nils-herrmann/clean_scirepeval huggingface dataset:
A theoretical analysis of the RDTC space

Remote navigation in image-based scene representations requires random access to the compressed reference image data to compose virtual views. When using block-based hybrid video coding concepts, the degree of inter frame dependencies introduced during compression has an impact on the effort that is required to access reference image data and at the same time delimits the rate distortion trade-off that can be achieved. If, additionally, a maximum available channel bitrate is taken into account, the traditional rate-distortion (RD) trade-off can be extended to a trade-off between the storage rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). In this work we present a theoretical analysis of this RDTC space. Experimental results qualitatively match those predicted by theory and show that an adaptation of the encoding process to scenario specific parameters like computational power of the receiver and channel throughput can significantly reduce the user perceived delay or required storage for RDTC optimized streams compared to RD optimized or independently encoded scene representations.
Synchronization of presentation slides and lecture videos using bit rate sequences

The temporal synchronization of presentation slides and lecture videos enables us to enhance the user experience of online lecture viewing systems. In this work, we present a novel approach to robustly and reliably detect and recognize slide changes, which is based on bit rate sequences. By exploiting readily available information, the computational complexity of the approach is very low. In contrast to prior work, no requirements on the amount or type of texture, motion of foreground objects, or text size on the slide are imposed. Experimental results show the ability to detect even minor slide changes and demonstrate the robustness against occlusions, foreground motion, and camera motion.
A Novel Rate Control Framework for SIFT/SURF Feature Preservation in H.264/AVC Video Compression

This paper presents a novel rate control framework for H.264/Advanced Video Coding-based video coding that improves the preservation of gradient-based features like scale-invariant feature transform or speeded up robust feature compared with the default rate control algorithm in the JM reference software. First, a criterion (matching score) for feature preservation on the basis of the bag-of-features concept is proposed. Then, the matching scores are collected as a function of the quantization parameters and analyzed for different feature types. With this analysis, macroblocks are categorized into different groups before encoding. Our rate control algorithm assigns different quantization parameters to each group according to the importance of the group for feature extraction. The experimental results show that our rate control algorithm achieves the desired target bit rate, and more features are preserved compared with videos encoded using the default rate control. The proposed approach not only improves feature preservation, but also leads to a noticeable performance improvement in a real image retrieval system. The rate control framework proposed in this paper is fully standard compatible.
Analysis of the Decoding-Complexity of Compressed Image-Based Scene Representations

Interactive navigation in image-based scenes requires random access to the compressed reference image data. When using state of the art block-based hybrid video coding techniques, the degree of inter and intra block dependencies introduced during compression has an impact on the effort required to access reference image data and therefore delimits the response time for interactive applications. In this work a theoretical model for the decoding-complexity of compressed image-based scene representations is presented and evaluated. Results show the validity of the model. Additionally, results for decoding-complexity constrained rate distortion optimization (RDC) using our model show the benefit of incorporating the computational power of client devices into the compression process.
Modeling the bit rate of H.264/AVC video encoding as a function of quantization parameter, frame rate and GoP characteristics

We present a novel analytical bit rate model for H.264/AVC video encoding which is based on the quantization parameter, the frame rate, the group-of-pictures length, the group-of-pictures structure and content-dependent parameters. The content-dependent parameters are described as a function of two standard video activity measures (temporal and spatial activity) which can easily be determined from the uncompressed video. Our bit rate model shows a Pearson correlation of more than 0.97 and a root-mean-square error of less than 2% as determined by statistical analysis.
Video synchronization using bit rate profiles

We present a novel approach for the temporal synchronization of multiple videos which is based on cross-correlating bit rate profiles. The proposed scheme determines the temporal offset without major restrictions on viewing angles, camera properties and camera motion. We propose two extensions of the basic algorithm which reduce the influence of camera motion and distracting background objects. Additionally, we describe how to optimally combine different bit rate components in order to further improve the reliability of the synchronization scheme. The proposed approach, when combined with the three extensions, leads to a reliable, robust, and frame accurate temporal alignment of videos at remarkably low complexity.
Opening the haptic loop: Network degradation limits for haptic task performance

