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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>\n            Auto-encoder has been widely used to compress high-dimensional data such as the images and videos. However, the traditional auto-encoder network needs to store a large number of parameters. Namely, when the input data is of dimension\n            <jats:italic>n<\/jats:italic>\n            , the number of parameters in an auto-encoder is in general\n            <jats:italic>O<\/jats:italic>\n            (\n            <jats:italic>n<\/jats:italic>\n            ). In this article, we introduce a network structure called 3D Tensor Auto-Encoder (3DTAE). Unlike the traditional auto-encoder, in which a video is represented as a vector, our 3DTAE considers videos as 3D tensors to directly pass tensor objects through the network. The weights of each layer are represented by three small matrices, and thus the number of parameters in 3DTAE is just\n            <jats:italic>O<\/jats:italic>\n            (\n            <jats:italic>n<\/jats:italic>\n            1\/3). The compact nature of 3DTAE fits well the needs of video compression. Given an ensemble of high-dimensional videos, we represent them as 3DTAE networks plus some small core tensors, and we further quantize the network parameters and the core tensors to get the final compressed data. Experimental results verify the efficiency of 3DTAE.\n          <\/jats:p>","DOI":"10.1145\/3431768","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:56:03Z","timestamp":1620780963000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["3D Tensor Auto-encoder with Application to Video Compression"],"prefix":"10.1145","volume":"17","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[{"name":"School of IoT Engineering (School of Information Security), Jiangsu Vocational College of Information Technology, Wuxi, China"}]},{"given":"Guangcan","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"given":"Yubao","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"given":"Qingshan","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"given":"Shengyong","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"ISO\/IEC CD 23090-3 Versatile Video Coding document N10692 Joint Video Experts Team (JVET) of ITU-T SG 16 WP3 and ISO\/IEC JTC 1\/SC 29\/WG 11. 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