nils-herrmann/clean_scirepeval huggingface dataset:
Compressive online robust principal component analysis with multiple prior information

Online Robust Principle Component Analysis (RPCA) arises naturally in time-varying signal decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Unlike conventional batch RPCA, which processes all the data directly, our method considers a small set of measurements taken per data vector (frame). Moreover, our method incorporates multiple prior information signals, namely previous reconstructed frames, to improve the separation and thereafter, update the prior information for the next frame. Using experiments on synthetic data, we evaluate the separation performance of the proposed algorithm. In addition, we apply the proposed algorithm to online video foreground and background separation from compressive measurements. The results show that the proposed method outperforms the existing methods.
Experimental Comparison of Probabilistic Shaping Methods for Unrepeated Fiber Transmission

This paper studies the impact of probabilistic shaping on effective signal-to-noise ratios (SNRs) and achievable information rates (AIRs) in a back-to-back configuration and in unrepeated nonlinear fiber transmissions. For the back-to-back setup, various shaped quadrature amplitude modulation (QAM) distributions are found to have the same implementation penalty as uniform input. By demonstrating in transmission experiments that shaped QAM input leads to lower effective SNR than uniform input at a fixed average launch power, we experimentally confirm that shaping enhances the fiber nonlinearities. However, shaping is ultimately found to increase the AIR, which is the most relevant figure of merit, as it is directly related to spectral efficiency. In a detailed study of these shaping gains for the nonlinear fiber channel, four strategies for optimizing QAM input distributions are evaluated and experimentally compared in wavelength division multiplexing (WDM) systems. The first shaping scheme generates a Maxwell–Boltzmann (MB) distribution based on a linear additive white Gaussian noise channel. The second strategy uses the Blahut–Arimoto algorithm to optimize an unconstrained QAM distribution for a split-step Fourier method based channel model. In the third and fourth approach, MB-shaped QAM and unconstrained QAM are optimized via the enhanced Gaussian noise (EGN) model. Although the absolute shaping gains are found to be relatively small, the relative improvements by EGN-optimized unconstrained distributions over linear AWGN optimized MB distributions are up to 59%. This general behavior is observed in 9-channel and fully loaded WDM experiments.
Texture side information generation for distributed coding of video-plus-depth

We consider distributed video coding in a monoview video-plus-depth scenario, aiming at coding textures jointly with their corresponding depth stream. Distributed Video Coding (DVC) is a video coding paradigm in which the complexity is shifted from the encoder to the decoder. The Side Information (SI) generation is an important element of the decoder, since the SI is the estimation of the to-be-decoded frame. Depth maps enable the calculation of the distance of an object from the camera. The motion between depth frames and their corresponding texture frames (luminance and chrominance components) is strongly correlated, so the additional depth information may be used to generate more accurate SI for the texture stream, increasing the efficiency of the system. In this paper we propose various methods for accurate texture SI generation, comparing them with other state-of-the-art solutions. The proposed system achieves gains on the reference decoder up to 1.49 dB.
Extending models for two-dimensional constraints

Random fields in two dimensions may be specified on 2 × 2 elements such that the probabilities of finite configurations and the entropy may be calculated explicitly. The Pickard random field is one example where probability of a new (nonboundary) element is conditioned on three previous elements. To extend the concept we consider extending such a field such that a vector or block of elements is conditioned on a larger set of previous elements. Given a stationary model defined on 2 × 2 elements, iterative scaling is used to define the extended model. The extended model may be used for models of two-dimensional constraints and as examples we apply it to the hard-square constraint and the no isolated bits (n.i.b) constraint. The iterative scaling can ensure that the entropy of the extension is optimized and that the entropy is increased compared to the initial model defined on 2 × 2 elements. Application to a simple stationary model with hidden states is also outlined. For the n.i.b constraint, the initial model is based on elements defined by blocks of (1 × 2) binary symbols.
Approximating the constellation constrained capacity of the MIMO channel with discrete input

In this paper the capacity of a Multiple Input Multiple Output (MIMO) channel is considered, subject to average power constraint, for multi-dimensional discrete input, in the case when no channel state information is available at the transmitter. We prove that when the constellation size grows, the QAM constrained capacity converges to Gaussian capacity, directly extending the AWGN result from . Simulations show that for a given constellation size, a rate close to the Gaussian capacity can be achieved up to a certain SNR point, which can be found efficiently by optimizing the constellation for the equivalent orthogonal channel, obtained by the singular value decomposition. Furthermore, lower bounds on the constrained capacity are derived for the cases of square and tall MIMO matrix, by optimizing the constellation for the equivalent channel, obtained by QR decomposition.
Noise residual learning for noise modeling in distributed video coding