For realtime teleoperation with haptic feedback, network-induced artifacts like delay, packet loss and lossy coding of haptic data deteriorate the operability of the system. We provide a systematic quantification of the limits within which, these network-induced degradations are tolerable for human task performance in executing a given haptic task. These limits are conservative in the sense that we do not account for any stabilizing approaches from control engineering to counter the impact of the network-induced degradations. Through experimental evaluation with a pursuit tracking task, we show that task performance is most affected by delay on the network and a strong lossy coding scheme. With statistical analysis we show that task performance is significantly decreased for one-way delay higher than 14 ms and strong lossy coding with a deadband parameter higher than 27 %. For the given task, packet loss is found not to affect task performance significantly.
Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder

Acceleration signals captured during the interaction of a rigid tool with an object surface carry relevant information for surface material classification. Existing methods mostly rely on carefully designed perception-related features or features adapted from audio processing motivated by the observed similarity between acceleration signals and audio signals. In contrast, our proposed method automatically learns features from RAW acceleration data without preprocessing. The approach is based on Convolutional Neural Networks (CNN) trained and tested on RAW data. For better performance and faster convergence of the CNN, we use the weights of a trained sparse Autoencoder (AE) to initialize the weights of the first convolution layers of the CNN. This strategy is named CNN pretrained by sparse AE (ACNN). Our classification results on a publically available Haptic Texture Database demonstrate that the proposed algorithm performs favorably against existing methods.
A scalable virtual programmable real-time testbed for rapid multimedia service creation and evaluation

In this paper we describe a new flexible and universally applicable testbed for Internet-based multimedia applications. Our approach combines the technology of programmable networks with emulated network environments and delivers a virtual programmable testbed (VPT). In this work we show that a VPT is well suited for rapid creation and evaluation of distributed multimedia applications. There are three advantages of our approach compared to previously proposed testbed solutions: first, the software for processing IP datagrams, so called atomic modules, only depends on the programmable platform deployed, making it reusable. Second, loading several atomic modules concurrently allows the easy setup of customized test environments, e.g., channel models. Third, the application being developed can be tested under various network conditions (failures, congestion, etc.) at an early stage. Based on our VPT, distributed multimedia services can be created and evaluated both fast and economically in emulated environments without expensive and complex hardware setup.
Combining Contact Models With Perceptual Data Reduction for Efficient Haptic Data Communication in Networked VEs

In cooperative networked virtual environments, haptic rendering enables joint haptic interaction with virtual objects and, thus, shared touch experiences among multiple users. In case of an underlying packet-based communication network (e.g., the Internet), minimizing the end-to-end delay, with the goal of preventing instability of the involved control loops, results in high packet rates. Previously proposed perceptual data reduction approaches address this challenge and satisfy the strict delay constraints. However, significantly increased packet rates still occur during contact events. We present a novel event-based coding scheme based on a distributed haptic-rendering framework that integrates model-based distributed haptic rendering with perceptual data reduction. We also present a comprehensive haptic contact model for signals with multiple degrees of freedom. Furthermore, we show that the integration of event-triggered force transient models from event-based haptics into our local contact model is instrumental for generating convincing haptic feedback. Psychophysical experiments reveal that the approach presented herein allows us to push the data reduction performance beyond what is normally achievable by perceptual data reduction schemes alone while significantly improving the quality of haptic contact feedback.
Traffic Shaping for Resource-Efficient In-Vehicle Communication

In-vehicle communication has become complex and costly due to the growing number of automotive network systems applied for different data types. In this work, our previously proposed in-vehicle network architecture that is based on Internet protocol (IP) and full-duplex switched Ethernet (IP/Ethernet) is further investigated for real-time audio and video streaming. Quality-of-service (QoS) and resource usage are analyzed for selected IP/Ethernet-based network topologies. Traffic shaping is used to reduce the required network resources and consequently the cost. A novel traffic shaping algorithm is presented that outperforms other traffic shapers in terms of resource usage when applied to variable bit rate video sources in the proposed double star topology. In addition, a new architecture design is introduced for traffic shaper implementation in switches which operates on a per stream basis. Analytical and simulation results confirm that the proposed network architecture with traffic shaping is well-adapted for in-vehicle communication.
Geometry refinement for light field compression

