andrewd@ee.uts.edu.au (Andrew Dorrell) Guest

Posted: Tue Dec 03, 2002 11:34 am Subject: Answer: Reference missing in Daubechies' Ten lectures ? (WD 4.4 #17)




Answer: Reference missing in Daubechies' Ten lectures ? (WD 4.4 #17)
The following is a response to my question which I have not seen posted to
the list yet (although you may have received it since the last posting).
Question: Reference missing in Daubechies' Ten lectures ?
Answer:
From: "S.PITTNER Diss.Wavelets" <pittner@uranus.tuwien.ac.at>
Subject: Response concerning Daubechies reference
I only know two articles that fit to your question. They are
both contained in my bibliography and are listed below
supplemented by abstracts. I think that Ingrid Daubechies
had the first one in mind.
Stefan Pittner
M. Antonini, M. Barlaud, P. Mathieu,
Predictive Interscale Image Coding Using Vector Quantization,
in ``Signal Processing V: Theories and Applications"
(L. Torres, E. Masgrau, M. A. Lagunas, Eds.), Vol. 2, Elsevier Science
Publishers, Amsterdam, 1990, pp. 10911094.
The authors propose a new method for image compression
associating the biorthogonal wavelet transform and an interscale
prediction scheme. They use a biorthogonal wavelet transform in order
to obtain a set of images at different scales and for different
orientations. The method consists of predicting the position and the
amplitude of the edges at a given scale using the edges of the lower
scales. They also propose an interscale vector quantization scheme
which accounts for the correlation between the wavelet coefficients
inside the classification algorithm (LBG or KOHONEN neural network
algorithms).
M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies,
Image Coding Using Vector Quantization in the Wavelet Transform Domain,
Proc. of the IEEE Int. Conf. on Acoust., Speech and Signal Processing, Vol. 4,
Albuquerque, 1990, pp. 22972300.
Image compression is now essential for applications such as transmissions
and storage in databases. This paper proposes a new scheme for image
compression that takes into account psychovisual features both in the space and
frequency domains; this new method involves two steps. First, the authors use
a wavelet transform in order to obtain a set of orthonormal subclasses of
images; the original image is decomposed at different scales using a
pyramidal algorithm architecture. The decomposition is along the vertical
and horizontal directions and maintains constant the number of pixels
required to describe the image. Second, according to Shannon'/s rate
distortion theory, the wavelet coefficients are vector quantized using a
multiresolution codebook. Furthermore, to encode the wavelet coefficients
the authors propose a noise shaping bit allocation procedure which assumes
that details at high resolution are less visible to the human eyes. Finally,
in order to allow the receiver to recognize a picture as quickly as possible
at minimum cost they present a progressive transmission scheme. In fact, it
is shown that the wavelet transform is particularly welladapted to the
progressive transmission. 
