{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:57:25Z","timestamp":1772243845908,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"abstract":"<jats:p>Image processing is traditionally a very compute-intensive process. In case of stringent timing constraints, traditional approaches scale down application quality, therefore, compromising the visual clarity of processed images. In order to overcome such drawbacks, we follow a new resource-aware parallel computing paradigm called invasive computing in this paper, where an application can dynamically claim, execute, and release resources on a multi-core computing system. In this context, we show how an invasive image processing application is able to make run-time tradeoffs of quality or throughput depending on the requirements and the number of available processing resources. As a target architecture, a class of massively parallel architectures called tightly-coupled processor arrays is chosen, to show the adaptivity provided by invasive computing. The applications gain the ability to fulfill constraints in two directions: a) In case of strict throughput requirements, the image quality may be adjusted in dependence on the number of available resources by run-time selection and loading of different algorithmic kernels, e.g., image filters in dependence of the success of invasion. b) Alternatively, a certain level of quality can be guaranteed by dynamically adjusting the throughput with respect to available resources.<\/jats:p>","DOI":"10.3233\/978-1-61499-381-0-53","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T10:30:51Z","timestamp":1739961051000},"source":"Crossref","is-referenced-by-count":0,"title":["Exploitation of Quality\/Throughput Tradeoffs in Image Processing through Invasive Computing"],"prefix":"10.3233","author":[{"family":"Tanase Alexandru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Lari Vahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Hannig Frank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Teich J&uuml;rgen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Advances in Parallel Computing","Parallel Computing: Accelerating Computational Science and Engineering (CSE)"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T10:50:43Z","timestamp":1739962243000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0927-5452&volume=25&spage=53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-381-0-53","relation":{"is-cited-by":[{"id-type":"doi","id":"10.1007\/978-981-13-8387-8_2","asserted-by":"object"},{"id-type":"doi","id":"10.1007\/978-981-10-1058-3_1","asserted-by":"object"}]},"ISSN":["0927-5452"],"issn-type":[{"value":"0927-5452","type":"print"}],"subject":[],"published":{"date-parts":[[2014]]}}}