A NEW TERRAIN DATA COMPRESSION SCHEME FOR

INTERACTIVE TERRAIN VISUALIZATION SYSTEMS

Ricardo Olanda, Mariano Pérez and Juan Manuel Orduña

Departamento de Informática, University of Valencia, Valencia, Spain

Keywords: Virtual reality, Terrain data compression, Interactive terrain visualization, JPEG2000.

Abstract: Over the last years, there has been a great development on real time terrain visualization applications using

remote databases. One of the main problems that these applications must face is the storage and

transmission of terrain data. Despite the considerable bandwidth increase of internet connection during last

years, the large amount of data to be transmitted can easily saturate these connections. On other hand, since

data must be stored in the client side, clients need a considerable storage space. In this context, we propose a

new compression scheme that solves or minimizes these problems. It is based on JPEG2000 standard;

however, the wavelet analysis and synthesis algorithms are modified to allow efficient transmission and

reconstruction of terrain data by using tiled pyramids multiresolution techniques. A comparative study

including current techniques shows that the proposed scheme obtains a better compression ratio of the

terrain data, reducing the storage space and transmission bandwidth needed, achieving a better visual quality

of terrain data reconstructed after data decompression.

1 INTRODUCTION

Real-time terrain visualization is a very active

research field in the area of computer graphics.

Some applications examples in this area are driving

or flight simulator, cartography applications or

virtual worlds visualization.

These applications usually use a client-server

architecture to transmit terrain data over the Internet.

In this architecture, the terrain visualization

component runs on the local host (client) and the

terrain database component runs on a remote server.

In order to visualize the terrain data, the most

commonly used strategy is to define a

multiresolution hierarchy for a data set, splitting

each level of resolution of the terrain data into a set

of fixed-size regular tiles (rectangular non-

overlapping blocks). This process is known as tiling.

All the tiles form a tiled pyramid as shown in

Figure 1 - a. This figure shows four resolution levels

of a terrain dataset. Each resolution level is split into

tiles of the same size, so the number of tiles

decreases with the resolution level.

In order to visualize the terrain data, initially a

lower resolution tile is transmitted. If more

resolution is needed, this tile is replaced with the

next level of resolution tiles. This process is repeated

until required visual quality of the terrain is reached.

This structure was initially used by Goss and

(Yuasa, 1998) and (Cline and Egbert, 1998). This,

and similar structures (Okamoto, de Mello and

Esperança, 2008) have been used later in multiple

works because allow a progressive and independent

transmission of the different terrain regions.

The data structures commonly used in these

applications are heightmaps, that describe the terrain

surface, and texture images that provide a more

realistic appearance of the terrain. Both of them can

be encoded as an image. Therefore, techniques used

for texture compression can also be applied to

compress the heightmap data.

JPEG2000 image compression standard (JPEG

Committee, 2004) has been proved to be one of the

best image compression algorithms available for the

moment. However, in order to use this standard in a

terrain visualization application, several problems

must be solved. One of these problems is due to the

fact that the original JPEG2000 standard does not

properly suit the tiled pyramid scheme used to

organize data in terrain visualization applications.

In this paper, we propose a new data

compression scheme for remote terrain visualization

systems. We have performed a comparative studio

280

Olanda R., Pérez M. and Orduña J..

A NEW TERRAIN DATA COMPRESSION SCHEME FOR INTERACTIVE TERRAIN VISUALIZATION SYSTEMS.

DOI: 10.5220/0003324402800285

In Proceedings of the International Conference on Computer Graphics Theory and Applications (GRAPP-2011), pages 280-285

ISBN: 978-989-8425-45-4

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

including current techniques. The results show that

the proposed compression scheme obtains a better

compression ratio of the terrain data, reducing the

storage space and the required transmission

bandwidth and providing a better visual quality of

terrain data in real time visualization. Moreover, this

new scheme fits the tiled pyramid that terrain

visualization applications usually use to manage

large terrain databases.

2 RELATED WORK

Nowadays, compression schemes based on discrete

wavelet transform (DWT) have been proved to be

the most efficient way to compress images. Some of

these schemes can be found in the survey done by

Sudhakar, Karthiga and Jayaraman (2005).

