Selected Papers

Progressive Compression of Surface Light
FieldsMasaki Kitahara†, Hideaki Kimata, Kazuto Kamikura,
and Yoshiyuki Yashima
Abstract
A surface light field is a light field parameterized to the surface of a geometric model. Although it
enables the generation of high-quality images from arbitrary viewpoints, a huge amount of data isrequired. Therefore, data compression is needed for efficient transmission and storage. This paper
describes a data compression scheme that enables both progressive compression and memory-efficient
rendering and presents experimental results.

1. Introduction
Interactive three-dimensional (3-D) content is
expected to be used in next-generation visual content
applications on the Internet and other online communication applications [1]. It allows the user to freelychange views and interactively modify the propertiesof a scene. These features are suitable for many kindsof applications, ranging from virtual shopping mallsthat display homes, furniture, and other goods to virtual museums that can be used for educational purposes. One of the key technologies that will enable
the creation of such 3-D content is image-based rendering (IBR) [2].
In the IBR framework, multi-view images of ascene or an object are given as input and images takenfrom different views are generated by interpolating
the data obtained from the input images. The surface
light field (SLF) method [3] in the IBR frameworkuses the geometry of the object to render high-quality images of the object from arbitrary viewpoints. A
light field can be defined as a function that maps the
position and direction to radiance source [4]. In the
SLF method, the light field is parameterized on thesurface of the geometric model, and the SLF is given
as resampled light field data at the sampling points in† NTT Cyber Space Laboratories
Yokosuka-shi, 239-0847 Japan
E-mail: kitahara.masaki@lab.ntt.co.jp

Vol. 2 No. 8 Aug. 2004

the parameter space. Because the amount of the SLF
data is huge, data compression is needed for efficienttransmission and storage.
One highly important function required for datacompression algorithms is scalability. Although scalability can be interpreted in many ways, in this paper,
we consider signal-to-noise ratio (SNR) scalability.
Hereafter, we will refer to the SNR scalable datacompression scheme as a “progressive compression”
scheme. The texture-based coding proposed by
Magnor et al. in [5] can be used for progressive compression of the SLF. Although progressive compression is possible by their method, as reported in [5],they reconstruct the SLF with respect to every ray,prior to rendering. This requires a large amount ofruntime memory during rendering, and the data size
depends on the SLF resolution. To enable memoryefficient rendering, only the data needed for the current view should be reconstructed “on the fly” during
rendering. Magnor’s method does not provide suchfunctions.
To achieve on-the-fly reconstruction, in our progressive compression scheme we used a compressed
representation of the SLF given by a view basis andsurface coefficients, which is introduced in section 2.
Wood et al. showed that this compressed representation allows on-the-fly reconstruction while maintaining fast rendering [6]. Section 3 describes a data compression model based on this compressed representation. Our scheme enables both progressive compres27

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sion and on-the-fly reconstruction from the compressed representation. While the scheme itself does
not address any particular implementation, many are
possible. Section 4 shows an example implementation and evaluates its performance and characteristics.
2. Compressed representation using a view basis
and surface coefficients
Denoting u as an arbitrary 3-D point on the surfaceof the geometric model and w as an arbitrary direction
vector, we can define a function L(u, w) that maps (u,w) to a color (for example, red, green, and blue intensity) of the light reflected at point u on the object indirection w (Fig. 1). The SLF is a light field obtainedby discretely sampling L(u, w) with respect to (u, w).
We denote SLF L(ui, wj) as Li, j, where ui (i = 0, 1, .., I– 1) and wj ( j = 0, 1, .., J – 1) are both discrete sampling points of u and w. For simplicity, we use Li, j torepresent one component of the color. We consider
the approximation of the SLF in the following form.
K−1

Li, j ≈ ∑ ci,k b j ,k(1)1, 2). It is known that the following transformation of
the coordinate system at each point ui using the normal ni at that point can increase the coherence of the
SLF Li, j for the same direction index j [6]. Although
the formula in [6] is different from the formula presented above, the objectives of these formulas are thesame, and the formula in [6] can easily be applied tothe scheme presented in the next section.
Al, i = 2(niTAl)ni – Al

(2)

