nils-herrmann/clean_scirepeval huggingface dataset:
RDTC Optimized Compression of Image-Based Scene Representations (Part I): Modeling and Theoretical Analysis

Rendering of virtual views in interactive streaming of compressed image-based scene representations requires random access to arbitrary parts of the reference image data. The degree of interframe dependencies exploited during encoding has an impact on the transmission and decoding time and, at the same time, delimits the (storage) rate-distortion (RD) tradeoff that can be achieved. In this work, we extend the classical RD optimization approach using hybrid video coding concepts to a tradeoff between the storage rate (R), distortion (D), transmission data rate (T), and decoding complexity (C). We present a theoretical model for this RDTC space with a focus on the decoding complexity and, in addition, the impact of client side caching on the RDTC measures is considered and evaluated. Experimental results qualitatively match those predicted by our theoretical models 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.
Encoding Parameter Estimation for RDTC Optimized Compression and Streaming of Image-Based Scene Representations

Remote navigation in image-based scene representations requires random access to parts of the compressed reference image data to compose virtual views. The degree of 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 (RD) tradeoff that can be achieved. If a limited channel bitrate and computational power of client devices are taken into account, encoding can be performed in a RD optimal manner with respect to the expected maximum transmission data rate (T) and decoding complexity (C). In this work we present a practical framework for parameter estimation for RDTC optimal encoding of image-based scene representations.
Image-based object detection under varying illumination in environments with specular surfaces

Image-based environment representations capture the appearance of the surroundings of a mobile robot and are useful for the detection of novelty. However, image-based novelty detection can be impaired by illumination effects. In this paper we present an approach for the image-based detection of novel objects in a scene under varying lighting conditions and in the presence of objects with specular surfaces. The computation of an illumination-invariant image-based environment representation allows for the extraction of the shading of the environment from camera images. Using statistical models infered from the luminance and the saturation component of the shading images, specularities and shadows are detected and suppressed in the process of novelty detection. Experimental results show that the proposed method outperforms two recently presented reference approaches for illumination-invariant change detection in images.
Perceptual coding of haptic data in time-delayed teleoperation

In telepresence and teleaction systems the haptic communication channel plays a central role. As it closes a global control loop any introduced communication delay possibly destabilizes the system and impairs the performance. The scattering theory is known to solve these stability issues by transmitting wave variables instead of haptic signals, i.e. force and velocity, over the communication channel. For stability and performance additionally high packet rates are required stressing the underlying network resources. Perceptual coding techniques of haptic signals, such as the Weber-inspired deadband approach, are known to successfully reduce the packet rate on the haptic channel. However, as wave variables do not directly represent haptic information but a linear transformation of both signals, perceptual coding is not directly applicable anymore. In this paper, we present a novel control scheme as well as a modification of the deadband approach to take advantage of the stabilization ability of the wave variables while allowing perceptual coding on the communication channel. Simulation results and comparison with the wave variables architecture indicate improved data compression for same degree of transparency for purely stiff as well as free environments.
Application-driven cross-layer optimization for video streaming over wireless networks

Mobile multimedia applications require networks that optimally allocate resources and adapt to dynamically changing environments. Cross-layer design (CLD) is a new paradigm that addresses this challenge by optimizing communication network architectures across traditional layer boundaries. In this article we discuss the relevant technical challenges of CLD and focus on application-driven CLD for video streaming over wireless networks. We propose a cross-layer optimization strategy that jointly optimizes the application layer, data link layer, and physical layer of the protocol stack using an application-oriented objective function in order to maximize user satisfaction. In our experiments we demonstrate the performance gain achievable with this approach. We also explore the trade-off between performance gain and additional computation and communication cost introduced by cross-layer optimization. Finally, we outline future research challenges in CLD.
SIFT feature-preserving bit allocation for H.264/AVC video compression

Compression artifacts in low-quality videos strongly influence the performance of feature matching algorithms. In order to achieve reasonable feature matching performance even for low bit rate video, we propose to allocate the bit budget during compression such that the important features are preserved. Specifically, we present two bit allocation approaches to preserve the strongest SIFT features for H.264 encoded videos. For both approaches, we first categorize the Macroblocks in a Group of Pictures into several groups according to the scale specific characteristics of SIFT features. In our first approach a novel R-D model based on the matching score is applied to allocate the bit budget to these groups. In our second approach, in order to reduce the computational complexity, we analyze the detector characteristics of correctly matched pairs and propose a R-D optimization method based on the repeatability metric. Our experiments show that both approaches achieve better feature preservation when compared to standard video encoding which is optimized for maximum picture quality. The proposed approaches are fully standard compatible and the encoded videos can be decoded by any H.264 decoder.
Multiuser MIMO: Principle, Performance in Measured Channels and Applicable Service

