In this paper we present the current state of the project SCODE (Scanned COmpound Document Encoder).
The objective of this project is the development of a new image application, based on the Multidimensional
Multiscale Parser algorithm (MMP), for compression of scanned documents, composed by pictures, graphs
and text.
MMP is a generic compression algorithm that has been successfully applied in image coding. The use of
a multiscale adaptive pattern matching coding paradigm allows it to achieve good results, consistently, for
both smooth and text images. On the contrary, the traditional transform-based methods have a well known
performance deficit for non-smooth image coding.
Current state-of-the-art compound image coding schemes rely on the use of segmentation techniques to split
foreground and background planes of an input image. The performance of such methods, generally, degrades
with the loss of efficiency of the segmentation process, namely for complex documents or low quality scans.
These losses result from the use of transform-based compression for the background layer, like in DjVu or
JPEG2000/Part6. The flexibility of MMP algorithm makes it efficiency independent of the segmentation
process. Our experimental results show that MMP already outperforms some state-of-the-art algorithms, thus
proving its usefulness as a compound image encoding algorithm.
In this paper we present the current results and the developed coding schemes, as well as an overview on the
future work for this project.