Two-dimensional filter bank design for optimal reconstruction using limited subband information

Tirakis, Andreas; Delopoulos, Anastasios; Kollias, Stefanos

dc.contributor.author	Tirakis, Andreas	en
dc.contributor.author	Delopoulos, Anastasios	en
dc.contributor.author	Kollias, Stefanos	en
dc.date.accessioned	2014-03-01T01:10:29Z
dc.date.available	2014-03-01T01:10:29Z
dc.date.issued	1995	en
dc.identifier.issn	1057-7149	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/11401
dc.subject	Design Technique	en
dc.subject	Frequency Domain	en
dc.subject	Image Compression	en
dc.subject	Optimal Filtering	en
dc.subject	Perfect Reconstruction	en
dc.subject	Performance Optimization	en
dc.subject	Principal Component Analysis	en
dc.subject	Random Field	en
dc.subject	Two Dimensions	en
dc.subject	Filter Bank	en
dc.subject	Low Resolution	en
dc.subject	Second Order	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computational complexity	en
dc.subject.other	Frequency domain analysis	en
dc.subject.other	Image compression	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Signal filtering and prediction	en
dc.subject.other	Perfect reconstruction filter banks	en
dc.subject.other	Subband signals	en
dc.subject.other	Image reconstruction	en
dc.title	Two-dimensional filter bank design for optimal reconstruction using limited subband information	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/83.403423	en
heal.identifier.secondary	http://dx.doi.org/10.1109/83.403423	en
heal.language	English	en
heal.publicationDate	1995	en
heal.abstract	In this correspondence, we propose design techniques for analysis and synthesis filters of 2-D perfect reconstruction filter banks (PRFB's) that perform optimal reconstruction when a reduced number of subband signals is used. Based on the minimization of the squared error between the original signal and some low-resolution representation of it, the 2-D filters are optimally adjusted to the statistics of the input images so that most of the signal's energy is concentrated in the first few subband components. This property makes the optimal PRFB's efficient for image compression and pattern representations at lower resolutions for classification purposes. By extending recently introduced ideas from frequency domain principal component analysis to two dimensions, we present results for general 2-D discrete nonstationary and stationary second-order processes, showing that the optimal filters are nonseparable. Particular attention is paid to separable random fields, proving that only the first and last filters of the optimal PRFB are separable in this case. Simulation results that illustrate the theoretical achievements are presented.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	IEEE Transactions on Image Processing	en
dc.identifier.doi	10.1109/83.403423	en
dc.identifier.isi	ISI:A1995RL51100013	en
dc.identifier.volume	4	en
dc.identifier.issue	8	en
dc.identifier.spage	1160	en
dc.identifier.epage	1165	en