nils-herrmann/clean_scirepeval huggingface dataset:
Cross-Layer Optimization With Model-Based Parameter Exchange

Cross-layer optimization (CLO) promises significant gains in comparison to a conventional system design, which does not allow for information exchange across layers. One of the key challenges in CLO is the exchange of parameters between optimizer and layers. In this paper a model-based approach is presented that drastically reduces the amount of parameters that are to be exchanged. The optimizer employs models of the respective layers that emulate the communication system within the optimizer. The layers then only need to pass a small number of model parameters to the optimizer. This general concept is applied to CLO between application (APP) layer and medium access control (MAC) layer of a radio communications system. Our proposed model for the MAC layer is suitable for a transmitter without instantaneous channel state information (CSI). Simulation results demonstrate that the proposed model-based CLO is able to exploit the available diversity to enhance the system capacity. Dependent on the application characteristics, the same perceived quality in terms of mean opinion score (MOS) is maintained, while increasing the number of served users by up to 25%. Compared to known CLO approaches, much fewer parameters need to be exchanged.
A haptic texture database for tool-mediated texture recognition and classification

While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six well-established features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.
AVLAD: Optimizing the VLAD Image Signature for Specific Feature Descriptors

Recent works on content-based image retrieval have successfully used the Vector of Locally Aggregated Descriptors (VLAD) as a compact image signature. In this paper, we improve the VLAD signature by tailoring the VLAD representation to the specific properties of the visual feature descriptors. We combine this improvement with the recently proposed hierarchical VLAD approach and demonstrate the effectiveness of this extension for the two well-known feature descriptors SIFT and SURF. Furthermore, we investigate how to efficiently reduce the dimensionality of the resulting representation using different unsupervised dimensionality reduction techniques.
Texture compensation for haptic feedback signal compression

Recently proposed haptic offline compression algorithms remove perceptually irrelevant haptic samples to achieve data reduction. At display-time, the irregularly subsampled haptic signal is resampled at a higher constant sampling rate using interpolation. Such algorithms, however, have an important drawback. Although they are well suited for large-amplitude quasi-static feedback forces, low-amplitude high-frequency texture information is adversely affected. This informative tactile high-frequency component, critical to convey convincing realistic haptic impressions, needs to be treated separately for compression. To this end, we extract important tactile elements crucial to texture perception from the haptic signal in the time domain and propose a method to encode them so that the overall storage requirements are minimized. We then synthesize and superimpose them onto the reconstructed signal at display-time. Psychophysical tests confirm that the proposed approach significantly improves the texture assessment quality during playback, while reducing storage space requirements by up to 97%.
On the design of a novel JPEG quantization table for improved feature detection performance

Keypoint or interest point detection is the first step in many computer vision algorithms. The detection performance of the state-of-the-art detectors is, however, strongly influenced by compression artifacts, especially at low bit rates. In this paper, we design a novel quantization table for the widely-used JPEG compression standard which leads to improved feature detection performance. After analyzing several popular scale-space based detectors, we propose a novel quantization table which is based on the observed impact of scale-space processing on the DCT basis functions. Experimental results show that the novel quantization table outperforms the JPEG default quantization table in terms of feature repeatability, number of correspondences, matching score, and number of correct matches.
Low-complexity block size decision for HEVC intra coding using binary image feature descriptors

We present a novel low-complexity algorithm for feature-based block size decision in HEVC intra coding. Our approach evaluates a set of point pairs within a coding unit (CU) in order to determine whether a CU should be further split into smaller sub CUs for coding. We apply a modified version of the Binary Robust Independent Elementary Feature (BRIEF) descriptor, which in its original version is usually applied to describe local image properties in the context of image/video analysis. While the elements of the original BRIEF descriptor describe which pixel of a pixel pair has the higher value, the modified descriptor evaluates whether the difference between the pixel values is above a pre-defined threshold, which is determined by training. If a certain number of pixel pair differences exceed their corresponding threshold the CU is split into its four sub CUs. Furthermore, we restrict the feature point pairs to be located within different potential sub CUs. For an adaptive training approach, we achieved in our experiments an average encoding time reduction of 58% compared to the HEVC reference software HM12.0 with an average rate increase of 3.8% at equal quality and an average encoding time reduction of 65% with an average rate increase of 5.98% at equal quality for an offline training approach.
Telepresence Across Networks: A Combined Deadband and Prediction Approach

