Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256) 2001
DOI: 10.1109/acssc.2001.987029
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Improving image compression performance with balanced multiwavelets

Abstract: The multiwavelet transform-unlike the scalar wavelet transform-allows orthogonality and symmetry to coexist. For lossy image compression, the balancing order of the multiwavelet filter bank dictates energy compaction. But balancing alone does not guarantee good compression performance. Filter bank characteristics such as shi,f-variance and magnitude response also influence peak signal-to-noise ratio (PSNR) and perceived image quality. In this paper we analyze the effect of these multiwavelet properties on imag… Show more

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Cited by 7 publications
(3 citation statements)
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“…Therefore better performance is obtained when the wavelet coefficients have values clustered about the zero with little variance to avoid as much as quantization noise as possible. Thus Multiwavelets have the potential to offer better reconstructive quality of the same bit rate and can achieve better level of performance than the wavelets with a similar computational complexity [21 [28].…”
Section: Motivation Behind Multiwavelets For Compressionmentioning
confidence: 99%
“…Therefore better performance is obtained when the wavelet coefficients have values clustered about the zero with little variance to avoid as much as quantization noise as possible. Thus Multiwavelets have the potential to offer better reconstructive quality of the same bit rate and can achieve better level of performance than the wavelets with a similar computational complexity [21 [28].…”
Section: Motivation Behind Multiwavelets For Compressionmentioning
confidence: 99%
“…Some interesting cases may be found in [6,?]. See also [15,23,25,32,38,44,46,47,48,54,57,59,60,61] for more methods and applications.…”
Section: Introductionmentioning
confidence: 99%
“…It is a well-known fact that most orthogonal transforms tend to pack a large fraction of the average energy of the images into a relatively few components of the transform coefficients (energy compaction property). Some of the well-known orthogonal transforms used in image compression are the discrete cosine transform (DCT) [1,7,68,69], the Karhunen-Loeve transform (KLT) [6], the discrete Wavelet transform (DWT) [69,70] and the Walsh Hadamard transform (WHT) [71]. The KLT is an optimal transform in an information packing sense [58].…”
Section: Image Compressionmentioning
confidence: 99%