NDLI: Minimization of boundary artifacts on scalable image compression using symmetric-extended wavelet transform

Content Provider	IEEE Xplore Digital Library
Author	Cai, W. Adjouadi, M.
Copyright Year	2004
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA (Cai, W.; Adjouadi, M.)
Abstract	Image compression techniques are routinely applied to conserve storage space and minimize bandwidth utilization in various video and communication applications. Wavelet transform is an efficient approach to reduce spatial redundancies without the annoying blocking artifacts at low bit rates. The underlying signal processing used in wavelet transform is the convolution between the decimated input signal and the wavelet filters. Since image signals are not continuous at the boundaries, problems of coefficient expansion and boundary distortion are faced in the implementation of the filtering on the finite length signal. Circular convolution instead of linear convolution can eliminate coefficient expansion but introduce boundary artifacts, especially when more levels of decompositions are involved to obtain scalable images. Symmetric-extended wavelet transform (SWT) introduced in this paper can greatly reduce boundary artifacts with improved performances in scalable image compression in comparison with the periodic-extended wavelet transform and the JPEG compression.
Sponsorship	IEEE Comput. Soc. Task Force on Information Technology for Business Application
Starting Page	598
Ending Page	602
File Size	1582077
Page Count	5
File Format	PDF
ISBN	0769521088
DOI	10.1109/ITCC.2004.1286529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Wavelet transforms Convolution Image storage Continuous wavelet transforms Bandwidth Video compression Bit rate Video signal processing Signal processing
Content Type	Text
Resource Type	Article

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