nils-herrmann/clean_scirepeval huggingface dataset:
Testing VBR video traffic for stationarity

It has been previously reported that variable bit rate video traffic displays signs of long-range dependence (Beran et al. 1995, and Garrett 1993), which is defined within the framework of weak stationarity. The concept of weak stationarity is of fundamental practical interest, because it implies the existence and independence with time of mean and autocovariance. Since mean and variance might be used as parameters for policing the traffic stream on a broadband communication network, it is important to verify the underlying assumption of weak stationarity. The results presented in this paper strongly call into question this assumption. As a possible explanation, we offer shifting level processes which are asymptotically weakly stationary for infinite sample sizes.
EFFICIENT MODE SELECTION ALGORITHM USING IMAGE DISTORTION FOR H.264 VIDEO ENCODER

Many video compression algorithms require decisions to be made to select between different coding modes. In the case of H.264, this includes decisions about whether or not motion compensation is used, and the block size to be used for motion compensation. It has been proposed that constrained optimization techniques, such as the method of Lagrange multipliers, can be used to trade off between the quality of the compressed video and the bit rate generated. In this paper, we show that in many cases of practical interest, very similar results can be achieved with much simpler optimizations. Mode selection by simply minimizing the distortion with motion vectors and header information produces very similar results to the full constrained optimization, while it saves 31% mode selection computational time
Towards robust high data-rate hydro-acoustic modems

High data-rate underwater communications can be facilitated by the use of high carrier frequencies. Development of hydroacoustic modems that operate on high carrier frequencies requires knowledge of the channel conditions that may be encountered. Further, the development of mature and reliable systems is expedited by the use of synthetic channel models. Such channel models offer advantages over the existing validation method based on recordings. This is because models can generate a much larger number of channel states than could realistically be recorded. In addition, channel models can be used to test previously untried waveforms, whereas experimental measurements require the field tests to be repeated for any new waveforms.
Improved H.264-based video coding using an adaptive transform

In block-based video coding, the Discrete Cosine Transform (DCT) has been adopted for signal decorrelation in state-of-the-art standards. Although the Karhunen Loeve Transform (KLT) is known to achieve optimal energy compaction, it has been reported to offer only moderate compression as the KLT basis functions are source dependent and hence require the transform itself to be coded. This paper describes a technique for prediction-error block coding using the KLT. The proposed method does not require coding of the KLT bases. Instead the basis functions can be derived at the decoder in a manner similar to the encoder. The proposed method is incorporated into a standard H.264 video codec using an adaptive transform selection approach. Our experiments show that the Peak Signal-to-Noise Ratio (PSNR) improvement of up to 0.9 dB is achieved with the proposed technique when compared with the standard H.264 codec.
An optimized Multiple Description video codec for lossy packet networks

The problem of resilient video transmission over lossy packet networks is addressed in this paper. We propose a rate-distortion optimized multiple description (MD) codec. Two different optimization controls of the codec are described that are suited to rates of packet loss, including the case where packets can travel over multiple paths through the network, with each path-dependent packet-loss probabilities. A packetization method optimized to work seamlessly with the proposed MD codec is also proposed. Simulations performed under various packet loss scenarios show the importance of the two optimizations and also that the proposed framework achieves significantly improved video quality when compared with similar techniques.
Resilient transmission of motion data in multiple description coding of video

In the most common form of wavelet video coding, a three-dimensional Discrete Wavelet Transform (DWT) is applied on a group of video frames (GOF). This approach suffers in compression performance because no motion compensation is used. Other approaches to motion compensated wavelet video coding fails to achieve good video quality in packet loss network due to the absence of a resilient transmission method for the motion data. This paper describes a resilient transmission method for the motion parameters. A multiple description (MD) codec is used to encode the video for lossy network. Motion coding and transmission are incorporated in the MD framework. The resulting codec achieves good compression performance in lossless network as well as good resilience in unreliable network. Experimental results also show encouraging performance of the proposed codec compared to state-of-the-art video codec.
Towards a reliable aquatic-based Cyber Physical System: A new contextsituation aware low overhead routing scheme

