The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Keperluan untuk penggunaan masa nyata data dinamik manusia semakin meningkat. Keperluan teknikal untuk ini termasuk pangkalan data yang dipertingkatkan untuk mengendalikan sejumlah besar data serta penderiaan pergerakan orang yang sangat tepat. Format indeks bitmap telah dicadangkan untuk pemprosesan data berkelajuan tinggi yang merebak dalam ruang dua dimensi. Menggunakan format yang sama dijangka menyediakan perkhidmatan yang mencari pertanyaan, membaca data yang dikehendaki, menggambarkannya dan menganalisisnya. Dalam kajian ini, kami mencadangkan format pengekodan yang membolehkan data dinamik manusia memampatkannya dalam saiz data sasaran, untuk menjimatkan storan data untuk peningkatan berturut-turut data dinamik manusia masa nyata. Dalam kaedah yang dicadangkan, taburan populasi spatial, yang dinyatakan dengan taburan kebarangkalian, dianggarkan dan dimampatkan menggunakan format data satu-bait satu piksel yang biasanya digunakan untuk pengekodan imej. Kami menggunakan dua jenis anggaran, iaitu ketepatan kebarangkalian dan ketepatan lokasi spatial, untuk mengawal saiz data dan jumlah maklumat. Untuk ketepatan kebarangkalian, kami mencadangkan kaedah pemetaan bukan linear untuk taburan spatial, dan untuk ketepatan lokasi spatial, kami mencadangkan pengekodan berlapis berskala spatial untuk memperhalusi tahap jaringan taburan spatial. Selain itu, untuk membolehkan analisis terperinci tambahan, kami mencadangkan satu lagi pengekodan berlapis berskala yang meningkatkan ketepatan pengedaran. Kami menunjukkan melalui eksperimen bahawa penghampiran data dan format pengekodan yang dicadangkan mencapai anggaran taburan populasi spatial yang mencukupi dalam keadaan saiz data sasaran yang diberikan.
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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Salinan
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
Salinan
@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},}
Salinan
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 -