Distributed video coding (DVC) is a coding paradigm which exploits the source statistics at the decoder side to reduce the complexity at the encoder. The noise model is one of the inherently difficult challenges in DVC. This paper considers Transform Domain Wyner-Ziv (TDWZ) coding and proposes noise residual learning techniques that take residues from previously decoded frames into account to estimate the decoding residue more precisely. Moreover, the techniques calculate a number of candidate noise residual distributions within a frame to adaptively optimize the soft side information during decoding. A residual refinement step is also introduced to take advantage of correlation of DCT coefficients. Experimental results show that the proposed techniques robustly improve the coding efficiency of TDWZ DVC and for GOP=2 bit-rate savings up to 35% on WZ frames are achieved compared with DISCOVER.
Algorithms for coding scanned halftone pictures

A method for coding scanned documents containing halftone pictures, e.g. newspapers and magazines, for transmission purposes is proposed. The halftone screen is estimated and the grey value of each dot is found, thus giving a compact description. At the receiver the picture is rescreened. A novel data structure and related algorithms for handling the digital screen without restrictions on the screen parameters is presented. Data compression rates above 20 are obtained for the halftone pictures. The algorithms are suited for implementation with fast dedicated hardware. The rescreening can also be used as digital halftoning with arbitrary screens, using lookup tables.<<ETX>>
Constellation Shaping for Fiber-Optic Channels With QAM and High Spectral Efficiency

In this letter, the fiber-optic communication channel with a quadrature amplitude modulation (QAM) input constellation is treated. Using probabilistic shaping, we show that high-order QAM constellations can achieve and slightly exceed the lower bound on the channel capacity, set by ring constellations. We then propose a mapping function for turbo-coded bit-interleaved coded modulation based on optimization of the mutual information between the channel input and output. Using this mapping, spectral efficiency as high as 6.5 bits/s/Hz/polarization is achieved on a simulated single channel long-haul fiber-optical link excluding the pilot overhead, used for synchronization, and taking into account frequency and phase mismatch impairments, as well as laser phase noise and analog-to-digital conversion quantization impairments. The simulations suggest that major improvements can be expected in the achievable rates of optical networks with high-order QAM.
Coding with partially hidden Markov models

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A forward-backward reestimation of parameters with a redefined backward variable is given for these models and used for estimating the unknown parameters. Proof of convergence of this reestimation is given. The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt to instationarities in the images.
An objective method for High Dynamic Range source content selection

With the aim of improving the immersive experience of the end user, High Dynamic Range (HDR) imaging has been gaining popularity. Therefore, proper validation and performance benchmarking of HDR processing algorithms is a key step towards standardization and commercial deployment. A crucial component of such validation studies is the selection of a challenging and balanced set of source (reference) HDR content. In order to facilitate this, we present an objective method based on the premise that a more challenging HDR scene encapsulates higher contrast, and as a result will show up more visible errors on contrast reduction. This information is subsequently analyzed via fuzzy clustering to enable a probabilistic interpretation. To evaluate the proposed approach, we performed an experimental study on a large set of publicly available HDR images.
Compressive Online Robust Principal Component Analysis via n-ℓ1 Minimization

This paper considers online robust principal component analysis (RPCA) in time-varying decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Different from conventional batch RPCA, which processes all the data directly, our approach considers a small set of measurements taken per data vector (frame). Moreover, our algorithm can incorporate multiple prior information from previous decomposed vectors via proposing an $n$ - $\ell _{1}$ minimization method. At each time instance, the algorithm recovers the sparse vector by solving the $n$ - $\ell _{1}$ minimization problem—which promotes not only the sparsity of the vector but also its correlation with multiple previously recovered sparse vectors—and, subsequently, updates the low-rank component using incremental singular value decomposition. We also establish theoretical bounds on the number of measurements required to guarantee successful compressive separation under the assumptions of static or slowly changing low-rank components. We evaluate the proposed algorithm using numerical experiments and online video foreground-background separation experiments. The experimental results show that the proposed method outperforms the existing methods.
Reduced complexity MPEG2 video post-processing for HD display

