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This paper presents a model of image coding and delivering for multimedia and images browsing system based on a multiresolution format. The multiresolution format coding is suitable for evaluations either on server performance or on the effect on the image content, in terms of semantic and syntactic degradation. Multimedia and image browsing systems are used in image based service (IBS). Pictorial, technical, medical, geographic information management, such as home shopping and WWW service, are based on images organised in databases. In this system a large amount of resources are enslaved to image coding, transmission, decoding and showing. Considering that not every image retrieved corresponds to the user needs, a non negligible resource is unwisely used. The need for full-resolution image retrieval involves high time consumption for data retrieval and for image viewing. Likewise in a window system, the user wants to be able to resize the image frame flexibly. More techniques are available1 with different performance in terms of content maintenance and complexity, but the network load is not reduced if the resizing is realised by the client. A simple solution to guarantee the client independent service, in terms of client image-resolution, is in the storing of the images in the server in more files with different resolution of the same image, Multi File Coding (MFC), but with the image information degraded as a function ofthe downsized images and the algorithm used. It is well known that an image can be represented in mathematical form as a continuous functionf of two variables x and y. Using x and y as coordinates of the point on the screen,f is an attribute of the point (like luminance, HSV component etc.). Assuming that the information contained in one image is localized in the points where the functionfis defmed, in a picture the information is uniformly distributed over a large part of the image. On the other hand, in technical images the functionf is defined only in a small area (the plotting area),while the remaining area represents the background and is devoid of information. Resizing operations are characterized by the reduction of the image pixels (understood as a basic element of the image), likewise the information located in the image decreases as a function of the lost pixels or as a function ofthe reduction ratio2. This situation involves a degradation of the image. In the case of pictorial images the information loss is uniformly distributed and usually counterbalanced by human reasoning-driven mechanisms. So in the reduction of pictorial images the visual information loss is less than the pixel loss. In the resizing of technical images the use of symbols, thin lines and types localized in small areas involves the loss of information content.
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