This enables a good approximation for smaller valuesof K (number of basis vectors).
In their method, Wood et al. evaluated the median–value Li of the data at each point ui on the geometricmodel (they called these median values “diffuse texture”) and derived the view basis and surface coefficients by their method principal function analysis(PFA). This can be interpreted as having ci,0 (i = 0, 1,–.., I – 1) as Li, and bj,0 = 1 for all j, and the rest of theview basis vectors and the surface coefficients can bederived by PFA. Incorporating the diffuse texture into
representation (1) ensures the recovery of the diffuse
component-like (view-independent) textures.

k =0

3. Our scheme
We call the sequence bj, k ( j = 0, 1, .., J – 1) the “viewbasis” vector and ci, k (i = 0, 1, .., I – 1) the “surface
coefficients”. We denote the axis of the coordinate
system, where directions wj are defined, as Al (l = 0,
Color L(u, w )Direction w

Surface point u

Surface of a geometric modelFig. 1. Surface light field function.

28

In our scheme, the encoded view basis and the progressively encoded surface coefficients are placed in
the bitstream progressively. In particular, the encoded
data of the diffuse texture is placed at the beginningof the bitstream. After that, the encoded data for thepair of view basis vector and surface coefficient set
for the same value of k (i.e., view basis vector bj, k ( j= 0, 1, .., J – 1) and the surface coefficient set ci, k ( i =0, 1, .., I – 1) are placed in the order of importance.
Here, “importance” means smaller total squared
reconstruction error when the pair is added to theapproximation in equation (1). The diffuse texture,each view basis, and each surface coefficient set (bothwith respect to k) are encoded independently, so thatthey can be independently decoded. For the encodingof the diffuse texture and the surface coefficients,
they are wavelet transformed and progressively
encoded to obtain a finer-grained bitstream (i.e., inorder to refine the progressively reconstructed SLF,
fewer bits need to be decoded). On the other hand, thedata size of the uncompressed view basis is verysmall compared with the surface coefficients and canbe regarded as side information for recovering the
SLF by equation (1). Hence, they are quantized andencoded by a fixed length code and placed right
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before the encoded data of the surface coefficients inthe bitstream.
The decoding and rendering can be done in the following way. The diffuse texture, the view basis, andthe surface coefficients are decoded offline prior torendering. During rendering, images from novel
views are generated directly from the compressed
representation of equation (1), where only the dataneeded for the current view is reconstructed (on-thefly reconstruction). For example, the algorithm presented in [6] can be used for rendering. In this way,complete reconstruction for all surface points ui anddirections w j can be avoided, thus reducing therequired runtime memory.
Under the compressed representation of equation(1), the optimization techniques for principal components analysis (PCA) searches for sets of optimum
view basis and surface coefficients in the meansquared error (MSE) sense, so it is a good candidate
for the optimization. Since the values for the SLF Li, jcannot be defined for ones in which the direction of
the light points inward from the geometric model, an
optimization method that can cope with the missing
data must be used. Such PCA-based methods havebeen proposed in past research, and they can be used
for the optimization in our compression model. Furthermore, the PFA proposed by Wood et al. can also
be used.
Wavelet transform is independently applied to the–
diffuse texture Li (i = 0, 1, .., I – 1) and to the surface
coefficient set for a fixed value of k (i.e., ci, k (i = 0, 1,.., I – 1)). For wavelet transform, either “first-generation wavelets (FG wavelets)” [7] or “second-generation wavelets (SG wavelets)” [8] can be used. In
either case, the surface sampling points ui (i = 0, 1, ..,I – 1) must be generated in a way in which the wavelet
transform is applicable (this must be done prior to theresampling of the SLF). For the FG wavelets, the surface of the geometric model must be parameterized toa rectangular 2D plane, and the surface must be regularly sampled with respect to the axis of the 2D plane
to obtain ui. For instance, this can be done using themethod proposed by Magnor et al. [5]. On the other
hand, to use SG wavelets, we must generate surface
sampling points ui (i = 0, 1, .., I – 1) with subdivisionconnectivity [9]. This can be done by methods such as
MAPS [9]. In the following, we denote the wavelet
transform coefficients of the diffuse texture and surface coefficients ci, k (i = 0, 1, .., I – 1 and k = 0, 1, ..,K – 1) as c'i, k.
The encoding of the wavelet transform coefficients–is processed independently for the diffuse texture Li (i= 0, 1, .., I – 1) and the surface coefficient set of fixedk (i.e., ci, k (i = 0, 1, .., I – 1)). For FG wavelets, encoding of the wavelet transform coefficients is the sameas in 2D image compression, and progressive compression algorithms such as SPIHT [10] can be used.
On the other hand, for SG wavelets, a modified version of SPIHT such as [11], can be used.
In most cases, SLF consists of color data usuallywith three color components. In these cases, i) the
derivation of the view basis and the surface coefficients, ii) the wavelet transform, and iii) the encodingof the diffuse texture, view basis, and the surface
coefficients are carried out independently for eachcolor component, and the encoded data is integrated
into one bitstream. In particular, encoded data of theview basis vector of a particular k for each color component is placed in an arbitrary order agreed upon byboth the encoder and the decoder before the corresponding encoded data of the surface coefficients.
The encoded data of the surface coefficients for eachcolor component is interleaved to preserve bitstream
progressiveness. Figure 2 explains this bitstream

