Summarization of paper "Compressed Random-Access Trees for Spatially Coherent Data"

1. Goals

       Randomly-accessible tree is proposed for the adaptive multiresolution hierarchies which are highly efficient at representing spatially coherent graphics data. To reduce the space and bandwidth are the main goals of this method.

 

2. Method

       In this paper, there some novel elements as follows:

l         Use of a primal-subdivision tree structure.

l         Replacement of node pointers by local offsets.

l         A forested mipmap structure

l         Lossy compression of inter-level residuals.

 

The main steps for encoding:

1)        construct the mipmap in fine-to-coarse order and compute the desired values dl with multilinear interpolation.

Figure 1: constructing mipmap

2)        compressing the tree data.

First, we need to compute the predicted value pl from parents. Then, we have to compute the residual rl = dl - pl which will be compressed using brood-base VQ resulting in compressed residual rl c . At last, we should compute the codebook for the VQ tree.

Figure 2: computing the predicted value

3)        compressing tree topology.

We process each level of the VQ tree in fine-to-coarser order, looking to prune it’s leaves under the condition that accumulated approximation errors at all affected nodes do not exceed the tolerance т .

Figure 3: pruning leaves.

 

Moreover, we need to pack the tree topology by replacing pointers to local offsets for reducing spaces.

 

3. Conclusion

       This method is not intended for detail color images, which result in dense trees and are thus better handled by block-based schemes.

This method starts with a complete tree and prunes leaves during compression. So the current tree construction procedure can only deal with small datasets.

Because this method replaces the pointers with local offsets, so it need more time for this process. Moreover this compression method is lossy.