Several authors have combined data compression

methods with multiresolution schemes to reduce the

bandwidth and memory requirements for terrain

visualization applications. For example, Gioia,

Aubault and Bouville (2004) and Kim and Ra (2004)

use wavelet transform for geometry reconstruction

of the terrain over the network, and Royan, Gioia,

Cavagna and Bouville (2007) use wavelet transform

for coding and transmitting the terrain geometry.

These works use different techniques that are

able to provide high rates of compression,

progressive transmission and random spatial access.

Among them, the JPEG2000 standard compression

scheme can be emphasized. One example of its use

is the work of Lin, Huang and Chen (2007), that

uses JPEG2000 standard for 3D geometric objects

compression and transmission.

Many other authors have developed compressed

geometry representations, but in most cases they are

oriented to achieve high compression rates instead of

focusing on fast algorithms, more suitable for real

time visualization (Alliez and Gostman, 2005).

3 DATA COMPRESSION

As it was discussed in the previous section,

JPEG2000 standard is one of the best image

compression schemes. The purpose of our work is

the adaptation of JPEG2000 standard for its use in

terrain visualization applications, and the evaluation

of its performance with respect to current

techniques. For comparison purposes, we have

implemented four possible strategies.

3.1 Strategy 1: No Information Reused

This strategy consists in generating a different image

for each level of resolution of the tiled pyramid

(Figure 1 - a). Each one of these images are split in

tiles, which are transmitted and decompressed in an

independent way, as isolated images, without using

information of tiles belonging to lower levels of

resolution, which were previously transmitted.

The total amount of data for the whole tiled

pyramid represents an overhead of 33 % with

respect to the image with the higher resolution level

of the tiled pyramid (Goss and Yuasa, 1998).

This strategy is usually used for terrain

visualization in applications like Google Earth

(Google, 2010).

3.2 Strategy 2: JPEG2000 with Tiling

JPEG2000 standard allows splitting an original

image into a group of smaller regular images that are

then compressed and transmitted in an independent

way. The compression/decompression scheme of

JPEG2000 tiling is shown in Figure 2. In this

compression scheme, the image is split into tiles and

the wavelet transform is applied to each tile in an

independent way (JPEG Committee, 2004).

a b

Figure 1: a) Tiled pyramid with 4 different resolution

levels. b) JTiles in Tiled Pyramid.

We will denote the tiles generated by the

JPEG2000 compression scheme as JPEG2000 tiles

(JTiles), in order to avoid confusion with the tiles

used in a tiled pyramid.

The main problem of this scheme is that the

number of JTiles generated at every resolution level

is always the same, and its size is decreased (Figure

1 - b), so this scheme do not match with the tiles

used in a tiled pyramid (where the number of tiles

decrease and its size remains constant) and it will be

necessary to transmit and combine several of these

JTiles to obtain an equivalent tile of the tiled

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pyramid. These differences can be observed

comparing Figure 1 - a and Figure 1 - b.

Figure 2: JPEG2000 standard with tiling scheme, a)

Compression, b) Reconstruction.

This strategy is used by Hayat et al. (2010) to do

online 3D terrain visualization.

3.3 Strategy 3: JPEG2000 without

Tiling

If the tiling process is not used, then JPEG2000

generates only one JTile for each resolution level of

the tiled pyramid. If a region of this JTile is

reconstructed in an independent way, visual artifacts

appear at the borders of these regions, as we will

show in section 4.1.

This is due to the fact that in the analysis process

JPEG2000 uses information different from the used

in the analysis process and in the synthesis process.

In the analysis process, all the image data are used to

generate all the wavelet coefficients, but in the

synthesis process (in order to reconstruct a tile of the

tiled pyramid in an independent way) only the

coefficients placed inside the region matching the

tile are used to reconstruct this terrain region,

without using neighboring coefficients that were

used in the analysis process.

The compression/decompression scheme is the

same shown in Figure 2, but now without using the

tiling process.

3.4 Strategy 4: New Scheme

In order to solve the problems of the previous

strategies, we have modified the compression

scheme as is shown in Figure 3 - a. This scheme is

similar to the one used by JPEG2000 standard; the

main differences are the image tiling process and the

wavelet transform application.