SPIHT bitstream for the diffuse texture(color components are interleaved)

SPIHT bitstream for the surface coefficientsof the 2nd principal component(color components are interleaved)

Data for the view basisof the 1stprincipal component

Data for the view basisof the 2nd
principal component
SPIHT bitstream for the surface coefficientsof the 1st principal component(color components are interleaved)

Fig. 2. Bitstream structure.

Vol. 2 No. 8 Aug. 200429

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structure.
4. Experimental implementation of the proposed
scheme4.1 Example implementation
The scheme presented in the last section provides aframework for enabling the functionality explained in
section 1. Various implementations are possible,
depending on the wavelet filters used, the optimization method used to derive the view basis and the surface coefficients, and so on. In this section, we present an example implementation of our scheme andprovide some experimental results.
Multi-viewpoint images (with corresponding camera parameters given) and a geometric model were
given to generate the SLF. The geometric model wasa triangular mesh. Here, we describe the experiments
for two datasets: “horse 1” and “horse 2”. The horse
1 dataset consists of 110 images of a real-world
object and a mesh that approximates the object’s surface. The horse 2 dataset consists of 281 rendered
images, generated by texture mapping a texture to themesh of the horse 1 dataset.
The surface sampling points ui (i = 0, 1, .., I – 1)
were generated by the MAPS [9] parameterization
algorithm to use the SG wavelets. In our experiments,
45,954 sampling points were generated (I = 45954).
For the SLF directions, a geodesic dome consisting of
258 vertices was generated in the global coordinate
system by subdividing an octahedron, where wj ( j =0, 1, .., J – 1) are given as directions that point outward from the center of the dome to the vertices of thedome (J = 258). The SLF was resampled with respect
to the local coordinate system Al, i (l = 0, 1, 2) definedby equation (2) at each sampling point ui. To obtain
the resampled SLF, the color at the point in each
image that ui was projected to was evaluated by bilinear interpolation. This was done only for images
where ui was visible. This resulted in a set of color
data, with corresponding directions at each point ui.
A method similar to the nearest neighbor sampling
was applied to obtain color for each direction wj. For
each direction wj at each point ui, the color that wasclosest in direction (i.e., the color with the smallest
inner product of the direction vector) was mapped.
–
The diffuse texture Li was computed as the mean ofthe data at each surface point ui. Denoting ϑi as the setof direction indices j, where wj points outwards fromthe mesh, and |ϑi| as the volume of the set ϑi, the diffuse texture is given by30

Li = ∑Li, jj ∈ϑ l ϑ l.

(3)

We define the residual L'i, j of the SLF with respect–
to Li by–
L'i, j = Li, j – Li.

(4)

The view basis and the surface coefficients were
derived for the residual L'i by the NIPALS algorithm,
which is an optimization algorithm for PCA that cancope with missing values. There are several versionsof this algorithm that can cope with missing values.
We used the version proposed by Christofferson,
which is explained in a paper by Grung and Manne[12].
The filter used for the wavelet transform was the“modified butterfly” filter [13]. For encoding of the
wavelet transform coefficients, the modified SPIHT[11] was used. The entropy coding of the significancevalues [10] was not used in our implementation. For
SPIHT, the input data must be fixed-point binary datathat can be treated as an integer. In this example, thewavelet coefficients were rounded to integer values
and fed into the SPIHT encoder. On the other hand,the view basis vectors were scalar quantized with 8
bits (because the view basis vectors were normalized,
each element in the vectors was bounded by –1 and 1)
and encoded with a fixed-length code.
4.2 Experiments
We defined the PSNR (peak signal to noise ratio)
for the SLF PSNRSLF by
PSNRSLF = 10 log10(2552/MSESLF).

