nils-herrmann/clean_scirepeval huggingface dataset:
Improving the verification process of video quality metrics

The most important step in the development process of a video quality metric is its verification with regards to the subjective quality experience. Even though guidelines in the form of standards and recommendations are well known, there are still quite often shortcomings in the verification process of many metrics. In this contribution we revisit these rules, point out important details and review contributions to video quality metrics for typical shortcomings. We will highlight in detail five steps that should be followed in order to improve the overall quality of the verification process of video quality metrics: using a large and diverse data base, planing and conducting subjective tests carefully, using different data for calibration and verification of a metric, avoiding unnecessary data fitting steps, and a clear and meaningful presentation of the results. Also we will provide short examples how an improper verification might affect the results of a video quality metric.
Nonlinear control of a semi-active suspension system considering actuator and state constraints using methods of optimal control

Two optimal control methods for a nonlinear semi-active suspension system subject to state and actuating constraints are presented and compared with LQR and skyhook control. Nonlinearities in the suspension system are considered in terms of variable kinematic transmission factor, bump stops and asymmetric and degressive continously variable damper characteristics. The first approach incorporates the state inequality constraint as well as the state-dependent actuating variable constraints due to the working range of the damper characteristics by applying the method of nonlinear programming. In the second strategy, the optimal control problem is formulated as an optimal switching problem between nonlinear subsystems defined by the valve current depending damper characteristics. This enables a recurrent problem formulation and thus the application of Dynamic Programming. Both approaches lead to a state-dependent control law, which is integrated in the feedback loop. The performance of the proposed approaches is analyzed in simulations based on the nonlinear model of a quarter-car test rig.
AVC Intraprediction Mode Decision Based on 4x4 Integer Transform Coefficients

In order to achieve AVCs fall potential in intracoding efficiency an encoder must choose an appropriate coding option for each block in every frame. This leads to a significant computational effort. In this paper, we investigate employing the AVC 4times4 integer transform in order to decide intraprediction size and mode for AVC encoding in an efficient manner. The transform is used to constrain the encoding options with a feasible overhead, as it can be implemented using shifts and additions only.
Emotional evaluation of bandit problems

In this paper, we discuss an approach to evaluate decisions made during a multi-armed bandit learning experiment. Usually, the results of machine learning algorithms applied on multi-armed bandit scenarios are rated in terms of earned reward and optimal decisions taken. These criteria are valuable for objective comparison in finite experiments. But learning algorithms used in real scenarios, for example in robotics, need to have instantaneous criteria to evaluate their actual decisions taken. To overcome this problem, in our approach each decision updates the Zürich model which emulates the human sense of feeling secure and aroused. Combining these two feelings results in an emotional evaluation of decision policies and could be used to model the emotional state of an intelligent agent.
Dense disparity maps from sparse disparity measurements

In this work we propose a method for estimating disparity maps from very few measurements. Based on the theory of Compressive Sensing, our algorithm accurately reconstructs disparity maps only using about 5% of the entire map. We propose a conjugate subgradient method for the arising optimization problem that is applicable to large scale systems and recovers the disparity map efficiently. Experiments are provided that show the effectiveness of the proposed approach and robust behavior under noisy conditions.
Optimization of video coding for telepresence applications

In several telepresence applications the transmitting end is resource constrained, hence multi-view video has to be source coded and transmitted to a location where stereo matching is performed. The effect of video source coding on stereo matching techniques is presently not well understood. In this paper, we present an analysis which quantitatively demonstrates the impact of standard video coding techniques on the quality of well-known stereo matching algorithms. We propose a quantitative evaluation methodology and framework. Evaluation is also presented for the state-of-the-art Annex H of H.264/AVC (multi-view coding). The results shed a light on the tradeoff between video coding parameters and the quality of dense stereo matching. This enables us to select suitable operating regions for both stereo matching and video coding, for a diverse range of applications. In addition, various system configurations with varying performance-complexity tradeoff are also compared, giving insight on selecting the configuration suitable for a variety of telepresence applications.
HRTF-based localization and separation of multiple sound sources

The human auditory system excels at pinpointing and distinguishing multiple sound sources in noisy and reverberant environments. Mobile robotic platforms implement such capabilities with varying success, classically solving localization and separation independently. This paper presents an algorithm utilizing Head-Related Transfer Function (HRTF) based localization to aid the task of separation. HRTFs for robotic binaural hearing represent the digital emulation of a human's innate direction-dependent filtering for solving the localization problem in a compact and robust manner. The overall result of the presented algorithm for robotic binaural hearing is an HRTF-based localization and separation system, capable of dynamically and intelligently processing simultaneously active sound sources.
Authentication of MPEG-4-based surveillance video