The exploitation of multiuser diversity and the application of multiple antennas at transmitter and receiver are considered to be key technologies for future highly bandwidth-efficient wireless systems. We combine both ideas in a downlink multicarrier transmission scheme where multiple users compete for the available resources in time, frequency and space. The instantaneous channel impulse responses for all users are assumed to be perfectly known at the transmitter. Our proposed algorithm allocates each spatial dimension on a subcarrier to the user which has the highest channel tap gain on the respective spatial dimension. The scheduling strategy is optimized for sum capacity maximization. In this paper, we restrict ourselves to a more illustrative description of the idea rather then providing mathematical details. We demonstrate the potential of the proposed scheme by capacity results for measured real world channels in a large office environment. Finally, video streaming is used as a potential application with high data rate and low latency demands. It is shown that the proposed method has the potential to exploit multiuser diversity while still providing stable video streams even though QoS constraints are not explicitly taken into account by the scheduler.
Fast converging auction-based resource allocation for QoE-driven wireless video streaming

Quality-of-Experience (QoE)-driven centralized resource allocation approaches for video streaming have been studied intensively but require the availability of utility information about the video content and channel conditions of all users at the central optimization entity to perform an optimal allocation. On the other hand, the practical application of game-theory-based decentralized resource allocation approaches for potentially selfish users has been limited so far. This is mainly because the varying network conditions and the delay constraints for wireless multimedia communications require low-complexity methods with fast convergence. We propose an auction-based radio resource allocation method which is shown to converge within a bounded number of iterations. The achieved allocation maximizes the average QoE over all users, while the users are maximizing their own payoff. The users pay a price for the requested resources which is defined on the utility scale. The proposed game-theory framework is compatible with cross-layer optimization approaches, as the resources are abstracted to provide an interface between the application and the lower layers. We implement the proposed resource allocation scheme in a simulated LTE uplink environment with multiple video streaming users. Experimental results confirm the derived properties and additionally show that, unlike state-of-the-art decentralized resource allocation schemes, our proposed auction is scalable, as the number of iterations to converge decreases with an increasing number of participating users.
Energy-efficient and QoE-driven adaptive HTTP streaming over LTE

LTE networks offer broadband wireless access to mobile users who can benefit from high data rate applications such as video streaming. In order to improve the user satisfaction, Quality-of-Experience (QoE) based resource allocation for multiple streaming users in an LTE cell has been studied. However, the high energy consumption of these applications has not been considered. In this paper, we propose adaptive Discontinuous Reception (DRX) parameters for LTE that reduce the energy consumption of mobile devices without degrading the video quality of adaptive HTTP streaming users. Furthermore, we extend the QoE-optimized resource allocation by additionally considering the power consumption of the mobile devices. Simulation results show the benefits of using the proposed adaptive DRX parameters and that further energy saving gains can be achieved by including the power consumption in the optimization problem.
A novel coordinated adaptive video streaming framework for Scalable Video over mobile networks

This paper presents a novel adaptive video streaming framework designed for OFDM-based mobile networks. The proposed adaptation algorithm exploits the benefits of the Scalable Video Coding (SVC) extension of the H.264/AVC standard together with cross-layer information from the mobile terminals in performing adaptation that maximizes streaming qualities amongst all participating users. Simulations for adaptive video streaming along with the well-known TFRC-based and no adaptation scenarios were conducted in a realistic mobile network simulator. Our results show significant improvements in all aspects for the introduced framework over both the TFRC-based and no adaptation cases.
Minimizing the number of support points in perceptual offline haptic compression

