This paper explores the challenges and solutions in compressing object mask video, which can provide additional scene context for machine learning applications. Object masks, identifying specific objects or regions in images, are crucial for precise visual analysis. However, compressing them with video codecs tuned for human consumption may lead to detrimental artefacts for machine tasks. To alleviate such artefacts, a modification to the luma mapping and chroma scaling (LMCS) in-loop filter in the Versatile Video Coding standard is proposed, targeting the reconstruction of object mask video. By performing object mask reconstruction within the encoding loop, the encoder has access to correctly reference pictures for prediction. Consequently, coding performance is significantly enhanced. Objective evaluation against post-state-of-the-art processing mask reconstruction confirms a 42.0dB and 66.1dB improvement in Y-PSNR for random access and all intra encoding, respectively, while maintaining coding complexity similar to unmodified encoding without post-processing.

Featured snippets that attempt to satisfy users’ information needs directly on top of the first search engine results page (SERP) have been shown to strongly impact users’ post-search attitudes and beliefs. In the context of debated but scientifically answerable topics, recent research has demonstrated that users tend to trust featured snippets to such an extent that they may reverse their original beliefs based on what such a snippet suggests; even when erroneous information is featured. This paper examines the effect of featured snippets in more nuanced and complicated search scenarios concerning debated topics that have no ground truth and where diverse arguments in favor and against can legitimately be made. We report on a preregistered, online user study (N = 182) investigating how the stances and logics of evaluation (i.e., underlying reasons behind stances) expressed in featured snippets influence post-task attitudes and explanations of users without strong pre-search attitudes. We found that such users tend to not only change their attitudes on debated topics (e.g., school uniforms) following whatever stance a featured snippet expresses but also incorporate the featured snippet’s logic of evaluation into their argumentation. Our findings imply that the content displayed in featured snippets may have large-scale undesired consequences for individuals, businesses, and society, and urgently call for researchers and practitioners to examine this issue further.

This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstructing the geometry from the two depthmaps associated to a single projection direction. The partial reconstruction obtained from the depthmaps is completed to a full reconstruction of the point cloud by sweeping section by section along one direction and encoding the points which were not contained in the two depthmaps. The main ingredient is a list-based encoding of the inner points (situated inside the feasible regions) by a novel arithmetic three dimensional context coding procedure that efficiently utilizes rotational invariances present in the input data. State-of-the-art bits-per-voxel results are obtained on benchmark datasets.

Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.

The paper proposes a new lossy way of encoding the geometry of point clouds. The proposed scheme reconstructs the geometry from only the two depth maps associated to a single projection direction and then proposes a progressive reconstruction process using suitably defined anchor points. The reconstruction from the two depth images follows several primitives for analyzing and encoding, several of which are only optional. The resulting bitstream is embedded and can be truncated at various levels of reconstruction of the bounding volume. The encoding tools for encoding the needed entities are extremely simple and can be combined flexibly. The scheme can also be combined with the G-PCC coding, for reconstructing in a lossless way the sparse point clouds. The experiments show improvement of the rate-distortion performance of the proposed method when combined with the G-PCC codec as compared to G-PCC codec alone.

This paper presents an agent-based power scheduling framework for interconnected local energy communities (LECs) using the Nash bargaining solution (NBS) approach to offer a fair and financial reimbursement for changing the operation objectives of such LECs. LECs are a modern form of microgrids (MGs) which consist not only of electrical energy systems but also include thermal energy. The focus is set on the LEC operator needs with different operational objectives, where in classical MGs the island aspect often dominates the operation. We assume that in the future distribution system several LECs will exist and are, therefore, interconnected with each other. It can be assumed that the operational objectives of an LEC and the distribution system operator (DSO) may differ considerably. To align these objectives and achieve system-wide cost-optimal operation, the DSO incentivizes the LECs to deviate from their previously planned power schedules by offering a financial reimbursement that is determined using the NBS mechanism. The local and global cost saving for the proposed agent-based power scheduling framework is demonstrated on a use case scenario modeled after a real pilot microgrid.

Due to the increased popularity of augmented and virtual reality experiences, the interest in representing the real world in an immersive fashion has never been higher. Distributing such representations enables users all over the world to freely navigate in never seen before media experiences. Unfortunately, such representations require a large amount of data, not feasible for transmission on today's networks. Thus, efficient compression technologies are in high demand. This paper proposes an approach to compress 3D video data utilizing 2D video coding technology. The proposed solution was developed to address the needs of `tele-immersive' applications, such as virtual (VR), augmented (AR) or mixed (MR) reality with Six Degrees of Freedom (6DoF) capabilities. Volumetric video data is projected on 2D image planes and compressed using standard 2D video coding solutions. A key benefit of this approach is its compatibility with readily available 2D video coding infrastructure. Furthermore, objective and subjective evaluation shows significant improvement in coding efficiency over reference technology.