This paper presents MPEG(2) decoder post-processing for high definition (HD) flat panel displays. The focus is to design efficient post-processing to reduce blocking and ringing artifacts. Standard deblocking modules are improved to obtain a significant load reduction through a new DCT based control scheme. Standard deringing modules are enhanced through adaptive thresholding to improve the image quality. The schemes are implemented in a MPEG2 decoder for evaluation. The enhanced deblocking filter results in load reduction with an overall reduction in execution time of 41~46% over the basic implementation. The enhanced deringing combined with the deblocking achieves PSNR improvements on average of 0.5 dB over the basic deblocking and deringing on SDTV and HDTV test sequences. The deblocking and deringing models described in the paper are generic and applicable to a wide variety of common (8times8) DCT-block based real-time video schemes.
Simplified fiber-wireless distribution of HD video in passive and active W-band close proximity terminals

We experimentally demonstrate uncompressed high-definition (HD) video distribution at 84 GHz Radio over Fiber link achieving up to 3 meters of wireless transmission. We experimentally emulate Metro-Access architecture by deploying single/multimode fibers. Passive and active approaches for remote antenna unit (RAU) are experimentally investigated. The bit error rate (BER) performance of the optical and wireless channels is reported. A successful transmission of uncompressed HD video in the W-band wireless channel is demonstrated with prospects to pave the way for application-focused fiber-wireless connectivity supported by diverse optical fiber distribution infrastructure.
Controlling Power Consumption for Displays With Backlight Dimming

Backlight dimming of Liquid Crystal Displays (LCD) is a technology which aims at saving power and improving visual quality. The evolution of energy standards and the increasing public expectations regarding power consumption have made it necessary for backlight systems to manage their power. Such a control is challenging to implement, because for LCD displays quality and power are closely interlinked, and one cannot be modified without affecting the other. To address this issue, we present a framework for power controlled backlight dimming defining some key concepts. Two methods to obtain backlights with a predefined power level for images are presented: one method has low complexity and the other achieves high performance in terms of quality/power trade-off. Those methods are evaluated on a modeled Light-Emitting Diode edge-lit backlight display. The high-performance method performs significantly better than other algorithms from the literature, when considering both calculated power and quality. This high-performance method is then extended to video in three modes. The first mode favors high quality in a power-aware manner and allow significant power variations, the second mode has strict power constraints and the third one provides a trade-off between the other two.
Game-theoretic rate-distortion-complexity optimization for HEVC

This paper presents an algorithm for rate-distortion-complexity optimization for the emerging High Efficiency Video Coding (HEVC) standard, whose high computational requirements urge the need for low-complexity optimization algorithms. Optimization approaches need to specify different complexity profiles in order to tailor the computational load to the different hardware and power-supply resources of devices. In this work, we focus on optimizing the quantization parameter and partition depth in HEVC via a game-theoretic approach. The proposed rate control strategy alone provides 0.2 dB improvement compared to the approach implemented in HEVC reference software, while rate-distortion-complexity optimization allows very accurate complexity control providing at the same time rate-distortion performance close to the optimal one.
Measures of Effective Video Tracking

To evaluate multitarget video tracking results, one needs to quantify the accuracy of the estimated target-size and the cardinality error as well as measure the frequency of occurrence of ID changes. In this paper, we survey existing multitarget tracking performance scores and, after discussing their limitations, we propose three parameter-independent measures for evaluating multitarget video tracking. The measures consider target-size variations, combine accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy levels, and evaluate ID changes relative to the duration of the track in which they occur. We conduct an extensive experimental validation of the proposed measures by comparing them with existing ones and by evaluating four state-of-the-art trackers on challenging real-world publicly-available data sets. The software implementing the proposed measures is made available online to facilitate their use by the research community.
WiSE-MNet: an experimental environment for wireless multimedia sensor networks

We propose a simulation environment for networks for Wireless Multimedia Sensor Networks (WMSNs), i.e. net- works with sensors capturing complex vectorial data, such as for example video and audio. The proposed simulation environment allows us to model the communication layers, the sensing and distributed applications of a WMSN. This Wireless Simulation Environment for Multimedia Networks (WiSE-MNet) is based on Castalia/Omnet++ and is available as open source to the research community . The environment is designed to be flexible and extensible, and has a simple camera model that enables the simulation of distributed computer-vision algorithms at a high level of abstraction. We demonstrate the effectiveness of WiSE-MNet with a distributed tracking application. (5 pages)
Detecting group interactions by online association of trajectory data

