useful tool in analyzing this kind of texture as it leads to stationary wavelet packet coefficients.
A proximal approach for constrained cosparse modelling

The concept of cosparsity has been recently introduced in the arena of compressed sensing. In cosparse modelling, the ℓ0 (or ℓ1) cost of an analysis-based representation of the target signal isminimized under a data fidelity constraint. By taking benefit from recent advances in proximal algorithms, we show that it is possible to efficiently address a more general framework where a convex block sparsity measure is minimized under various convex constraints. The main contribution of this work is the introduction of a new epigraphical projection technique, which allows us to consider more flexible data fidelity constraints than the standard linear or quadratic ones. The validity of our approach is illustrated through an application to an image reconstruction problem in the presence of Poisson noise.
A Cross-Layer Approach for Dynamic Rate Allocation in H.264 Multi-User Video Streaming

In this paper, we propose a cross-layer approach for the dynamic allocation of unequal transmission rates between several users requesting video from a streaming server. Our allocation technique takes into account distortions caused by source coding, channel impairments and error concealment in order to assign users different priority levels. These priorities are then used to serve users in such a way that the highest levels correspond to the worst performing users. Variable bitrates are achieved by unequal error protection of the different video streams using rate-compatible punctured turbo-codes. Simulation results show a significant improvement in the overall system performance compared to a traditional system where all users are allocated equal channel resources.
Weighted average spatio-temporal update operator for subband video coding

Spatio-temporal motion-compensated wavelet decomposition is an increasingly popular method for scalable video coding, with coding efficiency which is competitive with state-of-the-art nonscalable codecs. In this paper, we propose a new spatio-temporal update operator in the lifting scheme allowing efficient implementation of these temporal decompositions. We demonstrate its improved performance both theoretically, by exhibiting a decrease in the reconstruction error and by simulation results.
Intra Coding With Prediction Mode Information Inference

In a typical competition-based coding, the pertinence of a prediction mode does not only depend on its own efficiency but also on the fact that it is complementary with the other modes. The method proposed in this paper to improve the intra coding of the H.264/AVC standard relies on this remark; it shows how the cost of signaling predictors that are quite similar can be avoided. Indeed, at low bitrates, the information related to the predictor signaling in intra coding reaches up to 25% of the total bitrate for the whole set of standard VCEG test sequences. In order to reduce this cost, a method reproducible at the decoder side is proposed to eliminate some predictors from the intra predictor set. The proposed method exploits the proximity of the predictors in the transform domain in order to obtain a representative and non-redundant set of predictors.
Evaluation of Side Information Effectiveness in Distributed Video Coding

The rate-distortion performance of a distributed video coding system strongly depends on the characteristics of the side information. One could naïvely think that the best side information is the one with the largest PSNR with respect to the original corresponding image. However, previous works have shown that this is not always the case and a reduction of the side information MSE does not always translate into better rate-distortion performance for the complete system. The scope of this paper is to explore a set of metrics other than the PSNR and explicitly designed to classify the side information with respect to its impact on the end-to-end compression performance. A first contribution is to define an experimental framework that can be used to meaningfully compare different metrics for side information evaluation. As a second contribution, our analysis allows to understand why in some cases PSNR-based metrics provide a fairly reliable estimation of the side information quality, while in other cases they do not. This analysis also allows us to introduce a set of new metrics that are better adapted for side information effectiveness evaluation, and that are based on a suitable power of the absolute difference between side information and the original image, or on the Hamming distance between the respective transform coefficients. Besides their theoretical interest, these new metrics can also improve the rate-distortion performance of some distributed video coding systems such as the hash-based ones. We observe improvement up to 74% rate reduction in a simple study case.
Modification of the disparity vector derivation process in 3D-HEVC

The up-and-coming extension of HEVC for 3D video (3D-HEVC) includes various tools to exploit different redundancies in a 3D video signal. Inter-view redundancies are in particular exploited using Inter-View Motion Prediction (IVMP) and Inter-View Residual Prediction (IVRP). Both of these tools compensate disparity-wise the current prediction unit (PU) in order to find its corresponding PU in a base view, from which some prediction information for the current PU is retrieved. The disparity vector (DV) used for disparity compensation is currently derived using a neighboring search process (NBDV) for a DV across spatial and temporal neighbors. The first DV found is selected as the final DV used in IVMP and IVRP, with no guarantee of optimality. In this paper, the NBDV derivation process is changed: all found DVs from different neighbors are stored in a list. Redundant vectors in this list are removed, and a median computation on the remaining vectors is performed. The resulting DV is set as the DV used for IVMP. Average bitrate reductions of 0.6% and 0.8% for the two dependent views and 0.2% on synthesized views are reported with only a slight increase in encoder and decoder runtimes.
Efficient context modeling in scalable 3D wavelet-based video compression

We address the problem of context modeling and coding in the framework of a three dimensional wavelet-based video compression scheme with temporal filtering in the motion direction. Embedded coding algorithms based on a 3D decomposition (like the 3D SPIHT algorithm) provide scalability, which is an important feature for delivering compressed bitstreams over heterogeneous networks. We propose a highly scalable video compression scheme, based on an efficient contextual modeling and a hierarchical encoding of the spatio-temporal trees of wavelet coefficients. We have shown that the efficiency of the final arithmetic encoding stage is increased by performing a statistical modeling based on the context tree weighting method. In order to reflect changes in the statistical behaviors of the different information sources that contribute to the bitstream, several statistical models have been distinguished. As shown by simulations, the proposed method results in significant improvements of the coding efficiency, while keeping the scalability functionality.
Improving H.264 video coding through block oriented transforms