Underwater acoustic communication systems are of increasing importance to all nations and organizations performing maritime operations. This paper explores a context-awareness enabled underwater Cyber-Physical Systems (CPS) system, as a critical factor in information sensing and control, this represents a new underwater computing paradigm. To achieve this, fundamentally, we propose a low overhead underwater acoustic sensory protocol to enable the data acquisition. This protocol considers the unique underwater characteristics and achieves increased throughput (packet delivery ratio) through significantly reduced communication overhead. Initial results show the comparisons with other popular underwater communication protocols, that further validates the efficiency of the new system in the proposed CPS framework.
Relationship between length of the chirp and depth of the ocean in active sonar processing

Active sonar is a fairly old area of research in ocean bottom profiling and imaging. A lot of work has already been done related to choice of signal in active sonar which shows that chirp is one of the best candidates. Previous works show that time-bandwidth product of chirp determines SNR and resolution in active estimations. This paper shows that an increase in the length of chirp does not always provide good results, rather it is dependent on the depth of the ocean. A mathematical relationship between the depth of the ocean and length of the chirp used to estimate that depth is demonstrated. Numerical results presented in this paper are in close agreement with theory.
A new statistical model for traffic generated by VBR coders for television on the broadband ISDN

Many techniques have been proposed for modeling source rates generated by variable bit rate video coders. However, little work has been done to verify these models, especially in the area of measuring the quality of their predictions for network performance and the grade of service experienced by a customer. In this paper, we summarize the previous work in this area, highlight some of the difficulties in effectively verifying these models, and suggest methods by which source rate models for VBR video might be more effectively verified in the future. In addition to studying the problem of model verification, a new model is proposed. Using this model, a video sequence is characterized by four parameters. As in previous cases, the number of bits generated by the coder for a sequence of video frames is modelled. The new model differs from previous models in that this sequence is not Markovian.
Motion-based Object Segmentation using Sprites and Anisotropic Diffusion

Many algorithms have been developed to recognize regions, edges, color, and objects in images and videos. For applications like surveillance or object-based video coding, it is important to segment the foreground objects from the background. The task is very challenging in the case of a moving camera. We present a foreground segmentation approach that is designed for sprite coding as well as other applications, e.g. video surveillance. Accurate frame-to- frame image registration and sprite generation build the pre-processing step. The segmentation algorithm operates on error images, which are produced by the image registration and subtraction from reconstructed background frames. It is processed in several steps including low-pass filtering using anisotropic diffusion. Experiments show excellent results with single- and multi-view test sequences.
An adaptive low-complexity global motion estimation algorithm

One important recent application of image registration has been in the estimation of global motion parameters for object-based video coding. A limitation of current global motion estimation approaches is the additional complexity of the gradient-descent optimization that is typically required to calculate the optimal set of global motion parameters. In this paper we propose a new low-complexity algorithm for global motion estimation. The complexity of the proposed algorithm is reduced by performing the majority of the operations in the gradient-descent optimization using logic operations rather than full-precision arithmetic operations. This use of logic operations means that the algorithm can be implemented much more easily in hardware platforms such as field programmable gate arrays (FPGAs). Experimental results show that the execution time for software implementations of the new algorithm is reduced by a factor of almost four when compared to existing fast implementations without any significant loss in registration accuracy.
Windowed Image Registration for Robust Mosaicing of Scenes with Large Background Occlusions

We propose an enhanced window-based approach to local image registration for robust video mosaicing in scenes with arbitrarily moving foreground objects. Unlike other approaches, we estimate accurately the image transformation without any pre-segmentation even if large background regions are occluded. We apply a windowed hierarchical frame-to-frame registration based on image pyramid decomposition. In the lowest resolution level phase correlation for initial parameter estimation is used while in the next levels robust Newton-based energy minimization of the compensated image mean-squared error is conducted. To overcome the degradation error caused by spatial image interpolation due to the warping process, i.e. aliasing effects from under-sampling, final pixel values are assigned in an up-sampled image domain using a Daubechies bi-orthogonal synthesis filter. Experimental results show the excellent performance of the method compared to recently published methods. The image registration is sufficiently accurate to allow open-loop parameter accumulation for long-term motion estimation.
A tipping point for undersea networks

For a long time cable-free undersea communication devices have provided a very modest communication capability. Relying on acoustic signals transmitted through a lossy channel with complex propagation characteristics they have provided unreliable connections and very low data rates. Because of the harsh environment, undersea modems have been very expensive, which in turn has limited their deployment and the resources available for development and production. We are now at a tipping point where the technology is available to produce systems that are orders of magnitude more useful to users than previous products. In this paper we outline the key developments in undersea modems from the user perspective.
Power Distortion Optimization for Uncoded Linear Transformed Transmission of Images and Videos