In geometry-aided light field compression, a geometry model is used for disparity-compensated prediction of light field images from already encoded light field images. This geometry model, however, may have limited accuracy. We present an algorithm that refines a geometry model to improve the overall light field compression efficiency. This algorithm uses an optical-flow technique to explicitly minimize the disparity-compensated prediction error. Results from experiments performed on both real and synthetic data sets show bit-rate reductions of approximately 10% using the improved geometry model over a silhouette-reconstructed geometry model.
Point Cloud-Based Model-Mediated Teleoperation With Dynamic and Perception-Based Model Updating

In this paper, we extend the concept of model-mediated teleoperation for complex environments and six degrees of freedom interaction using point cloud surface models. In our system, a time-of-flight camera is used to capture a high-resolution point cloud model of the object surface. The point cloud model and the physical properties of the object (stiffness and surface friction coefficient) are estimated at the slave side in real-time and transmitted to the master side using the modeling and updating algorithm proposed in this paper. The proposed algorithm adaptively controls the updating of the point cloud model and the object properties according to the slave movements and by exploiting known limitations of human haptic perception. As a result, perceptually irrelevant transmissions are avoided, and thus the packet rate in the communication channel is substantially reduced. In addition, a simple point cloud-based haptic rendering algorithm is adopted to generate the force feedback signals directly from the point cloud model without first converting it into a 3-D mesh. In the experimental evaluation, the system stability and transparency are verified in the presence of a round-trip communication delay of up to 1000 ms. Furthermore, by exploiting the limits of human haptic perception, the presented system allows for a significant haptic data reduction of about 90% for teleoperation systems with time delay.
Proxy Caching for Video-on-Demand Using Flexible Starting Point Selection

In this paper, we propose a novel proxy caching scheme for video-on-demand (VoD) services. Our approach is based on the observation that streaming video users searching for some specific content or scene pay most attention to the initial delay, while a small shift of the starting point is acceptable. We present results from subjective VoD tests that relate waiting time and starting point deviation to user satisfaction. Based on this relationship as well as the dynamically changing popularity of video segments, we propose an efficient segment-based caching algorithm, which maximizes the user satisfaction by trading off between the initial delay and the deviation of starting point. Our caching scheme supports interactive video cassette recorder (VCR) functionalities and enables cache replacement with a much finer granularity compared to previously proposed segment-based approaches. Our experimental results show a significantly improved user satisfaction for our scheme compared to conventional caching schemes.
Camera context based estimation of spatial and temporal activity parameters for video quality metrics in automotive applications

We present a low complexity approach for the estimation of the temporal and spatial activity parameters of videos which are captured by a front-facing camera of a vehicle based on context information of the vehicle. The estimated parameters are integrated into an objective video quality metric, which can be used to determine the perceptual quality of a compressed video stream. Our proposed video quality metric has very low computational complexity, which makes it suitable for live video streaming applications. It shows a high Pearson correlation of 0.98 with an average root-mean-square error of 6%, as verified by statistical analysis with data from subjective tests.
Long-term memory prediction using affine motion compensation

Long-term memory prediction extends motion compensation from the previous frame to several past frames with the result of increased coding efficiency. We demonstrate that combining long-term memory prediction with affine motion compensation leads to further coding gains. For that, various affine motion parameter sets are estimated between frames in the long-term memory buffer and the current frame. Motion compensation is conducted using standard block matching in the multiple reference frame buffer. The picture reference and the affine motion parameters are transmitted as side information. The technique is embedded into a hybrid video coder mainly following the H.263 standard. The coder control employs Lagrangian optimization for the motion estimation and macroblock mode decision. Significant bit-rate savings between 20 and 50% are achieved for the sequences tested over TMN-10 the test model of H.263+. These bit-rate savings correspond to gains in PSNR between 0.8 and 3 dB.
Disposal of explosive ordnances by use of a bimanual haptic telepresence system