We propose a method for detecting group interactions for groups of varying number of objects. We model each object as a moving agent with a direction-aware interest map and group interactions as mutual interests between objects. After grouping objects into unit interactions individually in each frame, we solve the temporal association problem by tracking group interaction over consecutive frames. Optimal grouping is obtained by finding the maximum weight spanning tree of a directed graph formed by objects and their potential interactions. Experimental results show that our method obtained around 80% recalling rates on two publicly available datasets.
Semantic segmentation and description for video transcoding

We present an automatic content-based video transcoding algorithm, which is based on how humans perceive visual information. The transcoder support multiple video objects and their description. First the video is decomposed into meaningful objects through semantic segmentation. Then the transcoder adapts its behavior to code relevant (foreground) and non-relevant objects differently. Both objects-based and frame-based encoders are combined with semantic segmentation. Experimental results show that the use of semantics and description prior to transcoding reduces the bandwidth requirements and makes it possible to adapt the video representation to limited network and terminal device capabilities still retaining the essential information.
Multifeature Object Trajectory Clustering for Video Analysis

We present a novel multifeature video object trajectory clustering algorithm that estimates common patterns of behaviors and isolates outliers. The proposed algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification of normal and rare object motion patterns. First we transform the trajectories into a set of feature spaces on which mean-shift identifies the modes and the corresponding clusters. Furthermore, a merging procedure is devised to refine these results by combining similar adjacent clusters. The final common patterns are estimated by fusing the clustering results across all feature spaces. Clusters corresponding to reoccurring trajectories are considered as normal, whereas sparse trajectories are associated to abnormal and rare events. The performance of the proposed algorithm is evaluated on standard data-sets and compared with state-of-the-art techniques. Experimental results show that the proposed approach outperforms state-of-the-art algorithms both in terms of accuracy and robustness in discovering common patterns in video as well as in recognizing outliers.
Localization of distributed wireless cameras

Cooperative cameras enable monitoring wide areas and detecting actions and events on a large scale. Due to hardware advancements and economic factors, distributed networks are becoming widely used for a variety of applications ranging from traffic monitoring and surveillance in shopping malls to sports coverage. However, the localization of a large number of cameras in a wide area is not a trivial task. Manual methods are time consuming and can be inaccurate over time. For this reason, we propose an algorithm that uses measurements from the observed objects to perform pair-wise automatic localization of a distributed set of cameras with non-overlapping fields of view. We use the temporal information derived from trajectory information to estimate unobserved trajectory segments, which are then used to estimate the position of the cameras on a common ground plane. Furthermore, the exit-entrance direction of the moving objects is used to estimate the relative orientation of adjacent cameras. We demonstrate the algorithm on a distributed network of simulated cameras with wireless communication and compare it with centralized state-of-the-art approaches.
Tracking Atoms with Particles for Audio-Visual Source Localization

We present a general framework and an efficient algorithm for tracking relevant video structures. The structures to be tracked are implicitly defined by a matching pursuit procedure that extracts and ranks the most important image contours. Based on the ranking, the contours are automatically selected to initialize a particle filtering tracker. The proposed algorithm deals with salient video entities whose behavior has an intuitive meaning, related to the physics of the signal. Moreover, as the interactions between such structures are easily defined, the inference of higher level signal configurations can be made intuitive. The proposed algorithm improves the performance of existing video structures trackers, while reducing the computational complexity. The algorithm is demonstrated on audio-visual source localization.
Single camera calibration for trajectory-based behavior analysis

Perspective deformations on the image plane make the analysis of object behaviors difficult in surveillance video. In this paper, we improve the results of trajectory-based scene analysis by using single camera calibration for perspective rectification. First, the ground-plane view is estimated from perspective images captured from a single camera. Next, unsupervised fuzzy clustering is applied on the transformed trajectories to group similar behaviors and to isolate outliers. We evaluate the proposed approach on real outdoor surveillance scenarios with standard datasets and show that perspective rectification improves the accuracy of the trajectory clustering results.