This paper introduces a new dynamic 3D mesh representation that provides 3D animation support of progressive display and drastically reduces the amount of storage space required for 3D animation. The primary purpose of progressive display is to allow viewers to get animation as quickly as possible, rather than having to wait until all data has been downloaded. In other words, this method allows for the simultaneous transmission and playing of 3D animation. Experiments show that coarser 3D animation could be reconstructed with as little as 150 KB of data transferred. Using the sustained transmission of refined operators, viewers feel that resolution approaches that of the original animation. The methods used in this study are based on a compression technique commonly used in 3D animation - clustered principle component analysis, using the linearly independent rules of principle components, so that animation can be stored using smaller amounts of data. This method can be coupled with streaming technology to reconstruct animation through iterative updating. Each principle component is a portion of the streaming data to be stored and transmitted after compression, as well as a refined operator during the animation update process. This paper considers errors and rate-distortion optimization, and introduces weighted progressive transmitting (WPT), using refined sequences from optimized principle components, so that each refinement yields an increase in quality. In other words, with identical data size, this method allows each principle component to reduce allowable error and provide the highest quality 3D animation.

This study proposes an edge-based single-resolution compression scheme for triangular mesh connectivity. The proposed method improves upon EdgeBreaker. Nearly all of these algorithms are either multiple traversals or operate in reverse order. Operating in reverse order should work only off-line in the EdgeBreaker decompression process. Many restrictions on applications will be caused by these factors. To overcome these restrictions, the algorithm developed here can both encode and decode 3D models in a straightforward manner by single traversal in sequential order. Most algorithms require complicated operations when the triangular mesh is split. This study investigates spatial locality to minimize costs in split operations. Meanwhile, some simplification rules are proposed by considering geometric characteristics which ignore the last triangle when a split occurs. The proposed method improves not only the compression ratio but also the execution time.

This paper investigates the use of the affine transformation matrix when employing principal component analysis (PCA) to compress the data of 3D animation models. Satisfactory results were achieved for the common 3D models by using PCA because it can simplify several related variables to a few independent main factors, in addition to making the animation identical to the original by using linear combinations. The selection of the principal component factor (also known as the base) is still a subject for further research. Selecting a large number of bases could improve the precision of the animation and reduce distortion for a large data volume. Hence, a formula is required for base selection. This study develops an automatic PCA selection method, which includes the selection of suitable bases and a PCA separately on the three axes to select the number of suitable bases for each axis. PCA is more suitable for animation models for apparent stationary movement. If the original animation model is integrated with transformation movements such as translation, rotation, and scaling (RTS), the resulting animation model will have a greater distortion in the case of the same base vector with regard to apparent stationary movement. This paper is the first to extract the model movement characteristics using the affine transformation matrix and then to compress 3D animation using PCA. The affine transformation matrix can record the changes in the geometric transformation by using 44 matrices. The transformed model can eliminate the influences of geometric transformations with the animation model normalized to a limited space. Subsequently, by using PCA, the most suitable base vector (variance) can be selected more precisely.