Executing telepresence operations across networks poses a number of challenges. In particular, the transfer of certain data types requires high sampling rates, such as haptic and velocity data. This paper presents a two-step approach which reduces the total amount of data sent across a network, while attempting to reconstruct the original data series. The first step utilises a deadband approach to eliminate the transfer of redundant data. In this sense, redundant refers to the fact that the data will be undetectable or of no consequence at the receiving end. The second step is a prediction implementation to best reconstruct the reduced series to its original form. This combined deadband and prediction approach is applied to velocity data extracted from a telepresence scenario, resulting in an 86% reduction in the amount of data transferred, and good data reconstruction properties
Proxy-based transmission strategies for wireless video streaming

We present proxy-based transmission strategies for improving the quality of delay-critical video streams which are transmitted in parallel on the downlink of a cellular system. Compared to state-of-the-art radio link buffer management, our solution allows to shape the rate of the incoming video streams in a rate-distortion optimized way before congestion may occur. Furthermore, joint optimization across multiple parallel streams is possible to avoid blocking of certain users by less important data units of others. The proposed solution only requires periodic reporting of abstract side information from the radio link layer in terms of the current buffer status and the average throughput. We demonstrate the improved perceptual quality and fairness that can be achieved with our solution for a simple downlink scenario.
Objective quality prediction for haptic texture signal compression

Perceptual quality for media compression algorithms is traditionally evaluated through user studies. Such studies are time consuming, laborious and expensive, slowing down the development of new signal processing algorithms. To address this problem, a number of algorithmic quality prediction methodologies have been developed in the audio and video fields, something that is currently lacking in haptics research. In this paper, we present a novel method for predicting the perceptual quality degradation of compressed haptic texture signals. For this purpose, abstract perceptual features like Roughness, Brightness, etc. that capture the subjective experience of textures are exploited, in addition to low-level psychophysical models from the literature. As compared to the state-of-the-art, the presented prediction methodology shows an approximately 30% improvement in explaining the variance in the perceptual data.
RDTC Optimized Streaming for Remote Browsing in Image-Based Scene Representations

Remote navigation in compressed image-based scene representations requires random access to arbitrary parts of the reference image data to recompose virtual views. The degree of inter-frame dependencies exploited during compression has an impact on the effort needed to access reference images and delimits the rate distortion (RD) trade-off that can be achieved. If, additionally, a given receiver hardware and a maximum available channel bitrate is taken into account, the traditional rate-distortion optimization is extended to an RDTC trade-off between rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). In this work we introduce our RDTC optimized compression framework. In addition, an experimental testbed for streaming of these RDTC optimally compressed image-based scene representations is described and the impact of client side caching is investigated. Our results show a significant reduction in user perceived delay for RDTC optimized streams in such a remote browsing environment compared to RD optimized or independently encoded scene representations.
Low-Complexity and Context-Aware Estimation of Spatial and Temporal Activity Parameters for Automotive Camera Rate Control

Rate control in video compression adjusts the encoding parameters to reach a certain target bitrate for the encoded video. State-of-the-art rate controllers for hybrid video coding typically employ content-dependent video bitrate models and video quality metrics (VQMs). To capture the content characteristics, temporal and spatial video activity measures are determined from the raw video using computationally complex algorithms that require access to the uncompressed source video. In automotive deployments, however, full access to the uncompressed source video and the internal functions of video encoders is typically not possible. As a remedy, in this paper, we present a low-complexity approach to estimate the temporal activity (TA) and spatial activity (SA) measures for videos that are captured by a front-facing camera of a vehicle, based on the context information of the vehicle. To this end, we exploit information about the dynamics of the vehicle and other vehicles in the field-of-view of the front-facing camera. We apply the estimated TA and SA values to a video bitrate model and an objective VQM and use these models to solve the rate control problem to determine the optimal encoding settings for given bitrate constraints. The proposed low-complexity solution offers a similar accuracy in achieving rate constraints and similar perceptual quality characteristics as a solution that uses the computed TA and SA values, with the advantage that no access to the uncompressed source video stream or the internal functions of the video encoder is required.
Recent advances in mobile video communications

