000174390 001__ 174390
000174390 005__ 20181203022620.0
000174390 0247_ $$2doi$$a10.1109/TIP.2011.2181525
000174390 022__ $$a1057-7149
000174390 02470 $$2ISI$$a000302181800042
000174390 037__ $$aARTICLE
000174390 245__ $$aSparse Approximation Using M-Term Pursuit and Application in Image and Video Coding
000174390 260__ $$bInstitute of Electrical and Electronics Engineers$$c2012
000174390 269__ $$a2012
000174390 336__ $$aJournal Articles
000174390 520__ $$aThis paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal components. The MTP algorithm provides an adaptive representation for signals in any complete dictionary. The basic idea behind the MTP is to partition the dictionary into L quasi-disjoint subdictionaries. A k-term signal approximation is then iteratively computed, where each iteration leads to the selection of M <= L atoms based on thresholding. The MTP algorithm is shown to achieve competitive performance with the matching pursuit (MP) algorithm that greedily selects atoms one by one. This is due to efficient partitioning of the dictionary. At the same time, the computational complexity is dramatically reduced compared to MP due to the batch selection of atoms. We finally illustrate the performance of MTP in image and video compression applications, where we show that the suboptimal atom selection of MTP is largely compensated by the reduction in complexity compared with MP.
000174390 6531_ $$aLTS4
000174390 700__ $$aRahmoune, Adel
000174390 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000174390 700__ $$0241061$$aFrossard, Pascal$$g101475
000174390 773__ $$j21$$k4$$q1950-1962$$tIEEE Transactions on Image Processing
000174390 909C0 $$0252393$$pLTS4$$xU10851
000174390 909C0 $$0252392$$pLTS2$$xU10380
000174390 909CO $$ooai:infoscience.tind.io:174390$$pSTI$$particle
000174390 917Z8 $$x101475
000174390 917Z8 $$x101475
000174390 937__ $$aEPFL-ARTICLE-174390
000174390 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000174390 980__ $$aARTICLE