The industry is currently starting to use MPEG-4 compressed digital video for surveillance applications. The transition from analog to digital video raises difficulties for using surveillance video in court. Since it is fairly easy to make hard-to-detect modifications to the video stream captured by the cameras, e.g. mask out a specific event or person, a system for proving authenticity and integrity of video streams is needed. This paper presents such a system based on digital signatures embedded in the video stream. Our concept provides a means for proving authenticity and integrity of MPEG-4 digital video streams, while leaving compatibility with standard media players untouched.
Challenges in crowd-based video quality assessment

Video quality evaluation with subjective testing is both time consuming and expensive. A promising new approach to traditional testing is the so-called crowdsourcing, moving the testing effort into the Internet. The advantages of this approach are not only the access to a larger and more diverse pool of test subjects, but also the significant reduction of the financial burden. Recent contributions have also shown that crowd-based video quality assessment can deliver results comparable to traditional testing in some cases. In general, however, new problems arise, as no longer every test detail can be controlled, resulting in less reliable results. Therefore we will discuss in this contribution the conceptual, technical, motivational and reliability challenges that need to be addressed, before this promising approach to subjective testing can become a valid alternative to the testing in standardized environments.
A Rational Hrtf Interpolation Approach for Fast Synthesis of Moving Sound

For the purpose of a realistic rendering of moving sound with no noticeable discontinuity, binaural synthesis of the time-varying sound field is performed by updating a dense grid of head related transfer functions, HRTFs. Unless the differences in HRTFs are sufficiently small, a direct switching between them will cause an audible artifact that is heard as a click. To avoid this problem and ensure the availability of enough HRTFs, we have first reduced the HRTFs using PCA and then proposed a binaural impulse-response interpolation algorithm based on the solution for the rational minimal state-space interpolation problem. Compared with existing interpolation techniques, this method allowed very precise reconstruction of HRTFs in the horizontal plane and proved to have superior performance for a wide range of azimuths
The impact of nonlinear filtering and confidence information on optical flow estimation in a Lucas & Kanade framework

Determining optical flow has been a wide field of research for more than 20 years now that has not been solved satisfactorily yet. In this work, we study the influence of a nonlinear smoothing process based on bilateral filtering on a Lucas & Kanade framework for the estimation of optical flow between two image frames. Different confidence measures are used to improve the computation process and detect occlusion and innovation phenomena, explicitly handling discontinuous flow fields. By means of simulations we report that the accuracy can be increased significantly, making this approach interesting for further investigations.
A Novel Technique to Model the Variation of the Intrinsic Parameters of an Automatic Zoom Camera using Adaptive Delaunay Meshes Over Moving Least-Squares Surfaces

The accuracy of computer vision systems is highly dependent on the correct estimates of the camera intrinsic parameters. This accuracy is important in numerous applications like telepresence and robot navigation. In this work, a novel technique is proposed to model the variation of the camera's intrinsic parameters as a function of the focus and the zoom. The proposed method computes the complete surfaces of the intrinsic parameters from a predefined number of focus/zoom measurements using a moving least-squares (MLS) regression technique. Then, it approximates the generated MLS surfaces by employing adaptive Delaunay meshes. Compared to a previous technique using bivariate polynomial functions, the new method results in a 94% enhancement of the mean estimation error. In addition, the new method leads to the same accuracy of the results as compared to a previous version of the MLS technique while requiring a less amount of computations.
Backwards Compatible 3D Audio Conference Server Using HRTF Synthesis and SIP

This paper describes an integration of a 3D sound convolution engine into a telephone conference server software. Our system virtually places participants of a telephone conference to different positions around the listeners by using Head Related Transfer Functions (HRTFs), which helps to identify active speakers in an audio conference. Intelligibility of communication is also increased by making use of the cocktail party effect which helps to concentrate on one specific participant, even if there are more simultaneously active speakers. To support regular telephone systems, we focus in particular on compatibility to existing Voice over IP (VoIP) infrastructure by using standardized protocols like the Session Initiation Protocol (SIP) and the Real-Time Transport Protocol (RTP). Both traditional telephone devices and software clients are put into one heterogeneous conference. While the conference server transmits the down-mixed, mono single-channel conference signal to regular phones, the software clients can receive a stereo-signal enabling full 3D audio.
Cartoon-like image reconstruction via constrained ℓp-minimization