(5)

The MSESLF in the above equation is given byI −1∑ ∑ ( Li, j − Li, j )i = 0 j ∈ϑ l
MSESLF =I −1∑ ∑ ϑi2.

(6)

i = 0 j ∈ϑ l–
The Li, j in the above equation was the reconstructed SLF obtained by equation (1).
First, we present the rate-distortion (RD) results forthe horse 1 and horse 2 data set in Figs. 3 and 4,
where the SPIHT encoding was carried out losslessly. The results presented are for the R signal of thecolor components. The plots ■, ▲, ● are all results
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26
25.5
25
24.5

PSNR

24
23.5
23
proposed, SPIHT fully decoded
proposed, SPIHT decoding terminated (2/3)
proposed, SPIHT decoding terminated (1/3)
fixed-length coding

22.5
22
21.5
21

0

500

1000

1500

2000

2500

3000

3500

Number of bits (kbits)

Fig. 3. RD performance for horse 1.
25
24.5
24

PSNR

23.5
23
22.5
proposed, SPIHT fully decoded
proposed, SPIHT decoding terminated (2/3)
proposed, SPIHT decoding terminated (1/3)
fixed-length coding

22
21.5
21

0

500

1000

1500

2000

2500

3000

3500

Number of bits (kbits)

Fig. 4. RD performance for horse 2.

for the proposed scheme, and all the plots wereobtained from the same bitstream (i.e., obtained byprogressive reconstruction). The plots ■ point to theRD performance when either the diffuse texture or apair of the view basis vector and the corresponding
surface coefficient set was completely decoded. The
plots ▲, ● point to the RD performance when the
decoding of the SPIHT data for the diffuse texture or
the surface coefficient set was terminated at 1/3, 2/3of the corresponding SPIHT data. Plots × are results
for the method we present for comparison. The diffuse texture, the view basis, and the surface coefficients were all encoded by fixed length coding. To be
Vol. 2 No. 8 Aug. 2004

specific, the view basis was encoded in the same way
as in the proposed scheme, while the diffuse texture
and the surface coefficients were encoded by a fixedlength code with the smallest number of bits needed
to represent all the rounded integer values.
The plots ■ indicate that overall the rate of increaseof the RD performance was roughly higher when therate was low, indicating the RD characteristics that
progressive compression schemes should have. The
reason for this is that the more important the pair ofview basis vector and surface coefficient set was, thecloser that pair was to the beginning of the bitstream.
Comparing the RD performance with the × plots,31

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there was a significant compression gain. More
importantly, the plots ● indicate that the decoding of
the surface coefficients can be terminated and still
increase the quality of the reconstructed data with the
decoded SPIHT bits up to that point (i.e., the bitstream obtained by the proposed scheme is finer
grained).
On the other hand, the increase in RD performance
between the pair of plots ▲ and ● for the diffuse texture or the surface coefficients for a particular k wasvery small. This indicates that the bits decoded
between points ▲ and ● did not include important
information for the reconstruction. The reason forthis behavior is apparent from the characteristics of
the SPIHT algorithm.
To further understand this tendency, we conducted
another experiment. The results are shown in Fig. 5.
These results are only for the R signal of the horse 1
data set. The sequence of plots connected by lines ■,▲, ●, and × all represent the performance of our
method, and each sequence of plots represents results
decoded from the same bitstream. The difference
between the encoding methods for each plot
sequence is the point of truncation of the SPIHT bitstream at the encoder. For plots ■, SPIHT encoding
was carried out losslessly (i.e., with no truncation).
For plots ▲, SPIHT encoding was terminated by not
encoding the least significant bits (LSBs). For plots●, the LSB and the second LSB were not encoded.
For plots ×, the LSB, second LSB, and third LSB
were not encoded.
For the plot sequences ■, ▲, and ●, the more the
SPIHT bitstream was truncated, the better the RD

