Image compression with decomposition analysis using neural networks

Image compression lS an important tool to store and transmit visualinformation that used in several applications such as: satellite, remote sensing,multimedia communications, television broadcasting, internet, etc... . Thenecessity of compression process is because of the huge amount of transferreddata in most of the applications, which exceeds the capability of today’shardware.Compression of an image refers to a process in which the amount of dataused to represent an image is reduced to meet a bit rate requirement (below or atmost equal the maximum available bit rate), while the quality of thereconstructed image satisfies the requirements for a certain application and thecomplexity of the computation involved is affordable for the application.The concept of Progressive Image Transmission (PIT) is of particularimportance in browsing large image files. Progressive transmission of an imagepermits the initial reconstruction of an approximation followed by. a gradualimprovement of quality in the image reconstruction. In order to send image dataprogressively, the data should be organized in pyramidal form according to theorder of its importance, from the global characteristics of an image to the localetails. For building this pyramidal organization of data the spatial encoding, oryramidal encoding, is used. Hence, the pyramidal encoding method generates aet of image frames at different resolutions; the image is successively reduced inpatial resolution and size by subsampling or averaging. Approximation of anage can be obtained using a single frame or a combination of frames of theage, therefore sending a set of image frames in pyramid form from bottom top naturally constitutes a progressive transmission.n this thesis, the development of new image compression methods based onerging different Neural Networks (NN’s) and Inverse Difference Pyramidal