This paper considers the problem of reconstructing images from only a few measurements. A method is proposed that is based on the theory of Compressive Sensing. We introduce a new prior that combines an ℓp-pseudo-norm approximation of the image gradient and the bounded range of the original signal. Ultimately, this leads to a reconstruction algorithm that works particularly well for Cartoon-like images that commonly occur in medical imagery. The arising optimization task is solved by a Conjugate Gradient method that is capable of dealing with large scale problems and easily adapts to extensions of the prior. To overcome the none differentiability of the ℓp-pseudo-norm we employ a Huber-loss term like approximation together with a continuation of the smoothing parameter. Numerical results and a comparison with the state-of-the-art methods show the effectiveness of the proposed algorithm.
Robust 3D Robotic Sound Localization Using State-Space HRTF Inversion

We address the problem of robotic real-time binaural hearing using a humanoid head. The sound detection ability for a previously proposed robotic 3D binaural sound localization technique is stabilized and considerably enhanced. The proposed technique is based on using two small microphones placed inside the ear canal of a robot dummy head mounted on a torso. As initially proposed, the detection algorithm is based on a simple correlation approach using a generic set of head related transfer functions, HRTFs, inverted using the fast Fourier transform, FFT, method. In the current work, we replace the Fourier inversion mechanism with state-space inversion using outer-inner factorization, and we demonstrate the performance through simulation and further in a household environment. Due to this modification, our robotic sound detection set up proves to be more noise-tolerant and able to localize sound sources in a three-dimensional space with a higher precision.
A cognitive approach to monitor and control focal shifts in laser beam welding applications

Even though widely used, laser beam welding faces a strong demand for improved monitoring and control capabilities. In this work the occurring focal shift which leads to faulty welding results will be explained. An approach is taken to monitor this effect caused by the heading of the optics. By means of a camera sharing the same optical components as the laser beam, the deviation of the optimal focal point is obtained through application of sharpness operators. Moreover, a certain measure of light intensity allows one to determine whether the focal plane is in front or behind the optimal focal point. By inclusion of further process parameters and an estimation of the process quality given by a cognitive monitoring system, a large manifold is built up. By application of linear and non-linear dimensionality reduction algorithms, the manifold is reduced to significant features fed to a fuzzy kNN-classifier. Its outcome suggests the required change in z-direction of the optical system with a behavior similar to the human eyes. The overall system is taught by an expert during its initialization and later saves production down times and faulty welding procedures.
QualityCrowd — A framework for crowd-based quality evaluation

Video quality assessment with subjective testing is both time consuming and expensive. An interesting new approach to traditional testing is the so-called crowdsourcing, moving the testing effort into the internet. We therefore propose in this contribution the QualityCrowd framework to effortlessly perform subjective quality assessment with crowdsourcing. QualityCrowd allows codec independent quality assessment with a simple web interface, usable with common web browsers. We compared the results from an online subjective test using this framework with the results from a test in a standardized environment. This comparison shows that QualityCrowd delivers equivalent results within the acceptable inter-lab correlation. While we only consider video quality in this contribution, QualityCrowd can also be used for multimodal quality assessment.
Sparse stereo matching using belief propagation

Depth from stereo is an important research field in computer vision due to the wide range of its applications. In this work, we present a stereo matching algorithm based on belief propagation (BP). The algorithm is designed to work on sparse images originating from image content adaptive mesh representation techniques. There, an image is approximated with a mesh. The nodes of the mesh are the non-uniform samples which are the ones that form the sparse image. The key issue in the proposed method is to formulate BP such that it matches a sparse left stereo image with a dense right image to obtain a sparse depth map. Moreover, we propose a simple method that recovers the dense disparity map of the scene from the sparse one using the approximating mesh of the image. The results obtained show that the proposed method leads to an average of 40% improvement in the quality of depth maps when compared to existing sparse stereo matching techniques.
Video quality evaluation in the cloud

Video quality evaluation with subjective testing is both time consuming and expensive. An interesting new approach to traditional testing is the so-called crowdsourcing, moving the testing effort into the internet. The QualityCrowd framework allows codec independent, crowd-based video quality assessment with a simple web interface, usable with common web browsers. However, due to its codec independent approach, the framework can pose high bandwidth requirements on the coordinating server. We therefore propose in this contribution a cloud-based extension of the QualityCrowd framework in order to perform subjective quality evaluation as a cloud application. Moreover, this allows us to access an even larger pool of potential participants due to the improved connectivity. We compare the results from an online subjective test using this framework with the results from a test in a standardized environment. This comparison shows that QualityCrowd delivers equivalent results within the acceptable inter-lab correlation.
A Fast and Robust Solution to the Five-Pint Relative Pose Problem using Gauss-Newton Optimization on a Manifold

