Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature
In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart f...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838560/ |
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| author | Catarina Brites Joao Ascenso |
| author_facet | Catarina Brites Joao Ascenso |
| author_sort | Catarina Brites |
| collection | DOAJ |
| description | In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a paradigm shift in the way visual information is acquired. This new way of visual information acquisition enables faster and asynchronous per-pixel responses/recordings driven by the scene dynamics with a very high dynamic range and low power consumption. However, depending on the application scenario, the emerging neuromorphic vision sensors may generate a large volume of data, thus critically demanding highly efficient coding solutions in order applications may take full advantage of these new, attractive sensors’ capabilities. For this reason, considerable research efforts have been invested in recent years towards developing increasingly efficient neuromorphic vision data coding (NVDC) solutions. In this context, the main objective of this paper is to provide a comprehensive overview of NVDC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this emerging field. In this way, more solid conclusions can be drawn about the current NVDC status quo, thus allowing to better drive future research and standardization developments in this emerging technical area. |
| format | Article |
| id | doaj-art-d7a6669c30cc4e3a8ca6df9646a99e64 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-d7a6669c30cc4e3a8ca6df9646a99e642025-01-25T00:01:03ZengIEEEIEEE Access2169-35362025-01-0113146261465710.1109/ACCESS.2025.352837510838560Neuromorphic Vision Data Coding: Classifying and Reviewing the LiteratureCatarina Brites0https://orcid.org/0000-0002-6011-4574Joao Ascenso1https://orcid.org/0000-0001-9902-5926Instituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, PortugalInstituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, PortugalIn recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a paradigm shift in the way visual information is acquired. This new way of visual information acquisition enables faster and asynchronous per-pixel responses/recordings driven by the scene dynamics with a very high dynamic range and low power consumption. However, depending on the application scenario, the emerging neuromorphic vision sensors may generate a large volume of data, thus critically demanding highly efficient coding solutions in order applications may take full advantage of these new, attractive sensors’ capabilities. For this reason, considerable research efforts have been invested in recent years towards developing increasingly efficient neuromorphic vision data coding (NVDC) solutions. In this context, the main objective of this paper is to provide a comprehensive overview of NVDC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this emerging field. In this way, more solid conclusions can be drawn about the current NVDC status quo, thus allowing to better drive future research and standardization developments in this emerging technical area.https://ieeexplore.ieee.org/document/10838560/Dynamic vision sensorevent cameraneuromorphic vision data codingspike camerataxonomy |
| spellingShingle | Catarina Brites Joao Ascenso Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature IEEE Access Dynamic vision sensor event camera neuromorphic vision data coding spike camera taxonomy |
| title | Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature |
| title_full | Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature |
| title_fullStr | Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature |
| title_full_unstemmed | Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature |
| title_short | Neuromorphic Vision Data Coding: Classifying and Reviewing the Literature |
| title_sort | neuromorphic vision data coding classifying and reviewing the literature |
| topic | Dynamic vision sensor event camera neuromorphic vision data coding spike camera taxonomy |
| url | https://ieeexplore.ieee.org/document/10838560/ |
| work_keys_str_mv | AT catarinabrites neuromorphicvisiondatacodingclassifyingandreviewingtheliterature AT joaoascenso neuromorphicvisiondatacodingclassifyingandreviewingtheliterature |