Recently, there is a resurgence of interest in uncoded transmission for wireless visual communication. While conventional coded systems suffer from cliff effect as the channel condition varies dynamically, uncoded linear-transformed transmission (ULT) provides elegant quality degradation for wide channel SNR range. ULT skips non-linear operations, such as quantization and entropy coding. Instead, it utilizes linear decorrelation transform and linear scaling power allocation to achieve optimized transmission. This paper presents a theoretical analysis for power-distortion optimization of ULT. In addition to the observation in our previous work that a decorrelation transform can bring significant performance gain, this paper reveals that exploiting the energy diversity in transformed signal is the key to achieve the full potential of decorrelation transform. In particular, we investigated the efficiency of ULT with exact or inexact signal statistics, highlighting the impact of signal energy modeling accuracy. Based on that, we further proposed two practical energy modeling schemes for ULT of visual signals. Experimental results show that the proposed schemes improve the quality of reconstructed images by 3~5 dB, while reducing the signal modeling overhead from hundreds or thousands of meta data to only a few meta data. The perceptual quality of reconstruction is significantly improved.
Low rank regularization exploiting intra and inter patch correlation for image denoising

Based on the observation that a matrix X consisted of non-local highly-correlated patches is of low rank, many image restoration methods use low-rank regularization to exploit correlation between image contents, so that the uncertainty of the unknown image signal can be reduced. To tackle the problem that the underlying cost function to pursue minimal rank is hard to solve, an effective way is to employ smooth non-convex surrogate log |XXT| for the rank penalty. Essentially, such technique only considers to utilize correlation within image patches. In this paper, we propose to jointly exploit both intra- and inter-patch correlation of the input image, so as to further reduce the uncertainty of the signal, and thus improve the prediction of the latent image. The corresponding two surrogates are integrated to incorporate both intra- and inter-patch correlation. To solve the optimization problem, we use iterative alternating direction technique to divide the problem into two subproblems, each of which is solved via an empirical Bayesian procedure built upon variational approximation. Experimental results show that the proposed approach outperforms several state-of-the-art methods in terms of PSNR and perceptual quality.
Registration-reliability based strategy to enhance multi-frame super-resolution algorithms

Image registration plays an important role in most of multi-frame super-resolution methods. As far as we know, the accuracy of most registration algorithms is not enough for superresolution, which will lead to annoying artifacts. This paper proposes a simple but effective strategy that aims to enhance the performance of existing super-resolution methods. The idea is to measure the reliability of the estimated shifts and only choose the reliable frames to reconstruct a coarse high-resolution (HR) image. This coarse HR image helps to refine all the shifts to finally get a refined HR image. An iterative contour smoothing filter is proposed to improve the accuracy of this refining process. Experimental results demonstrate that the proposed algorithm can help to improve the performance of the existing superresolution methods with fewer artifacts.
Image Denoising via Low Rank Regularization Exploiting Intra and Inter Patch Correlation

In image restoration tasks, image priors generally utilize correlation within image contents to predict the latent image signal. In this paper, we propose to jointly exploit both intra- and inter-patch correlation of the input image, so as to further reduce the uncertainty of the unknown signal, and thus improve the prediction of the latent image. The proposed scheme evolves from the low-rank regularization for non-local highly-correlated image contents. Since the underlying cost function to pursue minimal rank is hard to solve, we use non-convex smooth surrogates for the rank penalty. Two such surrogates are utilized in order to incorporate both intra- and inter-patch correlation. To tackle the optimization problem, we use iterative alternating direction technique to divide the problem into two subproblems, each of which is solved via an empirical Bayesian procedure built upon variational approximation. Experimental results on image denoising show that the proposed approach outperforms several state-of-the-art methods in terms of peak signal-to-noise ratio, structural similarity, and perceptual quality.
A Lifting-Based Wavelet Transform Supporting Non-Dyadic Spatial Scalability