A wavelet analysis example following this

scheme is shown in Figure 4. Before each wavelet

transform step is applied, the image is split into

several tiles of fixed size (Figure 4-a). (JPEG2000

standard initially splits the image into JTiles only

once). Then, a 2D analysis filter is applied to each

tile, generating four coefficients sub-bands: a low-

pass sub-band (LL) and three high-pass sub-bands

(HL, LH and HH) (Figure 4-b). The coefficients of

the high-pass sub-bands are grouped into code-

blocks, quantized and entropy encoded in an

independent way, similarly to the way JPEG2000

standard does. Before a new wavelet transform step

takes place, all low-pass sub-bands (LL) are grouped

into a lower resolution version of the image (Figure

4-c). This process is repeated until this lower

resolution version size is smaller than the tile size

(Figure 4-d, 4-e, 4-f).

Figure 3: New scheme a) Compression, b) Reconstruction.

In order to reconstruct these tiles, the inverse

process takes place in a progressive way ( Figure 3 –

b), but using the inverse DWT module properly

modified to apply only one step. Once a tile has been

reconstructed, any children tile can be reconstructed

by decompressing and applying one wavelet

transform step, joining the coefficients of the

children tiles sub-bands HL, LH and HH with data

of the father tile sub-band LL.

This tile reconstruction is perfect, without visual

artifacts in the region frontiers, because the same

coefficients are used for the synthesis and the

analysis process of the wavelet transform, avoiding

the use of coefficients that belong to neighboring

tiles in both processes, unlike the JPEG2000

standard scheme used in strategy 3. Additionally,

these modifications allow the number of generated

tiles to fit in the tiled pyramid.

These modifications slightly increases the time

required to compress the data with respect to the

original compression scheme, due to the process to

recombine the LL sub-bands. Nevertheless, the

compression of the data is done off-line, and

therefore it does not affect to the performance of the

terrain visualization application.

Compressed

Image Data

Reconstructed

Image / Tile

Tiling

Color

Transf.

DWT

(1 step)

Quant

zation

Entropy

Encoding

Source

Image

Inverse Color Tr.

Inverse

Quantization

Entropy Decoding

Inv. DWT

(1 step)

Recombine

Compressed

Image Data

Recons-

tructed

Image / Tile

Tiling

Color Transf.

DWT

Quantization

Entropy Encoding

Source

Image

Inverse Color Tr.

Inverse Quantization

Entropy Decoding

Inv. DWT

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d e f

Figure 4: Decomposition process over an image.

The time requiring for decompress the data is

similar to the original compression scheme, the

difference is that in the original scheme all the steps

of the inverse DWT were applied consecutively and

now they are applied in an independent way.

4 EVALUATION AND TESTING

A generic application of terrain visualization has

been used to compare the different compression

scheme strategies. This application consists of

performing a real time fly over different terrain

databases using different visual quality parameters.

This application allows taking top view photos and

user point of view photos of the terrain surface and

storing them for analysis.

The terrain database used for this application is

formed by a set of terrain textures and heightmaps

that have been compressed using the different

compression scheme strategies described in the

previous section.

Different tests have been performed on these

databases: visual artifacts, lossless compression ratio

and lossy compression root mean square error.

4.1 Visual Artifacts

Terrain visualization systems with remote databases

need to reconstruct every tile of the tiled pyramid in

an independent way. When strategy 3 is used

(JPEG2000 without tiling scheme) to reconstruct a

terrain region in an independent way, visual artifacts

will appear in the frontiers of these regions. These

artifacts do not appear in the other considered

strategies.

This test checks visual artifact impact for the

different strategies. In order to achieve this goal, our

terrain visualization application has taken top view

photos of the terrain database that have been lossless

compressed using the different compression

strategies. The terrain images have been

reconstructed by regions in an independent way.

Figure 5 shows a representative example of the

visual artifacts obtained when using strategy 3.

Figure 5-a shows an example of user point of view

inside terrain visualization application. Figure 5-b

shows the terrain dataset (at high resolution)

corresponding to the terrain region viewed by the

user at that moment. Figure 5-c and 5-d show the

tiles used from the tiled pyramid at that user point of

view by strategy 3 and 4 respectively. Figure 5-c

shows visual artifacts in the frontiers between terrain

regions of the same level of resolution. These visual

artifacts do not appear in Figure 5-d. These

differences have been emphasized by red ellipses.

