Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96497
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dc.contributor.authorYang, Huanen
dc.contributor.authorLin, Weisien
dc.contributor.authorDeng, Chenweien
dc.date.accessioned2013-07-22T03:09:06Zen
dc.date.accessioned2019-12-06T19:31:27Z-
dc.date.available2013-07-22T03:09:06Zen
dc.date.available2019-12-06T19:31:27Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationYang, H., Lin, W., & Deng, C. (2012). Learning based screen image compression. 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP).en
dc.identifier.urihttps://hdl.handle.net/10356/96497-
dc.description.abstractThere are usually two components in computer screen images: textual and pictorial parts. The pictorial part can be compressed efficiently by classical coding approaches (e.g. JPEG, JPEG2000), while the compression of the textual part is still far away from being satisfactory for the reason that the textual content is usually of high-frequency. In this paper, a learning approach is used to construct a tailored dictionary for text representation. Based on the learned dictionary, a novel screen image compression algorithm is proposed through adopting different basis functions for the textual and pictorial components respectively. The screen images are firstly segmented into textual and pictorial parts. Then we employ traditional discrete cosine transformation (DCT) to facilitate the compression of pictorial part, while the learned dictionary is used to represent the textual part in screen images. Experimental results demonstrate the effectiveness of the proposed compression algorithm.en
dc.language.isoenen
dc.rights© 2012 IEEE.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleLearning based screen image compressionen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.conferenceIEEE International Workshop on Multimedia Signal Processing (14th : 2012 : Banff, Alberta, Canada)en
dc.identifier.doi10.1109/MMSP.2012.6343419en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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