During the recording of a haptic interaction session both position and force-feedback data have to be saved. This work proposes two novel methods for haptic data compression which allow to keep the reconstruction errors during playback below human perception thresholds. Supported by well-known results from psychophysics, the first proposed non-iterative method determines the least amount of samples necessary to reconstruct the force-feedback signal within the perception thresholds. The second proposed method compresses the position signal in open-loop fashion exploiting the fact that for the replay of haptic interaction sessions the position signal no longer has influence on the force-feedback. Thus, the only position information perceived by the user is the displayed visual information and therefore it can be compressed exploiting the limits of human visual perception. Our experimental results show better haptic signal compression performance when compared to the state-of-the-art for both methods while maintaining good haptic and visual subjective experiences.
6DOF point cloud alignment using geometric algebra-based adaptive filtering

In this paper we show that a Geometric Algebra-based least-mean-squares adaptive filter (GA-LMS) can be used to recover the 6-degree-of-freedom alignment of two point clouds related by a set of point correspondences. We present a series of techniques that endow the GA-LMS with outlier (false correspondence) resilience to outperform standard least squares (LS) methods that are based on Singular Value Decomposition (SVD). We furthermore show how to derive and compute the step size of the GA-LMS.
Low-complexity error-resilient data reduction approach for networked haptic sessions

We introduce a low-complexity error-resilient perceptual haptic data reduction scheme addressing network packet losses. As shown in previous works, haptic packet losses highly impair telemanipulation systems displaying haptic artifacts and causing instabilities. Inspired by a previous error-resilient haptic data reduction approach, we propose a decreased-complexity binary tree scheme that aims to selectively trigger additional haptic packets in order to mitigate the haptic artifacts at the receiver. Moreover, we present the analytical expressions for updating on-the-fly all estimates in the binary tree. We show not only the complexity decrease in the system but also that the proposed scheme outperforms the previous approach in terms of data reduction and subjective quality ratings.
Proximity-Aware Collaborative Multicast for Small P2P Communities

In this paper we describe a novel solution for delay sensitive one-to-many content distribution in P2P networks based on cooperative m-ary trees. Our scheme maximizes the overall throughput while minimizing end-to-end delay by exploiting the full upload capacities of the participating peers and their proximity relationship. Our delivery scheme is based on cooperation between the source, the content-requesting peers and the helper peers. In our solution, the source splits the content into several blocks and feeds them into multiple m-ary trees rooted at the source. Every peer contributes its upload capacity by being a forwarding peer in at least one of the m-ary trees. Our performance evaluation shows that our proposal achieves similar throughput as the best known solution in the literature (mutualcast) while at the same time reducing content delivery delay.
Speeded-up SURF: Design of an efficient multiscale feature detector

We present a fast and highly performant multiscale feature detector which is based on the established SURF algorithm. The additional speed-up is achieved by linearizing the SURF detector in a way that its detection characteristics are preserved. Our evaluations show that the proposed suSURF detector is roughly 30% faster without significant sacrifices in feature quality. The key points detected by suSURF are compatible with standard SURF features and those found by other determinant of Hessian (DoH) based detectors.
Block structure reuse for multi-rate high efficiency video coding

Adaptive HTTP streaming requires a video to be encoded at multiple independently decodable bitrates. Encoding of multiple bitrates is a complex and time consuming process, especially with the new video coding standard HEVC. In this paper, we analyze the relation of HEVC block structures across encodings of a video at different bitrates. We propose a multi-rate encoding method for HEVC to decrease the overall encoding complexity while keeping the rate-distortion performance as high as possible. Experimental results show an average encoding time decrease of 27% compared to the reference HEVC encoder without degrading the rate-distortion performance.
3D motion and scene structure estimation with motion dependent distortion of measurement windows

We present a two stage technique for estimation of rigid body motion and scene structure from monocular image sequences. The robustness of motion recovery is considerably increased by motion dependent distortion of rectangular measurement windows. In the first stage, we estimate a set of candidate motion parameter sets from which the optimum is determined in a second step evaluating motion based image content deformation together with corresponding depth values. Experimental results show that the motion parameter estimation error can be reduced significantly.
A novel real-time video data scheduling approach for driver assistance services

Traffic shaping and data scheduling are important components of an IP-camera based in-vehicle sensor network for driver assistance services. To this end, we present an IP-camera specific traffic shaping scheme as well as a service specific data scheduling approach. In order to determine a suitable configuration of the service specific scheduling approach, relevant driver assistance services are described and analyzed. In our work we assume that the video data is carried by an in-car switched IP/Ethernet network. As an implementation study, an IP-camera sensor network has been developed in order to investigate the proposed traffic shaping as well as data scheduling mechanisms. Our experimental results show that the IEEE 1588 v2 standard is suitable for the proposed data scheduling approach and that as a result reduced network resource overprovisioning is required.
A Quality-of-Experience driven bidding game for uplink video transmission in next generation mobile networks

