### Table 5. Comparative evaluation of the total lossless first order entropy and the weighted entropy (in brackets) obtained with the HINT and the ORT based lossless subband coding. The images are decomposed into five multiresolution levels.

### Table 7: Bit rates (bpp) for lossless recovery when the proposed entropy-coding methods were applied to the S+P lossless multiresolution transform.12

1997

"... In PAGE 11: ... The execution speed is similar to that of binary-uncoded SPIHT. Table7 shows the results for lossless compression. The best results were obtained with the method called LSS, which employs basically the scheme of example 3, but depending on the data it may not use a mask for all partitioned sets.... ..."

Cited by 12

### Table 5: Results for uniform grid and multiresolution data re- duction techniques

2001

"... In PAGE 7: ... code automatically determined the best decomposition to min- imize one of the two different error criteria defined in Section 2: the standard deviation, n, in the leaf octants, and the maxi- mum deviation, en, in the leaf octants. In Table5 , we report the the average n and maximum en over all octants for each case. The first value gives a measure of the overall fidelity of the re- duced data set to the original data set; the latter value gives a worst-case measure of fidelity.... ..."

Cited by 3

### Table 2: Surface multiresolution

1998

"... In PAGE 12: ... Finally, gure 15 shows a multiresolution of a medical model (an image of the voxelization of a skull from a computerized tomography). Table2 lists the same parameters as the previous table, but here for the surface models.... ..."

Cited by 2

### Tables 1 and 2 evaluate the compression efficiency of our approach compared to other methods mentioned in the literature. The proposed method is competitive with the single-resolution compres- sion approach of [TG98] in terms of geometry compression. The added functionality of progressive multiresolution mesh reconstruction comes at higher cost for connecitivity encoding due to the com- plexity of the multiresolution model. Our approach outperforms other multiresolution compression methods [CLR99, BPZ99] mainly in geometry encoding. Note that our method and the method in [CLR99] use a very similar geometry prediction and error coding mechanism. However, in [CLR99] the simplification process is not driven by an error metric, and their entropy coder depends on sym- bol statistics. The compressed progressive meshes (CPM) method presented in [PR99] compresses slightly better on average than the presented approach, however, at much higher processing cost for geometry prediction.

### Table 3: Multiresolution construction time.

### Table 3. Multiresolution recognition results

1995

### Table 1: Curve multiresolution: Africa

"... In PAGE 7: ... We have developed a simple algorithm for that conversion. Table1 lists the dimensions of the domain, the number of control points stored (spatial indices of I) and the number of defined nodes (same order of magnitude as the Node-Collection G) for each resolution level. The number of coefficients in the wavelet transform of Africa (Africa in the lowest resolution level plus all the detail data produced in the decomposition process) is 27116, 22.... ..."

### Table 2: Surface multiresolution: Skull

### Table 1: Basic Multiresolution Optimization Algorithm (OF-Basic). Essential parameters are the coarse scale jmax, the motion eld smoothness parameter , and the quantizer step size at scale jmax.

1997

"... In PAGE 12: ...Table1 ; de- tailed explanations and derivations may be found in the Appendix. The steps of the algorithm are outlined as follows.... In PAGE 16: ...The algorithm begins by creating the lowpass pyramids for the current and previous frames. The motion eld at the coarsest level, dimax, is estimated and encoded using the multiscale representation (4) and the basic algorithm in Table1 . At every level (0 i lt; imax), the following steps are performed.... In PAGE 17: ... The adaptive arithmetic coder described in [45] was used to entropy-code the quantizer outputs and obtain the quoted bit-rates. Our OF-CP algorithm used two levels in the control pyramid (imax = 1), and the following parameters for the basic motion estimator ( Table1 ) at each level: jmax = 4, = 500 and = 0:125. All coe cients at the nest level (j = 0) of the HFE pyramid were set to zero.... ..."

Cited by 16