nils-herrmann/clean_scirepeval huggingface dataset:
Content-based surface material retrieval

We present a content-based surface material retrieval (CBSMR) system for tool-mediated freehand surface exploration that relies on features motivated by the main psychophysical dimensions of tactile surface texture perception. The proposed approach does not require explicit scan force and scan velocity measurements. The perceptual features used in our CBSMR engine cover the tactile dimensions of friction, hardness, macroscopic roughness, microscopic roughness and warmth. We examine 108 surface materials recorded by different users and present the results of a free-sorting grouping experiment with 30 subjects which we conducted to determine the perceptual similarity of the surface materials in our database, providing a ground truth data set for perceived tactile similarity. The outcome of this experiment is used to demonstrate that the proposed CBSMR engine is able to determine the perceptually most similar surface materials for a test query. The proposed set of 8 features leads to a classification precision of 86% and a similarity precision-at-one of 30% when combined with a Euclidean Distance-based classifier.
Adaptive multi-source video multicast

We introduce an adaptive rate control scheme for multi-source video streaming in peer-to-peer (P2P) networks. The rate control scheme is applied in the context of a novel multi-source video multicast framework, where each source distributes its own video sequence while additionally forwarding video data received from other sources. Multiple videos are distributed to all requesting peers exploiting full collaboration between the sources, the requesting peers and helper peers. The rate adaptation is jointly decided for all sources based on their upload capacities and the required video quality. Our goal is to obtain either dasiasame throughputpsila or dasiasame video qualitypsila for all streams. To achieve the same video quality for all streams, we use scalable video coding techniques. Our evaluation made on PlanetLab shows that our solution achieves a better video quality or throughput adaptation compared to independent rate allocation performed at each source.
Robust H.263 compatible transmission for mobile video server access

For mobile video server access, precoded bitstreams have to be transmitted at very low bitrates over error-prone channels. In this paper, we investigate an H.263 compatible approach to robust transmission of stored video bit-streams that is based on a feedback channel. As a side product, our method allows random access without the overhead of periodic I-frames. Results are presented for a data-rate of 25 kbps and and QCIF resolution (176/spl times/144 pel).
3-D image models and compression: synthetic hybrid or natural fit?

This paper highlights recent advances in image compression aided by 3-D geometry information. As two examples, we present a model-aided video coder for efficient compression of head-and-shoulder scenes and a geometry-aided coder for 4-D light fields for image-based rendering. Both examples illustrate that an explicit representation of 3-D geometry is advantageous if many views of the same 3-D object or scene have to be encoded. Waveform-coding and 3-D model-based coding can be combined in a rate-distortion framework, such that the generality of waveform coding and the efficiency of 3-D models are available where needed.
Adaptive media playout for low-delay video streaming over error-prone channels

When media is streamed over best-effort networks, media data is buffered at the client to protect against playout interruptions due to packet losses and random delays. While the likelihood of an interruption decreases as more data is buffered, the latency that is introduced increases. In this paper we show how adaptive media playout (AMP), the variation of the playout speed of media frames depending on channel conditions, allows the client to buffer less data, thus introducing less delay, for a given buffer underflow probability. We proceed by defining models for the streaming media system and the random, lossy, packet delivery channel. Our streaming system model buffers media at the client, and combats packet losses with deadline-constrained automatic repeat request (ARQ). For the channel, we define a two-state Markov model that features state-dependent packet loss probability. Using the models, we develop a Markov chain analysis to examine the tradeoff between buffer underflow probability and latency for AMP-augmented video streaming. The results of the analysis, verified with simulation experiments, indicate that AMP can greatly improve the tradeoff, allowing reduced latencies for a given buffer underflow probability.
Multiple Description Video Transcoding

In this paper we introduce the concept of multiple description video transcoding (MDVT). MDVT converts a single description encoded video into two or more descriptions at an intermediate node in the network. The objective of our MDVT approach is to adapt the video transmission to a multi-radio environment where two or more independent transmission paths exist between the intermediate node and the receiver. The sender does not have to be aware of the transcoding process and the multi-path transmission. MDVT can for instance be applied for multi-mode terminals that are simultaneously connected to two wireless access technologies, e.g., UMTS and WLAN. We compare MDVT with multiple description coding at the sender (MDC-S) as well as with MDC at the intermediate node (MDC-I) where the incoming single description video is decoded and re-encoded into multiple descriptions and the transmission is optimized separately for each path. We present a fast greedy method that can be used to perform multiple description video transcoding in real-time at low complexity. Our experimental results show that we can achieve performance similar to MDC-S where the sender has to be aware of the availability of multiple paths. Compared to MDC-I we observe more than 2 dB gain in reconstruction quality.
Deep Learning for Surface Material Classification Using Haptic and Visual Information

