Screen content video (SCV) consists primarily of text areas, computer graphics and other computer-generated content and possesses unique perceptual characteristics. To compress SCVs more effectively with less reductio...
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Screen content video (SCV) consists primarily of text areas, computer graphics and other computer-generated content and possesses unique perceptual characteristics. To compress SCVs more effectively with less reduction in subjective quality, perceptual characteristics of SCVs are analyzed and a perceptual redundancy (PR) model for SCV compression is proposed, including spatial PR (SPR), temporal PR (TPR) and foveated PR (FPR) model. In SPR modeling, the SCV is divided into sharp edge (SE) areas and non-SE areas, then SPR is estimated separately. In TPR modeling, both inter-frame luminance adaptation effect and motion masking effect are taken into account. In FPR modeling, each frame of SCV is classified into abrupt frames, relative motion frames or static frames. Then fixation points of different kinds of frames are predicted using different methods, and FPR is modeled considering foveated masking effect and visual attention. Finally, the perceptual redundancy of SCV is estimated based on the product of SPR, TPR and FPR. It is experimentally demonstrated that compared to the state-of-the-art models, the authors' model could obtain more accurate estimates of PR. Moreover, the model is incorporated into SCV compression with an adaptive perceptual quantizer. An average of 7.42% bits could be saved with less decline in subjective quality.
We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algori...
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We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algorithm aims to find the shortest path possible. As we show, this approach scales excellently for various topologies, graph sizes and hardware specifications while maintaining a mean length error below 1% and reasonable memory consumption. By utilizing a simplified structure and keeping backtracking to a minimum, we are able to leverage the same approach on the massively parallel GPUs or any other shared memory parallel architecture, reducing the run time even further. (C) 2021 Elsevier Ltd. All rights reserved.
Guidance has been proposed as a conceptual framework to understand how mixed-initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of ...
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Guidance has been proposed as a conceptual framework to understand how mixed-initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed-initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed-initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
These days, there are numerous issues related to copyright protection and ownership identification as a result of the improper use of electronic data. With growing speed of online media, authentication has become a ne...
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Light leaking in Probe GI is typically solved by visibility tests, which cannot benefit from hardware-aided tri-linear sampling. We present Mask Decomposition, which decomposes the visibility into probe-group indicato...
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Light leaking in Probe GI is typically solved by visibility tests, which cannot benefit from hardware-aided tri-linear sampling. We present Mask Decomposition, which decomposes the visibility into probe-group indicators and their corresponding masks, making it possible to use tri-linear sampling in its reconstruction. We prove that the rendering overhead is significantly reduced with the help of Mask Decomposition, making the rendering at least 3 x faster than the state-of-the-art visibility-test Probe GI, and even as fast as the original leaking probe GI. We also present an efficient algorithm to solve the Mask Decomposition problem and a simple compression method to minimize the spatial overhead of the mask textures, which is much lower than in compression methods like Moving Basis Decomposition (MBD).
Bidirectional reflection distribution function (BRDF) is a function that describes the optical properties of the object surface, which reflects the reflection characteristics of the object surface under different angl...
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In recent years, there have been numerous initiatives regarding the incorporation of building performance simulation tools (BPSTs) in the field of architecture, but unfortunately they have not been considered in updat...
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In recent years, there have been numerous initiatives regarding the incorporation of building performance simulation tools (BPSTs) in the field of architecture, but unfortunately they have not been considered in updates of the curricula of building designers' careers at a global level. This paper presents an example of the use of BPSTs in the decision making of architectural models, after confirming that there is a lack of knowledge regarding these programs in architecture classrooms. As a result, based on the literature review, improvement aspects and recommendations have been identified and consequences for future research have been foreseen. To this end, the growing trend in the use of these tools has been analyzed, both in terms of the impact on architectural design and their use by architects. The authors' perceptions led us to confirm that the disconnection between BPSTs and architectural students can easily be diminished and alleviated through the teaching-learning process. We also noted the importance of energy modelling in the initial stages of architectural design. In addition, a proposed format for the application of BPSTs to architectural design for architects is presented.
Aiming at the problems of high damage rate of cultural relics, low processing efficiency and fuzzy detail processing when producing handmade rubbing, a method of generating digital rubbing of Han Dynasty Stone Relief ...
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Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, a...
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Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on two 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being partially unable to reach the accuracies reported in the original papers, we compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites.
Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability ...
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Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.
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