Progressive Geometry Compression Base On Wavelet Transform

Three-dimensional (3D) meshes, As one of the representation of 3D objects, have been widely used in computer graphics. They often require a huge amount of data for storage and transmission in the raw data format. In order to adapt bitstreams to the heterogeneity and variation of network bandwidth and different graphics processing capabilities of network terminals, the concepts of space scalability, rate scalability and resolution scalability are introduced into mesh compression. A view-dependent mesh compression method which can generate the bitstreams with both rate scalability and resolution scalability is expected.An algorithm for wavelet based progressive geometry compression is proposed. Geometric models are presented as subdivision surfaces and their control meshes are partitioned into multiple blocks. After the wavelet transformation, bit-plane encoding is used to encode the wavelet coefficient of each block separately, resulting in multiple progressively compressed streams. From the results, one can reconstruct geometric models with different quality and different resolutions at different parts as governed by view parameters. Experimental results show that the algorithm can decrease the network bandwidth occupation effectively.In the first chapter, introduce the background and main methods of the mesh compression, and tell the theory of these algorithms, at last give a brief introduction of our work.The second chapter, the theory of wavelet transform is introduced. At last, the application of wavelet transform in image compression is illuminated.In the third chapter, introduce the concept and main theory of geometry compression. Then represent some algorithms about geometry compression.The fourth chapter, we study the progressive geometry compression (PGC) method and ameliorate it. The reformative method can decode view-dependent. Finally, we represent a new algorithm which can generate the bitstreams with rate scalability, space scalability and resolution scalability.The fifth chapter, the main results are proposed and the main points in future work are briefly introduced.