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Abstract
Image compression lS an important tool to store and transmit visual
information that used in several applications such as: satellite, remote sensing,
multimedia communications, television broadcasting, internet, etc... . The
necessity of compression process is because of the huge amount of transferred
data in most of the applications, which exceeds the capability of today’s
hardware.
Compression of an image refers to a process in which the amount of data
used to represent an image is reduced to meet a bit rate requirement (below or at
most equal the maximum available bit rate), while the quality of the
reconstructed image satisfies the requirements for a certain application and the
complexity of the computation involved is affordable for the application.
The concept of Progressive Image Transmission (PIT) is of particular
importance in browsing large image files. Progressive transmission of an image
permits the initial reconstruction of an approximation followed by. a gradual
improvement of quality in the image reconstruction. In order to send image data
progressively, the data should be organized in pyramidal form according to the
order of its importance, from the global characteristics of an image to the local
etails. For building this pyramidal organization of data the spatial encoding, or
yramidal encoding, is used. Hence, the pyramidal encoding method generates a
et of image frames at different resolutions; the image is successively reduced in
patial resolution and size by subsampling or averaging. Approximation of an
age can be obtained using a single frame or a combination of frames of the
age, therefore sending a set of image frames in pyramid form from bottom to
p naturally constitutes a progressive transmission.
n this thesis, the development of new image compression methods based on
erging different Neural Networks (NN’s) and Inverse Difference Pyramidal