In subband image and video compression, the issue of dyadic spatial scalability with a downsizing ratio of 2:1 has already been widely investigated. In some application scenarios, however, other downsizing ratios can be desirable. This paper proposes a wavelet coding scheme to support non-dyadic spatial scalability. By extending lifting-based transform structure, we show how wavelet transform can directly support non-dyadic spatial scalability. Specially, based on the extended lifting framework, a wavelet transform is designed to decompose an image with size 3W×3H to a low-pass subband with size 2W×2H and three high-pass subbands to support 3:2-ratio spatial scalability. The characteristic of proposed wavelet low-pass filter is investigated and experimental results show that the low resolution image produced by our scheme has good quality while the coding performance just has marginal loss compared to Daubechies 9/7 filter.
Hybrid Intraprediction Based on Local and Nonlocal Correlations

In the latest video coding standard, namely, high-efficiency video coding (HEVC), intra coding efficiency is significantly improved by a quadtree partition structure and more intra prediction modes. For intra coding, 35 intra modes are employed including 33 angular intra prediction (AIP) modes that are effective for blocks with strong directions, and a planar mode and a dc mode that are used to predict smooth regions. However, intra prediction in HEVC still cannot handle complicated blocks well. To deal with this problem, this paper proposes a hybrid intra prediction method to improve the intra prediction efficiency. The proposed hybrid intra prediction method consists of three parts: adaptive template matching prediction (ATMP) by exploring nonlocal correlation, combined local and nonlocal prediction by exploring both local correlation for AIP and non-local correlation for ATMP, and combined neighboring modes prediction that can generate smooth prediction by exploring more local correlations. Experimental results suggest that the proposed hybrid intra prediction method achieves 2.8% BD-rate reduction on average for luma compared to HEVC reference software HM-14.0 under all intra main configurations. The gain can be up to 6.9%. When integrated into joint exploration model −1.0, the proposed method still can achieve 1.2% BD-rate reduction for luma.
Improved total variation based image compressive sensing recovery by nonlocal regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this paper presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.
Optimal PET Protection for Streaming Scalably Compressed Video Streams With Limited Retransmission Based on Incomplete Feedback

For streaming scalably compressed video streams over unreliable networks, Limited-Retransmission Priority Encoding Transmission (LR-PET) outperforms PET remarkably since the opportunity to retransmit is fully exploited by hypothesizing the possible future retransmission behavior before the retransmission really occurs. For the retransmission to be efficient in such a scheme, it is critical to get adequate acknowledgment from a previous transmission before deciding what data to retransmit. However, in many scenarios, the presence of a stochastic packet delay process results in frequent late acknowledgments, while imperfect feedback channels can impare the server's knowledge of what the client has received. This paper proposes an extended LR-PET scheme, which optimizes PET-protection of transmissed bitstreams, recognizing that the received feedback information is likely to be incomplete. Similar to the original LR-PET, the behavior of future retransmissions is hypothesized in the optimization objective of each transmission opportunity. As the key contribution, we develop a method to efficiently derive the effective recovery probability versus redundancy rate characteristic for the extended LR-PET communication process. This significantly simplifies the ultimate protection assignment procedure. This paper also demonstrates the advantage of the proposed strategy over several alternative strategies.
Enhanced inter prediction with localized weighted prediction in HEVC

Inter prediction plays an important role in most video encoding systems since it could significantly improve coding performance. The more accurate the prediction of the current block, the smaller the residual, and the higher coding efficiency could be achieved accordingly. In this paper, a localized weighted prediction method is proposed to improve inter prediction accuracy. The linear regression improvement model is employed to modify the prediction pixel values. The weighting parameters are estimated in both encoder and decoder, no additional bits are required to be transmitted. The proposed method shows better coding performance than previous methods, including the explicit weighted prediction method in High Efficiency Video Coding (HEVC). Experimental results show that the BD bit rate saving of the proposed method is up to 7.4% compared to HM12.0, while the decoding complexity is almost the same.
Non-local extension of total variation regularization for image restoration

Total-variation (TV) regularization is widely adopted in image restoration problems to exploit the feature that natural images are smooth with small gradient values at most regions. Basic TV method assumes identical zero-mean Laplacian distribution for the gradients at all pixels. However, for real-world images, the statistics of gradients may not be stationary, and the zero-mean assumption of gradients may not be valid either for a specific pixel. This paper presents a non-local extension of TV regularization for image restoration, called Non-Local Gradient Sparsity Regularization (NGSR). The NGSR model employs a separate gradient value distribution for each pixel. To figure out the distribution parameters, the NGSR method exploits a set of patches which are similar to the patch centered at current pixel and estimates the distribution parameter adaptively. Experimental results demonstrate that the proposed NGSR outperforms traditional TV remarkably for image restoration.