When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface material properties. More importantly, such haptic acceleration signals can be used together with surface images to jointly recognize the surface material. In this paper, we present a novel deep learning method dealing with the surface material classification problem based on a fully convolutional network, which takes the aforementioned acceleration signal and a corresponding image of the surface texture as inputs. Compared to the existing surface material classification solutions which rely on a careful design of hand-crafted features, our method automatically extracts discriminative features utilizing advanced deep learning methodologies. Experiments performed on the TUM surface material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.
Perception-Based Data Reduction for Haptic Force-Feedback Signals Using Velocity-Adaptive Deadbands

In telepresence and teleaction (TPTA) systems, the transmission of haptic signals puts high demands on the applied signal processing and communication procedures. When running a TPTA session across a packet-based communication network (e.g., the Internet), minimizing the end-to-end delay results in packet rates of up to the applied sampling rate of the local control loops at the human system interface and the teleoperator. The perceptual deadband data reduction approach for haptic signals successfully addresses the challenge of high packet rates in networked TPTA systems and satisfies the strict delay constraints. In this paper, we extend the underlying perceptual model of the deadband approach by incorporating psychophysical findings on human force-feedback discrimination during operators' relative hand movements. By applying velocity-dependent perception thresholds to the deadband approach, we observe further improvement in efficiency and performance due to improved adaption to human haptic perception thresholds. The psychophysical experiments conducted reveal improved data reduction performance of our proposed haptic perceptual coding scheme without impairing the user experience. Our results show a high data reduction ability of up to 96 without affecting system transparency or the operator's task performance.
Smooth object state updates in distributed haptic virtual environments

In a multi-user distributed haptic virtual environment (DHVE), object state update messages are necessary to maintain a consistent virtual environment at every entity involved. Traffic control schemes applied to reduce the usually high number of update packets lead to inconsistencies among the participants. Whenever a new state update is received, the discrepancy between the old and new object state has to be resolved. For this purpose, various interpolation methods can be used to smoothly correct the state error. In this paper, we investigate and compare three such methods using subjective experiments to determine a haptically preferred method that leads to a convincing force feedback. Besides basic linear interpolation, we test two other methods from literature which are based on cubic polynomials. Our experimental evaluation, based on a pursuit-tracking task and pairwise comparisons, shows that linear interpolation performs better than the two cubic approaches and is subjectively preferred.
QoE-driven resource optimization for user generated video content in next generation mobile networks

The increasing popularity of user-generated content and the high quality upstreaming capabilities of mobile phones indicate a prevalence of video traffic in the uplink of next generation mobile networks. Need arises for optimizing the network resource allocation while preserving the user satisfaction. In this paper, we propose a service-centric approach for uplink distribution of real-time user-generated content based on the Quality of Experience (QoE) and popularity of the video content. In case of limited network resources, the proposed approach assigns more resources for popular contents while maintaining a minimum guaranteed QoE for the less popular ones. We compare our service-centric approach with a QoE-driven one that does not consider video popularity and evaluate both approaches for the uplink of an LTE system. The simulation results show that a significant gain in terms of average user satisfaction can be achieved.
Cross-layer optimization for wireless video streaming-performance and cost

Cross-layer design (CLD) is a new paradigm for network architecture that allows us to make better use of network resources by optimizing across the boundaries of traditional network layers. Previous work has shown that applying CLD to mobile multimedia communication systems may lead to significant performance improvements. In this paper we also consider the other side of the coin, i.e., the additional computation and communication overhead introduced by CLD. We evaluate the performance improvements and the cost of cross-layer optimization using a wireless multi-user video streaming example.
Haptic Communications

