可变步长轮廓点采样

ADAPTIVE CONTOUR SAMPLING AND CODING USING SKELETON AND CURV ATURE

F.Jaillet,Y.M.Ghamri Doudane,M.Melkemi and A.Baskurt

LIGIM(Computer Graphics,Image and Modelisation Laboratory)

EA1899,University Claude Bernard,Lyon1

Bˆa t.710,43bd du11novembre1918,69622Villeurbanne cedex FRANCE

ABSTRACT

This paper presents a new binary shape sampling and cod-ing method.Within the framework of MPEG-4standard, the binary shapes in Video Object Plans have to be coded with a near to lossless approach in order to obtain a per-fect spatial and temporal localization.For this purpose,a new sampling method based on the object skeleton is de-veloped.The”r-sampling”is defined function of the Local Feature Size(LFS)which is the distance between a shape point and the closest point on the medial axis of this shape.

A weighting convex function depending on the local cur-vature is also introduced to increase the local adaptiveness of the sampling(”the r&c-sampling”).This new approach is tested on contours presenting high level details.Thefirst re-sults obtained are satisfactory in terms of both the reduction of samples and the reconstruction error.

1.INTRODUCTION

The MPEG-4standard[1]provides the means to encode audio-visual material as objects with their relations in time and space.This standardization effort has revived great interest for object video compression techniques in recent years.Within this framework,a video sequence is treated as a collection of disjoint video object plans(VOP).Each VOP is encoded as independent texture,motion and shape information.

The shape information can occupy a large part of the bit stream associated to a VOP,especially for very low bit rate applications.Otherwise,as the eye is quite sensitive to the localization of edges in time and space,the shape data is to be encoded with a”near to lossless”strategy.

A review on shape coding techniques in MPEG-4frame-work can be found in[2].The main idea remains the extrac-tion of a subset of points from the original shape(the coding process)and the interpolation(linear,spline,..)of the se-lected points for reconstructing the approximated shape(the decoding process).The methods differ by the“intelligent”way to select characteristic points in order to obtain an op-timal description of the original contour in a rate/distortion sense.

This paper addresses a new shape coding method based both on the skeleton of the object and on the local curvature of the object contour.

2.ADAPTIVE CONTOUR SAMPLING AND

CODING

The proposed method is based on an adaptive sampling of a binary shape based on the object medial axis.The adaptabi-lity here consists in having less points for the regular parts where we have less details than for the areas having more significant details.

2.1.Local Feature Size(LFS)and the r-sampling

Let be a smooth curve and afinite set of samples in.We use a fairly generous and well-defined sampling condition in.Precisely,we consider the notion of Local Feature Size(LFS)function which is used in computational geometry literature on mesh generation.This term wasfirst introduced by Ruppert[3].Based on this measure which depends on a proximity nearby feature for a point, the sampling condition quantifies the local“level of details”at a point on a smooth curve.Formally,the LFS and the sampling condition named r-sampling are defined as follows (Fig.1):

Definition1LFS(p)of a point is the Euclidean dis-tance from to the closest point on the medial axis of (Fig.1).

Definition2The curve is r-sampled by a set of samples if every point is within the distance

of a sample:

with(1) The r-sampling is used in[4]as a condition for curve re-construction from unorganized points.In next,we present a new binary shape coding algorithm based on the r-sampling adapted to the case of spatially organized points belonging to a given shape.