Extracting the motion parameters of a moving camera is an important issue in computer vision. This is due to the need of numerous emerging applications like telepresence and robot navigation. The key issue is to determine a robust estimate of the (3×3) essential matrix with its five degrees of freedom. In this work, a robust technique to compute the essential matrix is suggested under the assumption that the images are calibrated. The algorithm is a combination of the five-point relative pose problem using an optimization technique on a manifold, with the random sample consensus. The results show that the proposed method delivers faster and more accurate results than the standard techniques.
Schur parametrization of symmetric indefinite matrices

It is shown that the generalized Schur algorithm for triangular factorization of symmetric positive definite matrices has a natural extension to the factorization of symmetric indefinite matrices with nonsingular principal submatrices. The proof is constructive and provides for an explicit formulation of the J-orthogonal and triangular matrices involved in the procedure. The (group-theoretic) significance of degenerate transformation steps involving unbounded reflection coefficients is precisely identified. It is found how to assign them an interpretation as Schur parameters and how to get benefit from this knowledge for performing a suitable change of equivalence class during execution, instead of a breakdown of the algorithm.<<ETX>>
The TUM high definition video datasets

The research on video quality metrics depends on the results from subjective testing for both the design and development of metrics, but also for their verification. As it is often too cumbersome to conduct subjective tests, freely available data sets that include both mean opinion scores and the distorted videos are becoming ever more important. While many datasets are already widely available, the majority of these data sets focus on smaller resolutions. We therefore present in this contribution the TUM high definition datasets that include videos in both 1080p25 and 1080p50, encoded with different coding technologies and settings, H.264/AVC and Dirac, but also different presentation devices from reference monitors to home-cinema projectors. Additionally a soundtrack is provided for the home-cinema scenario. The datasets are made freely available for download under a creative commons license.
Content Adaptive Mesh Representation of Images Using Binary Space Partitions

The interest in content adaptive mesh generation of images has been arising lately due to its wide area of applications in image processing. The major issue is to represent an image with a low number of pixels while preserving its content. These pixels or the nonuniform samples are then used to generate a mesh that approximates the corresponding image. This work presents a novel method based on binary space partitions in combination with three clustering schemes to approximate an image with a mesh. The algorithm has the ability to simultaneously reduce the number of pixels and generate the mesh approximation. The idea is to assume each triangle of the mesh as a plane. Consequently, it will be possible to reconstruct the inlying pixels with planar equations defined from the three nodes of each triangle. If a triangle's equation does not have the ability to reconstruct the pixels lying within up to a predefined error, it is split into two new triangles. Tested on several real images, the proposed method leads to reduced size meshes in a fast manner while retaining the visual quality of the reconstructed images. In addition, it is parallelizable due to the property of binary space partitions which facilitates its application in real-time scenarios.
Efficient content adaptive mesh representation of an image using binary space partitions and singular value decomposition

Content adaptive mesh generation is an important research area with many applications in image processing and computer vision. The main issue is to represent an image with the pixels that preserve most of the amount of its information. The obtained pixels are then used to generate a mesh that approximates the original image. This work presents a novel iterative method that simultaneously reduces the number of the pixels and generates the mesh approximation of an image. The main idea is to incorporate binary space partitions along with singular value decomposition to cluster the pixels into planes and thus the nodes of the mesh are nothing but the pixels that define each plane. Compared to previous techniques, the proposed method leads to a 30% reduction in the size of the approximating mesh. In addition, the method minimizes the artifacts obtained from the reconstruction of the original image from the approximating mesh.
Partitioned moving least-squares modeling of an automatic zoom lens camera

The accuracy of machine vision systems is highly dependent on the correct estimates of the camera intrinsic parameters. This precision is needed in numerous applications like telepresence and robot navigation. In this work, a new technique is proposed, based on the moving least-squares (MLS) approach, to model the intrinsic parameters of an automatic zoom lens camera system. The key issue is to generate the polynomial functions offline using MLS. Then, by employing a surface tessellation algorithm, each parameter is subdivided into several non-overlapping regions each of which will be approximated using a polynomial function. Hence, the intrinsic parameters can now be estimated by simple evaluation of the corresponding functions. Compared to the previous version of the MLS algorithm, the proposed method requires less amount of computations to estimate the parameters without leading to a significant degradation of the results.