Centralized approaches to solve resource allocation problems for wireless real-time multimedia communications have been intensively studied but require the availability of meta information about the multimedia content and channel information of all users. In this paper, we propose a Quality of Experience (QoE) driven bidding game for de-centralized uplink resource allocation among multiple mobile video producers. Different from previous works, the price per resource unit is defined on a Mean Opinion Score (MOS) scale and users bid for the resources that maximize their own utility function. Simulations in an LTE environment show the benefits of our distributed approach in terms of convergence time and QoE performance, compared to a centralized greedy scheme and a state-of-the-art game-theoretic approach.
QoE-Based Traffic and Resource Management for Adaptive HTTP Video Delivery in LTE

There is a growing interest in over-the-top (OTT) dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) (DASH) services. In mobile DASH, a client controls the streaming rate and the base station in the mobile network decides on the resource allocation. Different from the majority of previous works that focus on client-based rate adaptation mechanisms, this paper investigates the mobile network potential for enhancing the user quality-of-experience (QoE) in multiuser OTT DASH. Specifically, we first present proactive and reactive QoE optimization approaches for adapting the adaptive HTTP video delivery in an long-term evolution network. We then show, using subjective experiments, that by taking a proactive role in determining the transmission and streaming rates, the network operator can provide a better video quality and a fairer QoE across the streaming users. Furthermore, we consider the playout buffer time of the clients and propose a novel playout buffer-dependent approach that determines for each client the streaming rate for future video segments according to its buffer time and the achievable QoE under current radio conditions. In addition, we show that by jointly solving for the streaming and transmission rates, the wireless network resources are more efficiently allocated among the users and substantial gains in the user perceived video quality can be achieved.
Exploiting Text-Related Features for Content-based Image Retrieval

Distinctive visual cues are of central importance for image retrieval applications, in particular, in the context of visual location recognition. While in indoor environments typically only few distinctive features can be found, outdoors dynamic objects and clutter significantly impair the retrieval performance. We present an approach which exploits text, a major source of information for humans during orientation and navigation, without the need for error-prone optical character recognition. To this end, characters are detected and described using robust feature descriptors like SURF. By quantizing them into several hundred visual words we consider the distinctive appearance of the characters rather than reducing the set of possible features to an alphabet. Writings in images are transformed to strings of visual words termed visual phrases, which provide significantly improved distinctiveness when compared to individual features. An approximate string matching is performed using N-grams, which can be efficiently combined with an inverted file structure to cope with large datasets. An experimental evaluation on three different datasets shows significant improvement of the retrieval performance while reducing the size of the database by two orders of magnitude compared to state-of-the-art. Its low computational complexity makes the approach particularly suited for mobile image retrieval applications.
Performance analysis of different network topologies for in-vehicle audio and video communication

In-vehicle audio and video communication has recently received considerable attention in the automotive industry. First, because of the increasing number of audio and video consumer electronic devices and the requirement to integrate them in the car. Second, because of the rapid growing of realtime audio and video based driver assistance systems. So far, the interconnection of in-vehicle stream oriented devices has been performed by different and automotive specific network systems with limited transmission capacities. IP over Ethernet provides good conditions for audio and video streaming by offering high transmission capacities. In order to design an appropriate IP/Ethernet network for audio and video communication in the car, the selection of the right network topology is essential. In this work, several network topologies are compared by their Quality of Service (QoS) performance and production cost. Appropriate topologies are proposed.
Preserving SIFT features in JPEG-encoded images

For image compression applications where the information sink is not a person but a computer algorithm, the image encoder should control the encoding process in such a way that the important and relevant features of the image are preserved after compression. In this paper, our goal is to preserve the strongest SIFT features for JPEG-encoded images. We analyze the relevant characteristics of SIFT features and categorize the image Macroblocks into several groups. Then we propose a novel rate-distortion model which is based on the SIFT feature matching score. The dependency between the quantization table in the JPEG file and the common Lagrange multiplier is obtained from a training image database. Then for a given image quality we exploit this relationship to perform R-D optimization for each group. Our results show that the proposed algorithm achieves better feature preservation when compared to standard JPEG encoding. The proposed approach is fully standard compatible.