Institute of Informatics & Telecommunications, NCSR Demokritos（希腊国家科学研究中心德谟克利特信息学与电信研究所）；Information Management Systems Institute, Athena Research Center（雅典娜研究中心信息管理系统研究所）；University of Piraeus（比雷埃夫斯大学）
Giannis Fikioris, Kostas Patroumpas, Alexander Artikis
In previous work we introduced a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. This methodology can provide reliable trajectory synopses with little deviations from the original course by discarding at least 70% of the raw data as redundant. However, such trajectory compression is very sensitive to parametrization. In this paper, our goal is to fine-tune the selection of these parameter values. We take into account the type of each vessel in order to provide a suitable configuration that can yield improved trajectory synopses, both in terms of approximation error and compression ratio. Furthermore, we employ a genetic algorithm converging to a suitable configuration per vessel type. Our tests against a publicly available AIS dataset have shown that compression efficiency is comparable or even better than the one with default parametrization without resorting to a laborious data inspection.
The Hong Kong Univeristy of Science and Technology（香港科技大学）
High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras, where the noise brings extra challenges to lossy image compression algorithms. Without the capacity to tell the difference between image details and noise, general image compression methods allocate additional bits to explicitly store the undesired image noise during compression and restore the unpleasant noisy image during decompression. Based on the observations, we optimize the image compression algorithm to be noise-aware as joint denoising and compression t…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Netflix Inc.（Netflix公司）
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess visual information loss, these simple norms are not highly consistent with human perception. Here, we propose a different proxy approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, which mimics the perceptual model while serving as a loss layer of the network.We experimentally demonstrate how this optimization framework can be appli…
A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation
National University of Singapore（新加坡国立大学）；Wuhan University of Technology（武汉理工大学）；Nanyang Technological University（南洋理工大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；University of Melbourne（墨尔本大学）；Hefei University of Technology（合肥工业大学）
The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications. We propose a quantum-enabled lossy 3d point cloud compression pipeline based on the constructive solid geometry (CSG) model representation. Key parts of the pipeline are mapped to NP-complete problems for which an efficient Ising formulation suitable for the execution on a Quantum Annealer exists. We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks…
Deep learning for computer vision depends on lossy image compression: it reduces the storage required for training and test data and lowers transfer costs in deployment. Mainstream datasets and imaging pipelines all rely on standard JPEG compression. In JPEG, the degree of quantization of frequency coefficients controls the lossiness: an 8 by 8 quantization table (Q-table) decides both the quality of the encoded image and the compression ratio. While a long history of work has sought better Q-tables, existing work either seeks to minimize image distortion or to optimize for models of the human…
School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）
There is a proliferation in the number of satellites launched each year, resulting in downlinking of terabytes of data each day. The data received by ground stations is often unprocessed, making this an expensive process considering the large data sizes and that not all of the data is useful. This, coupled with the increasing demand for real-time data processing, has led to a growing need for on-orbit processing solutions. In this work, we investigate the performance of CNN-based object detectors on constrained devices by applying different image compression techniques to satellite data. We ex…
Center for Visual Information Technology（视觉信息技术中心）；Kohli Center on Intelligent Systems（科利智能系统中心）；IIIT-Hyderabad（印度国际信息技术学院海得拉巴分校）
Binarization is an extreme network compression approach that provides large computational speedups along with energy and memory savings, albeit at significant accuracy costs. We investigate the question of where to binarize inputs at layer-level granularity and show that selectively binarizing the inputs to specific layers in the network could lead to significant improvements in accuracy while preserving most of the advantages of binarization. We analyze the binarization tradeoff using a metric that jointly models the input binarization-error and computational cost and introduce an efficient a…
ROIX-Comp: Optimizing X-ray Computed Tomography Imaging Strategy for Data Reduction and Reconstruction
The University of Electro-Communications, RIKEN (R-CCS)（电子通信大学，RIKEN（R-CCS））；Japan Synchrotron Radiation Research Institute（日本同步辐射研究中心）；RIKEN SPring-8 Center（RIKEN SPring-8研究中心）
ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）；Google（谷歌）
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types. Thus far, prior work mostly focused on optimizing their reconstruction performance. This work investigates INRs from a novel perspective, i.e., as a tool for image compression. To this end, we propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding. Encoding with INRs, i.e. overfitting to a data sample, is typically orders of magnitude slower. To mitigate this drawback, we leverage me…
Coupled Reconstruction of 2D Blood Flow and Vessel Geometry from Noisy Images via Physics-Informed Neural Networks and Quasi-Conformal Mapping
Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港心脑血管健康工程研究中心）；Norwegian Research Centre (NORCE)（挪威研究中心）；Department of Operations and Risk Management and the School of Data Science, Lingnan University（岭南大学运营与风险管理系及数据科学学院）
