A new 2D image compression technique for 3D surface reconstruction

Image compression is one of the important techniques used today for image and video transmission. There are many types of image compression techniques are used these days; one of them is JPEG technique. In this research, we introduce a new idea for applying the JPEG technique with Discrete Wavelet Transform (DWT) for high-resolution images. Our image compression algorithm consists of; firstly, transform an image by single level DWT. Secondly, JPEG algorithm applied on "LL" sub-band this process is called JPEG Transformation. Thirdly, separate the final transformed matrix into DC-Array and AC-Matrix contains DC values and AC coefficients respectively. Finally, the minimize-matrix-size algorithm applied on AC-Matrix followed by arithmetic coding. The novel decompression algorithm used in this research is Parallel Sequential Search Algorithm, which is represented inverse minimize-matrix-size algorithm. The searching algorithm consist of a P pointers, all these pointers are working in parallel to find the original AC-coefficients. Thereafter, combines all decoded DC-values with the decoded ACcoefficients in one matrix followed by apply inverse JPEG transformed and inverse DWT. the technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm by using 2D and 3D RMS

3D Point Cloud Data and Triangle Face Compression by a Novel Geometry Minimization Algorithm and Comparison with other 3D Formats

Polygonal meshes remain the primary representation for visualization of 3D data in a wide range of industries including manufacturing, architecture, geographic information systems, medical imaging, robotics, entertainment, and military applications. Because of its widespread use, it is desirable to compress polygonal meshes stored in file servers and exchanged over computer networks to reduce storage and transmission time requirements. 3D files encoded by OBJ format are commonly used to share models due to its clear simple design. Normally each OBJ file contains a large amount of data (e.g. vertices and triangulated faces) describing the mesh surface. In this research we introduce a novel algorithm to compress vertices and triangle faces called Geometry Minimization Algorithm (GM-Algorithm). First, each vertex consists of (x, y, z) coordinates that are encoded into a single value by the GM-Algorithm. Second, triangle faces are encoded by computing the differences between two adjacent vertex locations, and then coded by the GM-Algorithm followed by arithmetic coding. We tested the method on large data sets achieving high compression ratios over 90% while keeping the same number of vertices and triangle faces as the original mesh. The decompression step is based on a Parallel Fast Matching Search Algorithm (Parallel-FMS) to recover the structure of the 3D mesh. A comparative analysis of compression ratios is provided with a number of commonly used 3D file formats such as MATLAB, VRML, OpenCTM and STL showing the advantages and effectiveness of our approach