The paper outlines a novel approach for performing explosive ordnance disposal by use of a bimanual haptic telepresence system. This system enables an operator to perceive multimodal feedback from a remote environment for proper task execution. The developed experimental setup, comprising a bimanual human system interface and the corresponding bimanual teleoperator for use of both hands, is presented in detail. The teleoperation control architecture is discussed as well as a local model-based impedance control algorithm for manipulator control. Human-system performance is improved by means of stereo visualization of the tele-environment together with overlayed Augmented Reality assistance, and an algorithm for avoidance of dangerous manipulator configurations supported by augmented force feedback. Furthermore, a recently developed UDP communication library is presented for system interconnection taking into account compression of haptic data. Thus efficient and low delay of data transfer is ensured. The usability and effectiveness of the developed bimanual telepresence system are demonstrated by focusing a relevant task scenario, such as demining operations in a remote environment.
Fully Automatic and Frame-Accurate Video Synchronization Using Bitrate Sequences

Video synchronization is an essential processing step in many multimedia applications, and various methods have been proposed in the literature each of which addresses the problem from a different point of vantage. In this article, we present an information theoretic approach to video synchronization, based on the state-of-the-art in hybrid video coding. Time series derived from the videos' instantaneous bitrate demand are correlated in a robust manner employing the recently published ConCor algorithm. We enhance ConCor with integrated normalization capabilities in order to improve its shape-oriented matching performance. Furthermore, we present a mathematical framework to derive the most suitable ConCor parameters given a specific class of input videos. In an extensive experimental analysis, we give an insight into the representation of synchronization-relevant scene changes with bitrate data, and examine the influence of encoding parameters on the synchronization performance. Experiments on diverse video input substantiate the reliable performance of our easy to implement, yet effective video synchronization algorithm which distinguishes itself in that it operates largely without manual intervention.
Influence of Image/Video Compression on Night Vision Based Pedestrian Detection in an Automotive Application

With the introduction of IP/Ethernet networks in automobiles, video compression becomes mandatory for efficient video transfer. Lossy image and video compression standards, such as JPEG and H.264/AVC, achieve high compression efficiency, but also introduce irreversible modification to the video data. On the one hand, this can lead to perceivable image degradation, and on the other hand, affect the performance of image processing algorithms. The pedestrian detection application based on night vision technology, is one example of a driver assistance service using a video stream as input. In this paper, we examine the impact of image/video compression on pedestrian detection, which employs a far infrared (FIR) sensor. In order to measure application quality, metrics used for driver assistance system validation, are investigated through hardware in the loop (HIL) simulation.
A real-time Internet streaming media testbed

We describe a real-time LAN-based testbed that allows us to investigate the behavior of streaming media applications under various network conditions. For commercially available streaming media applications, we are interested to see how they perform over next-generation wireline and wireless networks. For future streaming media applications, the testbed is an invaluable tool for the development and verification of new algorithms. Our testbed implementation is based on Linux Divert Sockets and supports a straightforward integration of various packet erasure and delay models. Individual IP-packets are diverted to a user process where they are delayed or deleted according to the desired channel model. The testbed has been used to investigate the flow-control behavior of existing streaming media systems over wireless networks. Our experiments confirm that flow-control algorithms that consider lost packets to be the result of network congestion, as employed today in wireline streaming, are not suited for wireless networks, where loss is mainly due to link impairments.
Error Detection Capabilities of Automotive Network Technologies and Ethernet - A Comparative Study

Coding the payload data and sending the codeword as an overhead of the packet is a very common way to protect data in communication networks. The protection level of the coding technique is chosen depending on the importance of the transmitted data. While high priority, safety critical applications do not tolerate any single bit error in data packets, lower priority services can handle few bit errors. Coding techniques provide error detection and up to a certain level also error correction. Bit errors that are not detectable by coding techniques at the receiver side occur with a residual error probability. The better the coding technique is, the lower is its residual error probability for different bit error rates. Most automotive network systems use cyclic redundancy codes (CRC) mainly in order to detect transmission bit errors. Instead of correcting the identified bit errors which is quite time consuming, usually a retransmission of the damaged data packet is triggered. Similar to automotive network systems, Ethernet, the most applied network technology in local area networks uses the CRC error detection technique. In this work, we present a comparative study of the error detection capabilities of automotive network systems and Ethernet as a possible network system for time critical applications in the car. We evaluate the related residual error probabilities for a reasonable range of bit error rates. Furthermore, several commercial concepts are presented from the automation field that increase the error detection capability of the standard Ethernet technology significantly.