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- Publisher Website: 10.1109/TSP.2013.2290508
- Scopus: eid_2-s2.0-84893398483
- WOS: WOS:000330771300017
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Article: Reducing artifacts in JPEG decompression via a learned dictionary
Title | Reducing artifacts in JPEG decompression via a learned dictionary |
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Authors | |
Keywords | total variation decompression primal-dual algorithm learned dictionary JPEG |
Issue Date | 2014 |
Citation | IEEE Transactions on Signal Processing, 2014, v. 62, n. 3, p. 718-728 How to Cite? |
Abstract | The JPEG compression method is among the most successful compression schemes since it readily provides good compressed results at a rather high compression ratio. However, the decompressed result of the standard JPEG decompression scheme usually contains some visible artifacts, such as blocking artifacts and Gibbs artifacts (ringing), especially when the compression ratio is rather high. In this paper, a novel artifact reducing approach for the JPEG decompression is proposed via sparse and redundant representations over a learned dictionary. Indeed, an effective two-step algorithm is developed. The first step involves dictionary learning and the second step involves the total variation regularization for decompressed images. Numerical experiments are performed to demonstrate that the proposed method outperforms the total variation and weighted total variation decompression methods in the measure of peak of signal to noise ratio, and structural similarity. © 1991-2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/276978 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 2.520 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chang, Huibin | - |
dc.contributor.author | Ng, Michael K. | - |
dc.contributor.author | Zeng, Tieyong | - |
dc.date.accessioned | 2019-09-18T08:35:14Z | - |
dc.date.available | 2019-09-18T08:35:14Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | IEEE Transactions on Signal Processing, 2014, v. 62, n. 3, p. 718-728 | - |
dc.identifier.issn | 1053-587X | - |
dc.identifier.uri | http://hdl.handle.net/10722/276978 | - |
dc.description.abstract | The JPEG compression method is among the most successful compression schemes since it readily provides good compressed results at a rather high compression ratio. However, the decompressed result of the standard JPEG decompression scheme usually contains some visible artifacts, such as blocking artifacts and Gibbs artifacts (ringing), especially when the compression ratio is rather high. In this paper, a novel artifact reducing approach for the JPEG decompression is proposed via sparse and redundant representations over a learned dictionary. Indeed, an effective two-step algorithm is developed. The first step involves dictionary learning and the second step involves the total variation regularization for decompressed images. Numerical experiments are performed to demonstrate that the proposed method outperforms the total variation and weighted total variation decompression methods in the measure of peak of signal to noise ratio, and structural similarity. © 1991-2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Signal Processing | - |
dc.subject | total variation | - |
dc.subject | decompression | - |
dc.subject | primal-dual algorithm | - |
dc.subject | learned dictionary | - |
dc.subject | JPEG | - |
dc.title | Reducing artifacts in JPEG decompression via a learned dictionary | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSP.2013.2290508 | - |
dc.identifier.scopus | eid_2-s2.0-84893398483 | - |
dc.identifier.volume | 62 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 718 | - |
dc.identifier.epage | 728 | - |
dc.identifier.isi | WOS:000330771300017 | - |
dc.identifier.issnl | 1053-587X | - |