The last years have shown significant advances in immersive media. Virtual reality video, in the form of spherical panoramic video, is already widely available. Such technologies will continually evolve towards the ultimate goal of a truly virtual reality experience. The first step in this direction is the support of limited translational head movement for restricted six degrees of freedom virtual reality video. This experience can be achieved by rendering virtual viewports from supplementary depth information. In this context, this paper investigates the effect of depth map compression on the perceptual quality of immersive media. The presented study is focused on near-future applications with readily available hardware. Objective quality assessments indicate possible bit rate savings of 17% through high-quality depth maps. However, these findings could not be confirmed subjectively. On the contrary, subjective evaluation shows a robustness to low-quality depth maps when viewed in a virtual reality scenario.

Modern video compression standards rely on motion-compensated prediction from multiple reference pictures. In past standardisation efforts following the introduction of bidirectional prediction, reference pictures were typically signalled in two independent lists, used for forward- and backward-prediction. Modern video coding standards, such as H.264/MPEG-4 Advanced Video Coding or High Efficiency Video Coding, extend this concept by removing the separation between preceding and succeeding reference pictures, and allow reference frames to be included in the two lists regardless of their temporal index. Therefore, the principle of using two separate lists can be considered obsolete. This paper evaluates an alternative concept, using a single, unified reference picture list. This alternative list, called “list unified” or LU, reduces reference picture signalling, while all functionalities for motion-compensated prediction using multiple references are preserved. Evaluation shows competitive video coding efficiency compared to the usage of two lists, with the advantage of simplified bitstream parsing and much improved reference picture flexibility.

Acquiring scenery depth is a fundamental task in computer vision, with many applications in manufacturing, surveillance, or robotics relying on accurate scenery information. Time-of-flight cameras can provide depth information in real-time and overcome short-comings of traditional stereo analysis. However, they provide limited spatial resolution and sophisticated upscaling algorithms are sought after. In this paper, we present a sensor fusion approach to time-of-flight super resolution, based on the combination of depth and texture sources. Unlike other texture guided approaches, we interpret the depth upscaling process as a weighted energy optimization problem. Three different weights are introduced, employing different available sensor data. The individual weights address object boundaries in depth, depth sensor noise, and temporal consistency. Applied in consecutive order, they form three weighting strategies for time-of-flight super resolution. Objective evaluations show advantages in depth accuracy and for depth image based rendering compared with state-of-the-art depth upscaling. Subjective view synthesis evaluation shows a significant increase in viewer preference by a factor of four in stereoscopic viewing conditions. To the best of our knowledge, this is the first extensive subjective test performed on time-of-flight depth upscaling. Objective and subjective results proof the suitability of our approach to time-of-flight super resolution approach for depth scenery capture.

Consumer interest in 3D television (3DTV) is growing steadily, but current available 3D displays still need additional eye-wear and suffer from the limitation of a single stereo view pair. So it can be assumed that autostereoscopic multiview displays are the next step in 3D-at-home entertainment, since these displays can utilize the Multiview Video plus Depth (MVD) format to synthesize numerous viewing angles from only a small set of given input views. This motivates efficient MVD compression as an important keystone for commercial success of 3DTV. In this paper we concentrate on the compression of depth information in an MVD scenario. There have been several publications suggesting depth down- and upsampling to increase coding efficiency. We follow this path, using our recently introduced Edge Weighted Optimization Concept (EWOC) for depth upscaling. EWOC uses edge information from the video frame in the upscaling process and allows the use of sparse, non-uniformly distributed depth values. We exploit this fact to expand the depth down-/upsampling idea with an adaptive low-pass filter, reducing high energy parts in the original depth map prior to subsampling and compression. Objective results show the viability of our approach for depth map compression with up-to-date High-Efficiency Video Coding (HEVC). For the same Y-PSNR in synthesized views we achieve up to 18.5% bit rate decrease compared to full-scale depth and around 10% compared to competing depth down-/upsampling solutions. These results were confirmed by a subjective quality assessment, showing a statistical significant preference for 87.5% of the test cases.

Emergency physicians in small primary care hospitals seeing patients with acute neurological symptoms have difficulty differentiating ischemic from hemorrhagic strokes and from stroke mimics. Telestroke consults with experienced neurologists supplemented by computerized decision support may aid in this time critical situation. Here we present a Stroke Bayesian Network (SBN) based on a na¨ıve Bayesian classifier to predict the most likely stroke etiology-ischemia, hemorrhage or stroke mimic-in an emergency room (ER) setting. As a proof of concept, this probabilistic network was evaluated in a pilot study on a cohort of 44 acute neurological patients admitted to three primary care hospitals associated with the TASC telestroke network in Saxony-Anhalt. In this cohort, the SBN correctly classified 31 of 36 ischemic stroke patients, and all five stroke mimics, but failed to identify three hemorrhages. For the frequent and significant ischemic stroke type, 97% classification precision and 86% sensitivity were obtained. To properly evaluate the SBN performance, a randomized controlled clinical trial should be conducted on a cohort of patients admitted to the ER.