Mobile channels cannot provide guaranteed quality of service parameters. We illustrate this problem with a ray tracing simulation of an indoor DECT channel. Unreliable transmission poses severe problems for the transmission of motion video, using compression schemes such as described in the ITU-T H.263 international standard due to temporal error propagation. We discuss compatible extensions of H.263 that utilize a feedback channel for robust transmission. In conjunction with FEC and ARQ, an intelligent source coder control can provide excellent robustness at bit error rates worse than 10/sup -2/.
Model-mediated teleoperation for movable objects: dynamics modeling and packet rate reduction

We study Model-Mediated Teleoperation (MMT) for objects which can be moved and rotated on a planar surface. In MMT, a simple object model is employed on the master side to approximate the remote environment. The haptic feedback is rendered locally on the master side based on the model whose parameters are estimated on the slave side. Since the haptic control loop is running locally without noticeable delay, system stability can be guaranteed in the presence of arbitrary communication delays. The main challenges for MMT are environment modeling and online parameter identification. This work has two contributions. Firstly, we describe how to model movable objects with approximately known model geometry for MMT systems. The object dynamics including environment damping as well as the friction are modeled in real time during teleoperation. Secondly, we propose a hybrid deadband-based and time-triggered updating scheme to adaptively reduce the required packet rate while transmitting the estimated model parameters from the slave to the master. According to our experiments, the movable object can be accurately modeled in real time. In addition, compared to the conventional deadband-based updating scheme, our proposed hybrid updating scheme shows improvements in both model accuracy and packet rate reduction.
A realtime streaming architecture for in-car multimedia: Design guidelines and prototypical implementation

In this paper we propose a novel in-car realtime streaming multimedia architecture. This architecture is based on the Ethernet protocol combined with a single audio/video compression format that is valid throughout the car. Audio/video content deviating from the in-car format is transcoded by a central transcoding unit for delivery in the car. In this context, we compare three different transcoding approaches which differ in their design and performance. In the first scheme a cascaded transcoding unit consisting of a full decoder and a full encoder is studied. In the second approach, the transcoder reuses the motion information extracted from the incoming video stream. The motion information is either adopted without modification or serves for the initialization of a low-complexity motion search process in the encoder. The third transcoder investigated in this work reuses both motion information as well as the encoded residual data (DCT coefficients). All three trancoders have been implemented in an in-car communication prototype. We investigate the performance of the three transcoders by means of their complexity and their rate-distortion (RD) performance. From our experimental results we conclude that the transcoder which reuses both motion vectors and DCT coefficients shows the best trade-off between complexity and RD performance.
A Novel Network Architecture for In-Vehicle Audio and Video Communication

In-vehicle audio/video communication has recently received considerable attention in the automotive industry. First, because of the increasing number of audio/video oriented nomadic devices in the consumer electronic market and the requirement to integrate them in the car. Second, because the number of real-time audio/video based driver assistance systems is growing fast. So far, the interconnection of in-vehicle stream oriented devices has been performed by different and proprietary, automotive oriented network systems with limited transmission capacities. Ethernet provides good conditions for audio/video streaming, due to its high transmission capacity. But it unfortunately fails in providing adequate QoS. In this work, a new network architecture for in-car audio/video communication based on the Ethernet technology is introduced and solutions are proposed to extend Ethernet with QoS guarantees while keeping its cost low.
Delay Compensation for Actuated Stereoscopic 360 Degree Telepresence Systems with Probabilistic Head Motion Prediction

Communication delay is a major challenge for the acceptance of telepresence applications. It is particularly critical when the user experiences the remote environment via a Head-Mounted-Display. The lag between head motion and display response results in motion sickness, indisposition, and, at worst, abortion of the telepresence session. In this paper, we propose a delay compensation approach for 3D 360° telepresence systems realized with a mechanically actuated stereoscopic vision system. We further introduce a novel metric to evaluate the achievable level of delay compensation. We investigate state-of-the-art head motion predictors and propose a novel probabilistic prediction paradigm, which can half the mean prediction error and improve the level of delay compensation by up to 26%. The general validity of our approach is shown by means of two independent real head motion datasets. The experimental results verify that average compensation rates of more than 99% can be achieved for communication delays between 100-500ms.
Context-Aware Task Migration for HART-Centric Collaboration over FiWi Based Tactile Internet Infrastructures

