A Compound Image Encoder based on the Multiscale Recurrent Pattern Algorithm

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 trans form-based cornpression 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.