In order to evaluate the visual quality in a

quantitative way, five resolution levels of the terrain

surface (Figure 5) have been reconstructed by tiles

of the same resolution in an independent way, and

theirs root mean square error (RMSE) have been

measured in relation to the high resolution terrain

surface.

Table 1 shows the results for the strategy 3 and

4. Strategy 1 and 2 results are similar to the strategy

4 ones, because in these strategies do not appear

visual artifacts.

Version 0 corresponds to the lowest level of

resolution, and version 4 corresponds with the

highest resolution reconstruction.

Table 1: Image Figure 5 RMSE.

Version 0 1 2 3 4

Strategy 3 83,10 71,76 58,17 38,03 13,84

Strategy 4 82,51 70,61 55,29 34,14 0,00

The error using the strategy 3 is greater than the

error obtained when others strategies are used.

Moreover, strategy 3 does not achieve a perfect

reconstruction of the image at high resolution, unlike

the rest of strategies.

This behavior is due to the fact that strategy 3

has used all image data in the analysis process, but

only the transmitted tile coefficients have been used

in the synthesis process, when also the neighbor data

would be needed. These visual artifacts are extended

in each synthesis process of the wavelet transform,

causing an imperfect reconstruction of the complete

image.

As a result, we can conclude that strategy 3 is not

valid to be used for a terrain visualization

application, due to the fact that visual artifacts will

appear, resulting in a low quality terrain

visualization.

a b c

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Figure 5: Visual artifacts image examples.

4.2 Lossless Compression Ratio

Terrain data size is an important factor, since it

affects the amount of storage required, the

bandwidth and the time required to transmit the data.

In order to analyze the compression rate that

each compression scheme can reach, a set of

different terrain images have been compressed using

different tile sizes. Some examples of these images

are shown in Figure 6.

In order to measure the compression rate, we

divide the size of the uncompressed image at full

resolution by the size of the compressed image ready

to generate the tiled pyramid.

Table 2 shows a representative example of the

compression ratio achieved for the different

strategies when applied to the Figure 6 images using

a tile size of 128 pixels. The size of these images

goes from 2048x2048 till 16384x16384 pixels.

Figure 6: Four images used for compression test.

Table 2 shows that compression ratios provided

by strategies 3 and 4 are equal, and better than those

achieved by strategies 1 and 2. This is due to the fact

that strategy 1 has to transmit different images for

each level of resolution of the tiled pyramid, and the

compressed image size will be not only the high

level resolution image compressed size, but the

addition of the size of each different resolution

image. On other hand, strategy 2 treats each tile like

an isolated image, thus limiting the amount of data

available for the compression algorithm.

As a result, we can conclude that strategy 4

scheme provide the best compression ratio without

producing visual artifact.

Table 2: Compression ratio for Figure 6 images using the

four strategies.

Image A Image B Image C Image D

Strat. 1

1,67 1,78 1,68 2,36

Strat. 2 2,37 2,52 2,34 3,45

Strat. 3 2,41 2,56 2,37 3,55

Strat. 4 2,41 2,56 2,37 3,55

4.3 Lossy Compression Root Mean

Square Error (RMSE)

Lossy compression introduces errors in each

analysis step of the wavelet transform, degrading the

reconstructed image at each reconstruction step.

Nevertheless, this type of compression can be useful

to reduce both the data size and the transmission

time.

In order to study which compression scheme

provides higher visual quality when using lossy

compression, a set of images have been compressed

using different bit rates. These images have been

decompressed using all the information of each level

of the tiled pyramid. The RMSE with respect to the

original image has been measured.

Table 3 shows the lossy compression results in a

quantitatively way. It shows the RMSE value

obtained for the different strategies using different

compression ratios for the image Figure 6-C. The

image size is 2048x2048 pixels, using a tile size of

128x128 pixels (except for strategy 3, where the

tiling process was not applied).

Table 3 shows that strategies 3 and 4 obtain

image reconstructions of similar quality (and better

than the strategies 1 and 2), although strategy 4

applies a tiling process and no tiles have been used

for strategy 3.

The reason for this behavior is that strategy 1

repeats the information that has to transmit (the bit

rate is measured taking into account all the different

resolution images inside de tiling pyramid), so the

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data size is higher than the other strategies.