Low task execution time and low energy consumption of collaborating mobile human users and robots are important requirements of emerging human-agent-robot teamwork (HART)-centric Tactile Internet applications. In particular, task migration among mobile HART members has emerged as an important research topic, taking different task types, task deadlines, collaborative node capabilities, and mobility patterns into account. We propose a context-aware task migration scheme for efficiently orchestrating the real-time collaboration among human mobile users, central and decentralized computational agents (cloud/cloudlets), and collaborative robots (cobots) across converged fiber-wireless (FiWi) communications infrastructures. We investigate the problem of whether and, if so, when and where a HART-centric task should be best migrated to. For resource-efficient task execution, the migration decision is made according to given task processing capabilities of cloud/cloudlet agents and cobots, task execution deadline, energy consumption of involved cobots and mobile devices, and task migration latency. We evaluate the performance of our context-aware HART-centric task migration scheme and compare it to conventional task execution without migration. Towards this end, we develop an analytical framework for quantifying its performance in terms of a variety of task migration key performance metrics, including task migration gain-overhead ratio, deadline-miss ratio, task response time, and energy consumption efficiency.
Towards location recognition using range images

Retrieving the location of a mobile device by matching a query image to a database of geo-tagged imagery is one popular application of content-based image retrieval (CBIR). Standard CBIR-based approaches exploit appearance features of the environment for the matching process. Many locations, however, are characterized by distinct structural (geometric) features. We investigate whether a standard appearance-based CBIR pipeline can be adapted to perform location retrieval using a range image-based representation of the environment. The contributions are three-fold: We design a rigorous experimental setup using an extensive and challenging indoor dataset. Secondly, we compare the state-of-the-art feature algorithm specifically designed for range images, the Normal Aligned Radial Feature (NARF) , against some of the most established appearance-based features. Thirdly, we combine the high key point detection rate of NARF, with the robustness of the Speeded-Up Robust Feature for range-image based location recognition. This detector-descriptor combination, which we coin NURF, leads to 15% improvement in absolute location recognition performance compared to simple NARF in our experimental setup.
Bit rate estimation for H.264/AVC video encoding based on temporal and spatial activities

We present a novel bit rate model for H.264/AVC video encoding which is based on the quantization parameter, the frame rate as well as temporal and spatial activity measures. With the proposed model, it is possible to trade-off the frame rate versus the quantization parameter to achieve a target bit rate. Our model depends on video activity measures that can be easily calculated from the uncompressed video. In our experiments, the model achieves a Pearson correlation of 0.99 and a root-mean-square error of less than 5% with the measured bit rate values, as verified by statistical analysis.
An image-domain cost function for 3-D rigid body motion estimation

We derive an image-domain cost function for calibrated 3-D rigid body motion estimation from two views. The cost function is based on the accumulation of minimum mean squared error values along the epipolar lines for a large number of small measurement windows. In comparison to other non-linear, but feature-based formulations, the minimization of this cost function does not require pre-computed point correspondences. Several speedups are derived that keep the computational complexity similar to other non-linear formulations of the 3-D rigid body motion estimation problem. The main advantage of the proposed technique is that no effort has to be devoted to the reliable determination of point correspondences.
Energy Prediction for Teleoperation Systems That Combine the Time Domain Passivity Approach with Perceptual Deadband-Based Haptic Data Reduction

We study the combination of the perceptual deadband (PD)-based haptic packet rate reduction scheme with the time domain passivity approach (TDPA) for time-delayed teleoperation and propose a novel energy prediction (EP) scheme that deals with the conservative behavior of the resulting controller. The PD approach leads to irregular packet transmission, resulting in degraded system transparency and reduced teleoperation quality when the PD approach is combined with the TDPA. The proposed method (PD+TDPA+EP) adaptively predicts the system energy during communication interruptions and allows for larger energy output. This achieves less conservative control and improves the teleoperation quality. Evaluation of the displayed impedance shows that the PD+TDPA+EP method achieves improved system transparency, both objectively and subjectively, when compared with related approaches from literature. According to a subjective user study, the PD+TDPA+EP method allows for a high packet rate reduction (up to 80 percent) without noticeably distorting the perceived interaction quality. We also show that the PD+TDPA+EP method is preferred over related approaches from literature in a direct comparison test. Thus, with the proposed PD+TDPA+EP method, a high data reduction and a high teleoperation quality are simultaneously achieved for time-delayed teleoperation.