Most systems that support the visual interaction with 3D models use shape representations based on triangle meshes. The size of these representations imposes limits on applications, where complex 3D models must be accessed remotely. Techniques for simplifying and compressing 3D models reduce the transmission time. Multi-resolution formats provide quick access to a crude model and then refine it progressively. Unfortunately, compared to the best non-progressive compression methods, previously proposed progressive refinement techniques impose a significant overhead when the full resolution model must be downloaded. The CPM (Compressed Progressive Meshes) approach proposed here eliminates this overhead. It uses a new “patching ” technique, which refines the topology of the mesh in batches, which each increase the number of vertices by up to 50%. Less than 4 bits per triangle encode where and how the topological refinements should be applied. We estimate the position of new vertices from the positions of their topological neighbors in the less refined mesh using a new estimator that leads to representations of vertex coordinates that are 50 % more compact than previously reported progressive geometry compression techniques. 1.

XML documents are extremely verbose since the "schema" is repeated for every "record" in the document. While a variety of compressors are available to address this problem, they are not designed to support direct querying of the compressed document, a useful feature from a database perspective. In this paper, we propose a new compression tool called XGrind, that directly supports queries in the compressed domain. A special feature of XGrind is that the compressed document retains the structure of the original document, permitting reuse of the standard XML techniques for processing the compressed document. Performance evaluation over a variety of XML documents and user queries indicates that XGrind simultaneously delivers improved query processing times and reasonable compression ratios.

Our thesis is that high compression efficiency for text and images can be obtained by using sophisticated statistical compression techniques, and that greatly increased speed can be achieved at only a small cost in compression efficiency. Our emphasis is on elegant design and mathematical as well as empirical analysis. We analyze arithmetic coding as it is commonly implemented and show rigorously that almost no compression is lost in the implementation. We show that high-efficiency lossless compression of both text and grayscale images can be obtained by using appropriate models in conjunction with arithmetic coding. We introduce a four-component paradigm for lossless image compression and present two methods that give state of the art compression efficiency. In the text compression area, we give a small improvement on the preferred method in the literature. We show that we can often obtain significantly improved throughput at the cost of slightly reduced compression. The extra speed c...

Arithmetic coding, in conjunction with a suitable probabilistic model, can provide nearly optimal data compression. In this article we analyze the effect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding implementations to reduce time and storage requirements; it also introduces a recency effect which can further affect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant way the effect that periodic scaling has on the code length. We explain why and by how much scaling increases the code length for files with a homogeneous distribution of symbols, and we characterize the reduction in code length due to scaling for files exhibiting locality of reference. We also give a rigorous proof that the coding effects of rounding scaled weights, using integer arithmetic, and encoding end-of-file are negligible.

We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-efficient, approximate arithmetic coder with only minimal loss of compression efficiency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme.

system for the embedded coding of 3D oceanographic images. These images differ from those arising in other applications in that valid data exists only at grid points corresponding to sea; grids points that cover land or lie beyond the bathymetry have no associated data. For these images, the WAVAL system employs a 3D lifting wavelet transform tailored specifically to the potentially sparse nature of the data by processing only the valid sea data points in between land masses. We introduce successiveapproximation runlength (SARL) coding, an embedded-coding procedure which adds successive-approximation properties to the well known stackrun (SR) algorithm. SARL is employed to code wavelet coefficients resulting from the 3D transform in the WAVAL system; however, it is a general technique applicable to other coding tasks in which embedded coding is desired but for which zerotree-techniques are impractical. Experimental results show that the WAVAL system achieves substantial improvement in rate-distortion performance over the technique currently used by the US Navy for compression of oceanographic imagery. I.

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........................................................................................................................... x CHAPTER 1: INTRODUCTION ............................................................................................ 1 CHAPTER 2: IMAGE COMPRESSION ................................................................................. 3 2.1 INTRODUCTION ........................................................................................................... 3 2.2 LOSSLESS COMPRESSION METHODS ........................................................................... 5 2.2.1 Huffman Coding ................................................................................................. 5 2.2.2 Arithmetic Coding .............................................................................................. 5 2.2.3 Lempel-Ziv Compression ................................................................................... 6 2.2.4 Run Length Encoding ................

XML documents are extremely verbose since the "schema" is repeated for every "record" in the document. While a variety of compressors are available to address this problem, they are not designed to support direct querying of the compressed document, a useful feature from a database perspective. In this paper, we propose a new compression tool called XGrind, that directly supports queries in the compressed domain. A special feature of XGrind is that the compressed document retains the structure of the original document, permitting reuse of the standard XML techniques for processing the compressed document. Performance evaluation over a variety of XML documents and user queries indicates that XGrind simultaneously delivers improved query processing times and reasonable compression ratios.

Karel Culik and the first author have demonstrated how Weighted Finite Automata (WFA) provide a strong tool for image compression [1, 2, 3, 4]. In the present article we introduce an improved method for the last step of the compression algorithm: for compressing the WFA that approximates the given image. Our method is based on arithmetic compression of sparse matrices. 1