{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:34:35Z","timestamp":1750307675602,"version":"3.41.0"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2009,1,1]],"date-time":"2009-01-01T00:00:00Z","timestamp":1230768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Appl. Percept."],"published-print":{"date-parts":[[2009,1]]},"abstract":"<jats:p>\n            We present a novel perceptual method to reduce the visual redundancy of unstructured lumigraphs, an image based representation designed for interactive rendering. We combine features of the unstructured lumigraph algorithm and image fidelity metrics to efficiently rank the perceptual impact of the removal of subregions of input views (\n            <jats:italic>subviews<\/jats:italic>\n            ). We use a greedy approach to estimate the order in which subviews should be pruned to minimize perceptual degradation at each step. Renderings using varying numbers of subviews can then be easily visualized with confidence that the retained subviews are well chosen, thus facilitating the choice of how many to retain. The regions of the input views that are left are repacked into a texture atlas. Our method takes advantage of any scene geometry information available but only requires a very coarse approximation. We perform a user study to validate its behaviour, as well as investigate the impact of the choice of image fidelity metric as well as that of user parameters. The three metrics considered fall in the physical, statistical and perceptual categories. The overall benefit of our method is the semiautomation of the view selection process, resulting in unstructured lumigraphs that are thriftier in texture memory use and faster to render. Using the same framework, we adjust the parameters of the unstructured lumigraph algorithm to optimise it on a scene by scene basis.\n          <\/jats:p>","DOI":"10.1145\/1462048.1462050","type":"journal-article","created":{"date-parts":[[2009,2,4]],"date-time":"2009-02-04T13:01:58Z","timestamp":1233752518000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A perceptual approach to trimming and tuning unstructured lumigraphs"],"prefix":"10.1145","volume":"5","author":[{"given":"Yann","family":"Morvan","sequence":"first","affiliation":[{"name":"Graphics, Vision and Visualisation Group, Trinity College Dublin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carol","family":"O'sullivan","sequence":"additional","affiliation":[{"name":"Graphics, Vision and Visualisation Group, Trinity College Dublin"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2009,2,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383309"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/2383654.2383665"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Daly S. 1993. The visible differences predictor: an algorithm for the assessment of image fidelity. In Digital Images and Human Vision. 179--206.   Daly S. 1993. The visible differences predictor: an algorithm for the assessment of image fidelity. In Digital Images and Human Vision. 179--206.","DOI":"10.1117\/12.135952"},{"volume-title":"Rendering Techniques 98 Proc. Eurographics Workshop on Rendering.","author":"Debevec P.","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/636886.636888"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8659.00447"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237200"},{"volume-title":"Texture Re-Mapping for Decimated Polygonal Meshes","author":"Hale J. G.","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Heigl B. Koch R. Pollefeys M. Denzler J. and Gool L. J. V. 1999. Plenoptic modeling and rendering from image sequences taken by hand-held camera. In DAGM-Symposium. 94--101.   Heigl B. Koch R. Pollefeys M. Denzler J. and Gool L. J. V. 1999. Plenoptic modeling and rendering from image sequences taken by hand-held camera. In DAGM-Symposium. 94--101.","DOI":"10.1007\/978-3-642-60243-6_11"},{"volume-title":"Proc. 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Final report from the video quality experts group on the validation of objective models of video quality assessment. Tech. rep. VQEG."},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Walter B. Greenberg D. P. and Pattanaik S. N. 2002. Using perceptual texture masking for efficient image synthesis. In Eurographics Computer Graphics Forum. Vol. 21.  Walter B. Greenberg D. P. and Pattanaik S. N. 2002. Using perceptual texture masking for efficient image synthesis. In Eurographics Computer Graphics Forum. Vol. 21.","DOI":"10.1111\/1467-8659.00599"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Wang Z. Bovik A. C. and Lu L. 2002. Why is image quality assessment so difficult&quest; In Proceedings of the IEEE International Conference on Acoustics Speech &amp; Signal Processing. Vol. 4. 3313--3316.  Wang Z. Bovik A. C. and Lu L. 2002. Why is image quality assessment so difficult&quest; In Proceedings of the IEEE International Conference on Acoustics Speech &amp; Signal Processing. 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