Audiovisual communications is at the core of multimedia systems that allow users to interact across distances. It is common understanding that both audio and video are required for high-quality interaction. While audiovisual information provides a user with a satisfactory impression of being present in a remote environment, physical interaction and manipulation is not supported. True immersion into a distant environment and efficient distributed collaboration require the ability to physically interact with remote objects and to literally get in touch with other people. Touching and manipulating objects remotely becomes possible if we augment traditional audiovisual communications by the haptic modality. Haptic communications is a relatively young field of research that has the potential to substantially improve human-human and human-machine interaction. In this paper, we discuss the state-of-the-art in haptic communications both from psychophysical and technical points of view. From a human perception point of view, we mainly focus on the multimodal integration of video and haptics and the improved performance that can be achieved when combining them. We also discuss how the human adapts to discrepancies and synchronization errors between different modalities, a research area which is typically referred to as perceptual learning. From a technical perspective, we address perceptual coding of haptic information and the transmission of haptic data streams over resource-constrained and potentially lossy networks in the presence of unpredictable and time-varying communication delays. In this context, we also discuss the need for objective quality metrics for haptic communication. Throughout the paper, we stress the fact that haptic communications is not meant as a replacement of traditional audiovisual communications but rather as an additional dimension for telepresence that will allow us to advance in our quest for truly immersive communication.
Bottom-up approach to cross-layer design for video transmission over wireless channels

In this paper, a bottom-up approach to cross-layer design is proposed which is based on the partitioning of the optimized parameters into private and interfacing parameters. The optimization is divided into an intra-layer optimization of private parameters and an inter-layer optimization of interfacing parameters. This concept keeps as much of the optimization local to the layers as possible and thereby reduces the amount of additional information exchange and associated delay times caused by cross-layer design. Intra- and inter-layer optimization are illustrated for a simplified multi-user wireless communication system which offers a streaming video service application to its mobile clients.
Low-delay compression of polygon mesh deformation data for remote haptic interaction with simulated deformable objects

This paper addresses the communication of polygon mesh deformation data for remote haptic interaction with simulated deformable objects. In the studied client-server architecture, the server computes the resource-demanding finite-element-based object deformation at a low temporal update rate and transmits the resulting polygon mesh deformation data to the clients. There, the received deformation data is used to locally render the virtual environment visually and haptically at the required rates of 30 Hz and 1 kHz, respectively. We propose a low-delay lossy compression scheme for the transmission of the 3D polygon mesh deformation data, which considers the haptic and visual modalities individually. Objective and subjective evaluations show that the proposed compression scheme achieves a compression ratio of 11:1, while keeping the introduced visual and haptic distortion below the respective human perception thresholds.
Reconstruction of transparent objects in unstructured scenes with a depth camera

The visual 3D reconstruction of transparent objects in unstructured scenes is challenging due to the complex image formation principles underlying their visual appearance. Most state-of-the-art reconstruction methods ignore this problem and assume Lambertian reflection. Yet, transparent objects are relevant scene information for applications in intelligent robotics (such as grasping) or virtual reality. In this work, we present an approach to detect non-planar transparent objects, like bottles or glasses, by specifically searching for geometry inconsistencies caused by refraction or reflection. Depth information is acquired using a Kinect sensor, which is moved within the scene in order to acquire multiple views. The individual measurements are combined into a 3D volume, yielding the objects' location and a rough shape estimate. Results are presented using various household objects made of glass or plastic.
Cross layer optimization for wireless multi-user video streaming

A cross-layer optimization concept for wireless multi-user video streaming is proposed. We describe a cross layer optimizer that interfaces the video streaming application and the radio link layer by means of parameter abstraction. The optimizer maximizes the end-to-end quality of the video streaming service jointly for all users while efficiently using the wireless resources. Our simulation results for video streaming in a multi-user environment show the performance improvements achievable with this concept. We demonstrate that even for a small number of users and a small number of degrees of freedom in the optimization significant quality improvements can be obtained.
3-D reconstruction of real-world objects using extended voxels

In this paper we present a voxel-based 3-D reconstruction technique that computes a set of non-transparent object surface voxels from a given set of calibrated camera views. We show that the quality of the reconstruction strongly depends on the accuracy of the computed voxel projection in the image plane and discuss different approximations of the exact projection. The most simple and computationally least demanding approximation is obtained when projecting point voxels, i.e., voxels without spatial extent. However, correct occlusion handling is not possible for point voxel volumes leading to reconstruction artifacts. The most accurate projection is obtained by computing the exact outline of the projected voxels. This projection is computationally most demanding but allows correct occlusion handling during reconstruction. Experimental results that compare the reconstruction quality for point and exact voxel projection show that it is worthwhile computing the tract image plane footprint of the projected voxels.
Robust WiFi-based indoor localization using multipath component analysis

