Sphit

HIGHLY SCALABLE IMAGE COMPRESSION BASED ON SPIHT FOR NETWORK
APPLICATIONS
Habibollah Danyali and Alfred Mertins
School of Electrical, Computer and Telecommunications Engineering
University of Wollongong, Wollongong, NSW 2522, Australia
Email: hd04, mertins @uow.edu.au
 

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In general, an level wavelet decomposition allows at most
levels of spatial resolution. To distinguish between different resolution levels, we denote the lowest spatial resolution level as level
. The full image then becomes resolution level 1. The three
subbands that need to be added to increase the resolution from
level to level
are referred to as level
resolution subbands (see Fig. 1). An algorithm that provides full spatial scalability would encode the different resolution levels separately, allowing a transcoder or the decoder to directly access the data needed to
reconstruct with a desired spatial resolution. The original SPIHT
algorithm, however, encodes the entire wavelet tree in a bitplane
by bitplane manner and produces a bitstream that contains the information about the different spatial resolutions in no particular
order.
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0-7803-7622-6/02/$17.00 ©2002 IEEE

2. HIGHLY SCALABLE SPIHT (HS-SPIHT)
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Traditional image coding systems have only focused on efficient
compression of image data. The main objective of such systems
is optimizing image quality at given bit rate. Due to the explosive growth of the Internet and networking technology, nowadays
a huge number of users with different capabilities of processing
and network access bandwidth can transfer and access data easily.
For transmission of visual data on such a heterogenous network,
efficient compression itself is not sufficient. There is an increasing
demand for scalability to optimally service each user according to
his bandwidth and computing capabilities. A scalable image coder
generates a bitstream which consists of a set of... [continues]

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