Table 3: RMSE – Lossy compression.

Strat. 1 Strat. 2 Strat.3 Strat. 4

bpp RMSE RMSE RMSE RMSE

0,05 30,28 35,80 27,99 28,22

0,10 25,33 27,52 22,83 23,03

0,15 21,89 22,82 19,73 19,95

0,25 17,67 17,47 15,05 15,26

1,00 7,54 6,72 5,98 6,03

2,00 4,04 3,39 3,02 3,07

3,00 2,58 2,09 1,88 1,91

4,00 1,88 1,49 1,38 1,40

Regarding the strategy 2, each tile is treated like

an isolated image, causing a lower image quality and

block effect. This block effect appears when a low

compression ratio is used (for example 0.05 bpp).

Table 3 also shows that strategy 1 is better than

strategy 2 for lower values of bpp, but it is worst for

higher values of bpp. This is due to the fact that

strategy 2 suffers from a smaller block effect as bpp

is increased.

As a result, we can conclude that the proposed

scheme results in the best performance providing

similar lossy compression rates than strategy 3,

although it does not provide visual artifacts.

5 CONCLUSIONS

In this paper, we have proposed a new compression

scheme for terrain data to be used in remote terrain

visualization systems, performing a comparative

study with other three different strategies that can be

used in terrain visualization applications.

The performance evaluation results show that the

proposed scheme:

 Reuses previous information transmitted,

reducing the data to be transmitted.

 It obtains a good compress ratio and visual

quality.

 It can reconstruct terrain regions in an

independent way, avoiding visual artifacts in

the borders of these regions.

 It suits the tiled pyramid usually used to

organize terrain data.

Meanwhile, strategy 1 does not reuse previous

information transmitted, so it needs to transmit more

data than the other strategies. Strategy 2 obtains a

lower compress ratio and visual quality than the

other strategies, and strategy 3 produces visual

artifacts at the region borders when these regions are

reconstructed in an independent way.

These results show that the proposal scheme can

significantly improve the performance of remote

terrain visualization systems.

ACKNOWLEDGEMENTS

This work has been jointly supported by the Spanish

MICINN and the European Commission FEDER

funds under grants Consolider-Ingenio 2010

CSD2006-00046 and TIN2009-14475-C04-04.

REFERENCES

Alliez P., Gostman C., 2005. Recent advances in

compression of 3d meshes. Advances in

Multirresolution for Geometric Modelling. Springer.

Cline D., Egbert P., 1998. Interactive display of very large

textures. IEEE Visualization, 343-350.

Gioia P., Aubault O., Bouville C., 2004. Real-Time

reconstruction of wavelet-encoded meshes for view-

dependent transmission and visualization. IEEE

TCSVT, vol. 14 (7), 1009-1020.

Google, 2010. Google Earth home page. Retrieved

November 11, 2010 from http://earth.google.com.

Goss M. E., Yuasa K., 1998. Texture tile visibility

determination for dynamic texture loading.

Eurographics/Siggraph workshop on Graphics

Hardware, 55-60.

Hayat K., Puech W, Islam N., Gesquiere G., 2010.

Seamless heterogeneous 3D tessellation via DWT

domain smoothing and Mosaicking. Eurasip Journal

on Advances in Signal Processing.

JPEG Committee, 2004. JPEG2000 – ISO/IEC 15444 –

Information Technology, JPEG2000 Image Coding

System: Core Coding System.

Kim J. K., Ra J. B., 2004. A real-time terrain visualization

algorithm using wavelet based compression. The

Visual Computer, vol. 12 (2), 67-95.

Lin N. S., Huang T. H., Chen B. Y., 2007. 3D model

streaming based on JPEG2000. IEEE TCE, vol. 53 (1).

Okamoto R. M., de Mello F. L., Esperança C, 2008.

Texture management in view dependent application

for large 3d terrain visualization. Spring Simulator

conference, 641-647.

Royan J., Gioia P., Cavagna R., Bouville C., 2007.

Network-based visualization of 3d landscapes and city

models. IEEE Computer Graphics and Applications,

vol. 27 (6), 70-79.

Sudhakar R., Karthiga R., Jayaraman S., 2005. Image

compression using coding of wavelet coefficients – A

survey. GVIP journal, vol. 5 (6).

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