Advanced SVG Rendering of Digital Images Sebastiano Battiato, Gianpiero Di Blasi, Giovanni Gallo, Salvo Nicotra Dipartimento di Matematica ed Informatica Università di Catania Viale A..Doria 6 – 95125 Catania Italy {battiato, gdiblasi,gallo,snicotra} @dmi.unict.it Giuseppe Messina STMicroelectronics – Advanced System Technology Catania Lab Imaging Group - FAB. M6, Contrada Blocco Torrazze, Casella Postale 421, 95121 Catania, Italy giuseppe.messina@st.com ABSTRACT The SVG (Scalable Vector Graphics) standard allows representing complex graphical scenes by a collection of graphic vectorial-based primitives, offering several advantages with respect to classical raster images such as: scalability, resolution independence, etc. In this paper we present a full comparison between some advanced raster to SVG algorithms: SWaterG, SVGenie, SVGWave and Vector Eye. SWaterG works by a watershed decomposition coupled with some ad-hoc heuristics, SVGenie and SVGWave use a polygonalization based respectively on Data Dependent and Wavelet triangulation, while Vector Eye is a commercial tool. All techniques have been implemented and compared between them and with Vector Eye. The results obtained by SWaterG, SVGenie and SVGWave are satisfactory both in terms of PSNR and compression ratio. Keywords SVG, Triangulation, Watershed, Wavelet, Vectorialization 1. INTRODUCTION The SVG (Scalable Vector Graphics) ([4],[9],[11]) standard allows representing complex graphical scenes by a collection of graphic vectorial-based primitives, offering several advantages with respect to classical raster images such as: scalability, resolution independence, etc.. In this work we are interested in finding some heuristic techniques to cover the gap between the graphical vectorial world and the raster real world typical of digital photography in a specific application; SVG format could find useful application in the world of mobile imaging devices, where typical camera capabilities should match with limited color/size resolutions displays. Two different techniques ([1],[3]) have been applied to approximate local pixel neighborhood by triangles: the Data Dependent Triangulation (DDT) ([5]), the Wavelet Based Triangulation (WBT) ([8]). The DDT replaces the input image with a set of triangles according to a specific cost function able to implicitly detect the edge details. The overall perceptual error is then minimized choosing a suitable triangulation. Recently further optimization of the cost function has been introduced for Color Filtering Array demosaicing ([10]) and for image interpolation ([7]) . O n the other hand the DDT is strictly connected to the original pixel positions; therefore the number of actual triangles is larger than the number of pixels. The WBT uses the wavelet multilevel transformation to properly extract the details from the input images; a reverse process of triangulation, starting from the lowest level, is applied to achieve the WBT. In other words, a triangulation is, by first, achieved at the lowest level, introducing large triangles; then the process is refined by iterating for each level, the level details of each single triangle, according to the wavelet transformation. That means increasing the quantity of small triangles into the texturized areas and fixing the amount of large triangles into the smooth areas. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Conference proceedings ISBN 80-903100-7-9 WSCG’2005, January 31-February 4, 2005 Plzen, Czech Republic. Copyright UNION Agency – Science Press