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To address this issue, we propose a novel intelligent LF transmission approach integrating enhanced resampling reconstruction and angular attention estimation. The proposed enhanced resampling reconstruction method reduces spatial redundancy by downsampling before transmission and upsampling afterward, aided by a pre-calculated residual map to minimizing quality loss. To reduce angular redundancy while preserving perceptual quality, we designed a LF angular attention estimation network employing specifically designed angular attention kernels to guide differential transmission within the angular domain. To train this network, we construct the first LF eye-tracking dataset, featuring numerous samples, diverse realistic scenes, and a wealth of data elements, particularly real user angular attention data, establishing it as a key benchmark in LF transmission. Extensive experiments demonstrate that our transmission strategy decreases transmission time by 97.3% with only a 3.1% perceptual quality loss. Subjective experiments further confirm the superior performance of our method. An implementation of this article and the constructed dataset are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/VincentQQu\/LF-Transmission\">https:\/\/github.com\/VincentQQu\/LF-Transmission<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3716168","type":"journal-article","created":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T03:59:01Z","timestamp":1738382341000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Light Field Transmission via Enhanced Resampling Reconstruction and Angular Attention Estimation"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1098-9793","authenticated-orcid":false,"given":"Zongyou","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6648-5050","authenticated-orcid":false,"given":"Qiang","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney","place":["Sydney, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7364-8325","authenticated-orcid":false,"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4947-4832","authenticated-orcid":false,"given":"Zeke Zexi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney","place":["Sydney, Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7503-3021","authenticated-orcid":false,"given":"Xiaoming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Beijing Technology and Business University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Stanford Lytro Light Field Archive","author":"Raj Abhilash Sunder","year":"2017","unstructured":"Abhilash Sunder Raj, Michael Lowney, Raj Shah, and Gordon Wetzstein. 2017. 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