Within the framework of telemedicine, the amount of images leads first to use efficient lossless compression methods for the aim of storing information. Furthermore, multiresolution scheme including Region of Interest processing is an important feature for a remote access to medical images. Moreover, the securization of sensitive data (e.g. metadata from DICOM images) constitutes one more expected functionality: indeed the lost of IP packets could have tragic effects on a given diagnosis. For this purpose, we present in this paper an original scalable image compression technique (LAR method) used in association with a channel coding method based on the Mojette Transform, so that a hierarchical priority encoding system is elaborated.
The LAR (Locally Adaptive Resolution) coder, based on an non-uniform subsampling of the image, is a multi-layered scheme that provides just as well lossless representation of data as very low-bit rates encoded images. The Mojette transform technique realizes multiple description of information elements in a very low order of complexity. These descriptions are transmitted without adding any specific mechanism for regulating flows purpose. This global system provides a solution for secured transmission of medical images through low-bandwidth networks such as the Internet.
To meet QoS for multimedia transmission over packet-lossy network such as IP networks, two ways can be followed, either the source scalability is extended to packets, or multiple description schemes are used. In this case, equivalence between packets is assumed and forward error correction is needed. In this paper the proposed solution allows multiple description of a scalable bitstream source using a backprojection operator. This operator belongs to the class of the Mojette transforms, already presented in ITCom2001. In this scheme a redundant projections set is firstly computed for different angles. In a second step only few projections are selected to check the reconstructibility (quantization step). Third an entropic coding on remaining projections is applied. The Mojette transform is an exact discrete Radon transform generating bins from
ixels (information elements) computed as XOR or standard additions. This transform is linear (in number of pixels and number of projections) both for coding and decoding. In this new scheme we propose, sub flows (when assuming source scalability) issued from the application output bitstreams are mapped into buffers. The projections issued from these buffers meet both the compression of the bitstreams and the
multiple descriptions constraints.
The Mojette transform is a discrete projector generating bins from ixels (information elements) values. The initial bitstream is first rearranged into a 2D or n-dimensional box of ixels. Each bin value (belonging to a (n-1)-dimensional projection) is computed as the XOR addition of ixels belonging to a discrete line which fix the projection direction. The major advantage of this transform is the linear complexity (both in the number of ixels and the number of projections) for encoding and decoding. Each packet contains a projection, and additional projections can be computed without slowing the network flow. The size of this n-dimensional buffer is tuned according to the real- time constraints and to the desired packet size.
Video (and other multimedia sources) distribution starts to implement industrial solutions that supposes no quality of service (QoS) properties for the network. To overcome congestion problems in the core of a worldwide Internet network, mirrors sites at the edges of the network are dispatched. Thus the QoS problem is only relevant for the network extremities. Nevertheless, this strategy implies to replicate the multimedia database (denoted at MDB) at multiple edge points to meet the real-time constraints and to establish specific mechanisms between mirror sites to satisfy customer needs as for video distribution. For each of both kind of constraints, we propose a unique data/network representation.