The number of applications that rely on robust indoor localization is constantly growing. Conventional outdoor localization technologies are generally not suited for indoor use. WiFi-based indoor localization systems are a widely studied alternative since the necessary infrastructure is already available in most buildings. Existing WiFi-based localization algorithms, however, still face challenges such as high sensitivity to changes in the environment, temporal instability, a time consuming calibration process and low accuracy in non-line-of-sight (NLOS) multipath environments. In this paper, we propose a novel fingerprinting-based WiFi indoor localization scheme which operates by extracting and analyzing individual multipath propagation delays. We demonstrate through simulation the robustness of the proposed algorithm to changes in the multipath environment.
A Novel Signal Reconstruction Algorithm for Perception Based Data Reduction in Haptic Signal Communication

The performance and immersiveness of telepresence and teleaction systems critically depend on the quality of the communication between the operator and the teleoperator. High packet rate for low delay data exchange in internet-based tele-operation is one of the major challenges in this context. A psychophysical^ motivated data reduction scheme, earlier presented by the authors, solves this problem by the use of signal amplitude dependent deadband quantization. This scheme successfully limits the amount of transmitted packets, but as soon as the size of the deadband exceeds a certain limit, noticeable and disturbing artifacts are introduced into the haptic signals. In this paper, we present a novel signal reconstruction technique, based on a signal adaptive synthesis filter. It removes discontinuities in the displayed signals and enables the use of strong deadband based data reduction without perceptible disturbance in the telepresence system.
Efficient Multi-Rate Video Encoding for HEVC-Based Adaptive HTTP Streaming

Adaptive HTTP streaming requires a video to be encoded at multiple representations, that is, different qualities. Encoding these multiple representations is a computationally complex process, especially when using the recent High Efficiency Video Coding (HEVC) standard. In this paper, we consider a multi-rate HEVC encoder and identify four types of encoding information that can be reused from a high-quality reference encoding to speed up lower quality-dependent encodings. We show that the encoding decisions from the reference cannot be directly reused, as this would harm the overall rate-distortion (RD) performance. Thus, we propose methods to use the encoding information to constrain the RD optimization of the dependent encodings so that the encoding complexity is reduced while the RD performance is kept high. We additionally show that the proposed methods can be combined, leading to an efficient multi-rate encoder that exhibits high RD performance and substantial complexity reduction. Results show that the encoding time for 12 representations at different spatial resolutions and signal qualities can be reduced on average by 38%, while the average bitrate increases by less than 1%.
Sequence-level models for distortion-rate behaviour of compressed video

In this paper, two empirical models for the sequence-level distortion-rate performance of predictive video source encoding are proposed. They require very limited amount of empirical data, namely three pairs of rate and distortion, in order to set up the model parameters. The advantages of these proposed models are the robustness towards measurement noise, low number of model parameters with a closed form setup solution, and high accuracy. Experimental validation using H.264/AVC encoded video test sequences reports high accuracy of these two proposed models.
Survey of Visual Feature Extraction Algorithms in a Mars-like Environment

This paper presents a performance comparison of several state-of-the-art visual feature extraction algorithms when applied in a poorly-structured environment as found on the planet Mars. So far, no systematic evaluation of feature extraction algorithms in extraterrestrial environments is available. The algorithms in this paper are evaluated using the Devon Island dataset which is said to have one of the most Mars-like environments on Earth. The ground truth for the performance comparison is based on a location retrieval task using the GPS data provided by the dataset. A range of common feature detection and description algorithms is covered including floating point and binary descriptors. With the aim of efficient image retrieval the descriptor vectors are quantized in a bag-of-words model using the k-means algorithm. The results show that the well-known SURF feature provides superior performance over other state-of-the-art feature extraction algorithms in a Mars-like environment.
Using multiple global motion models for improved block-based video coding

A novel motion representation and estimation scheme is presented that leads to improved coding efficiency of block-based video coders like, e.g., H.263. The proposed scheme is based on motion-compensated prediction from more than one reference frame. The reference pictures are warped or globally motion-compensated versions of the previous frame. Affine motion models are used as warping parameters approximating the motion vector field. Motion compensation is performed using standard block matching in the multiple reference frames buffer. The frame reference and the affine motion parameters are transmitted as side information. The approach is incorporated into an H.263-based video codec at minor syntax changes and embedded into a rate-constrained motion estimation and macroblock mode decision frame work. In contrast to conventional global motion compensation, where one motion model is transmitted, we show that multiple global motion models are of benefit in terms of coding efficiency. Significant coding gains in comparison to TMN-10, the test model of H.263+, are achieved that provide bit-rate savings between 20% and 35% for the various sequences tested.