The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.
Hideaki KIMATA
Nippon Telegraph and Telephone Corporation
Xiaojun WU
Nippon Telegraph and Telephone Corporation
Ryuichi TANIDA
Nippon Telegraph and Telephone Corporation
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Hideaki KIMATA, Xiaojun WU, Ryuichi TANIDA, "Image Based Coding of Spatial Probability Distribution on Human Dynamics Data" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1545-1554, October 2021, doi: 10.1587/transinf.2021PCP0001.
Abstract: The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021PCP0001/_p
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@ARTICLE{e104-d_10_1545,
author={Hideaki KIMATA, Xiaojun WU, Ryuichi TANIDA, },
journal={IEICE TRANSACTIONS on Information},
title={Image Based Coding of Spatial Probability Distribution on Human Dynamics Data},
year={2021},
volume={E104-D},
number={10},
pages={1545-1554},
abstract={The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.},
keywords={},
doi={10.1587/transinf.2021PCP0001},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Image Based Coding of Spatial Probability Distribution on Human Dynamics Data
T2 - IEICE TRANSACTIONS on Information
SP - 1545
EP - 1554
AU - Hideaki KIMATA
AU - Xiaojun WU
AU - Ryuichi TANIDA
PY - 2021
DO - 10.1587/transinf.2021PCP0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - The need for real-time use of human dynamics data is increasing. The technical requirements for this include improved databases for handling a large amount of data as well as highly accurate sensing of people's movements. A bitmap index format has been proposed for high-speed processing of data that spreads in a two-dimensional space. Using the same format is expected to provide a service that searches queries, reads out desired data, visualizes it, and analyzes it. In this study, we propose a coding format that enables human dynamics data to compress it in the target data size, in order to save data storage for successive increase of real-time human dynamics data. In the proposed method, the spatial population distribution, which is expressed by a probability distribution, is approximated and compressed using the one-pixel one-byte data format normally used for image coding. We utilize two kinds of approximation, which are accuracy of probability and precision of spatial location, in order to control the data size and the amount of information. For accuracy of probability, we propose a non-linear mapping method for the spatial distribution, and for precision of spatial location, we propose spatial scalable layered coding to refine the mesh level of the spatial distribution. Also, in order to enable additional detailed analysis, we propose another scalable layered coding that improves the accuracy of the distribution. We demonstrate through experiments that the proposed data approximation and coding format achieve sufficient approximation of spatial population distribution in the given condition of target data size.
ER -