This paper introduces a pre-processing stage for the video coder H.264/MPEG4-AVC that takes advantage of the orientation of the blocks. Contrary to most of the proposed solutions in the literature, it is not the transform that adapts to the signal but the signal that is pre-processed to dasiafitpsila the transform. Residual blocks are straightened out to horizontal or vertical axes with the help of circular shifts applied at the pixel level. These shifts allow to simulate pseudo-rotations in the blocks. They are defined for all the partition sizes (16times16 to 4times4) and the two integer transforms of H.264 (8times8 and 4times4). Size and orientation for the blocks are evaluated with the help of a selection based on a rate-distortion criterion. This pre-processing stage applied on residual frames of a H.264 coder can improve its rate-distortion performances.
Graph-Cut Rate Distortion Algorithm for Contourlet-Based Image Compression

The geometric features of images, such as edges, are difficult to represent. When a redundant transform is used for their extraction, the compression challenge is even more difficult. In this paper we present a new rate-distortion optimization algorithm based on graph theory that can encode efficiently the coefficients of a critically sampled, non-orthogonal or even redundant transform, like the contourlet decomposition. The basic idea is to construct a specialized graph such that its minimum cut minimizes the energy functional. We propose to apply this technique for rate-distortion Lagrangian optimization in subband image coding. The method yields good compression results compared to the state-of-art JPEG2000 codec, as well as a general improvement in visual quality.
An adaptive framework for H.264 optimized encoding

Lossy compression using subband decomposition and quantization has been proven its efficiency, being part of both image and video coding standards. In this paper, we propose an adaptive encoding framework for H.264/AVC, in which the inverse transformation matrix is optimized as function of both content and quantization noise. The inverse transform matrix is adapted in an iterative manner for each frame, and an efficient coding scheme of the resulting synthesis transform is provided. The proposed transform optimization method shows promising results compared to H.264/AVC.
Inter prediction using lapped transforms for advanced video coding

This paper propose the use of lapped transforms in a predictive video coding scheme. Our approach involves an inter-frame, block-based prediction based on lapped transforms and allows rate-distortion optimization, different block sizes and integration with intra-frame prediction. Moreover, this coding scheme strongly reduces the blocking artifacts associated to block-based transforms and permits a better exploitation of the redundancies beyond block borders. The proposed method presents promising results in comparison to the standard implementation of the H.264/AVC, especially for high-definition sequences.
Multistage compressed-sensing reconstruction of multiview images

Compressed sensing is applied to multiview image sets and the high degree of correlation between views is exploited to enhance recovery performance over straightforward independent view recovery. This gain in performance is obtained by recovering the difference between a set of acquired measurements and the projection of a prediction of the signal they represent. The recovered difference is then added back to the prediction, and the prediction and recovery procedure is repeated in an iterated fashion for each of the views in the multiview image set. The recovered multiview image set is then used as an initialization to repeat the entire process again to form a multistage refinement. Experimental results reveal substantial performance gains from the multistage reconstruction.
Adaptive wavelet coding of the depth map for stereoscopic view synthesis

Multi-view video and 3D television are emerging applications raising the problem of efficient encoding of a depth map, in addition to classical texture images. This paper investigates depth image coding via an adaptive wavelet lifting scheme. Switching between long filters in homogeneous areas and short filters over the edges of the depth map is decided based on the contours detected in the texture image. The method takes thus into consideration the correlation existing between the edges in the texture and in the depth image, leading to an improved encoding of the latter one.
Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Optimal rate allocation is among the most challenging tasks to perform in the context of predictive video coding, because of the dependencies between frames induced by motion compensation. 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. This technique is integrated into the recent HEVC encoder, and tested on several standard sequences. Experiments indicate that the proposed rate allocation ensures a better performance (in the rate-distortion sense) than the standard HEVC rate control, and with a little loss with respect to an optimal exhaustive research, which is largely compensated by a much shorter execution time.
Using an exponential power model forwyner ziv video coding

The Laplacian model is the standard distribution for correlation noise estimation at the turbodecoder in Wyner-Ziv coding schemes. In practice, this hypothesis is not always satisfied and, regularly, the estimated model sensibly differs from the error distribution. In this work, we prove that using a model better fitted to the true distribution improves the performances, and we thus propose to use the more general exponential power distribution (EPD) which has never been tested in a distributed video coding context. Gains in rate-distortion over the Laplacian model are illustrated by results on several video sequences, showing that the EPD model outperforms the Laplacian one in off-line (oracle) as well as in on-line (practical implementation) modes. These results also indicate that, in some cases, the online EPD model reduces the bitrate even over the off-line Laplacian model.
A framework for joint multiple description coding and network coding over wireless ad-hoc networks

Network coding (NC) can achieve the maximum information flow in the network by allowing nodes to combine received packets before retransmission. Several papers have shown NC to be beneficial in mobile ad-hoc networks, but the delay introduced by buffered decoding raises a problem in real-time streaming applications. Here we propose to use NC jointly with multiple description coding (MDC) to allow instant decoding of the received packets. The optimal encoding coefficients are chosen via distributed optimisation of the expected video quality. Nodes receive up-to-date information about the network topology through a recently proposed protocol, originally designed for real-time streaming of MDC video. Results show that, due to the limitations imposed by instant decoding to the coding window size, our approach consistently outperforms the popular technique of random linear network coding.