performance was. This is because the truncated data
did not contain important information for reconstruction, and significant numbers of negligible bits were
eliminated from the bitstream. On the other hand, plot
sequence × indicates a significant degradation in RD
performance. This indicates that the SPIHT bits wereover-truncated. Furthermore, adding up to eight pairsof the view basis vector and the surface coefficient set
did not recover the RD performance, compared withthe other plot sequences. For these plot sequences,
only five pairs were used. In these experiments, weapplied the same rule of SPIHT encoding terminationfor every pair of the view basis vector and the surface
coefficient set. Since the probability for high-magnitude wavelet transform coefficients tends to be lower
for the later pairs in the bitstream, we suspect thatchanging the termination rule, depending on the pair
can increase the RD performance. We intend to investigate this bit allocation problem in future.
Finally, we will present examples of the rendered
images that correspond to the results discussed
above. The rendering algorithm presented in [6] has
not yet been implemented. Instead, our current implementation uses the following algorithm. Every timethe view of the virtual camera is changed, at every
surface sampling point ui (i = 0, 1, .., I – 1) a viewdependent blending of the color presented in [14] isdone to obtain one color per sampling point ui. Thenthe mesh defined by the subdivision connectivity ofthe surface sampling points ui (i.e., the mesh thattreats ui as vertices) is rendered, and the colors evaluated for each surface sampling point are interpolated for the whole surface by the procedure used in

26

25

PSNR

24

23

22

no bitplane discarded
1 bitplane discarded
2 bitplanes discarded
4 bitplanes discarded

21

20

0

500

1000

1500

2000

2500

Number of bits (kbits)

Fig. 5. RD performance comparison for the SPIHT encoding termination rule.

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Gouraud shading. Figure 6 is a rendered image of theuncompressed SLF. Figure 7 is a rendered image ofthe result corresponding to the first plot on the left as■ in Fig. 3. Figure 8 is a rendered image of the result
corresponding to the first plot on the right as ■ in Fig.
3. We confirmed that the result in Fig. 7 showed a diffuse component-like texture being recovered. In Fig.
8, adding the pair of view basis vector and surface
coefficient set to the reconstruction added a specularcomponent-like color to the rendered image.
5. Conclusion
In this paper, we described our progressive SLF
compression scheme. This scheme uses a compressed
representation of the SLF obtained using the view
basis and the surface coefficients to enable on-the-fly
reconstruction. The pairs of view basis vector andsurface coefficients set are placed in the bitstream in
the order of importance, and the surface coefficientsare progressively encoded to obtain a finer-grained
bitstream. Progressiveness of the bitstream allows theuser to decode the SLF without waiting for the whole
bitstream to be transmitted, in a low bandwidth environment. Also, rendering can be done without consuming much memory, allowing the user to renderwith a high-resolution SLF, or with multiple SLFs
simultaneously.
Our future plans include investigating 1) a bit allocation strategy for the pair of view basis vector andsurface coefficient set and 2) a rendering algorithm
that further exploits conventional rendering-accelerating graphics hardware compared with the algorithm
proposed in [6].

Fig. 6. Rendered image for the uncompressed SLF.

Fig. 7. Rendered image for the reconstructed SLF where
the result corresponds to the first plot on the left in
Fig. 3.

6. Acknowledgments
The horse 1 dataset was provided by Intel Corporation [15].
References
[1]
[2]
[3]

[4]
[5]

“Application and Requirements for 3DAV,” document N5877 MPEG
Trondheim Meeting, 2003.
S. B. Kang, “A Survey of Image-Based Rendering Techniques,”
VideoMetrics, Vol. SPIE2641, pp. 2-16, 1999.
G. Miller, S. Rabin, and D. Ponceleon, “Lazy decompression of surface light fields for precomputed global illumination,” Eurographics
Rendering Workshop, pp. 281-292, 1998.
M. Levoy and P. Hanrahan, “Light field rendering,” Proc. SIGGRAPH’96, Los Angeles, U.S.A., pp. 31-42, Aug. 1996.
M. Magnor, P. Pamanathan, and B. Girod, “Multi-View Coding for
Image-Based Rendering Using 3-D Scene Geometry,” IEEE Trans.
Circuits and Systems for Video Technology, Vol. 13, No. 11, pp.
1092-1106, 2003.

Vol. 2 No. 8 Aug. 2004

Fig. 8. Rendered image for the reconstructed SLF where
the result corresponds to the first plot on the rightin Fig. 3.

33

Selected Papers[6]

D. N. Wood, D. I. Azuma, K. Aldinger, B. Curless, T. Duchamp, D.
H. Salesin, and W. Stuetzle, “Surface light fields for 3D photography,” Proc. SIGGRAPH’00, New Orleans, Louisiana, U.S.A., pp.
287-296, July 2000.
[7] S. Mallat, “A theory for multiresolution signal decomposition: The
wavelet representation,” IEEE Trans. on Pattern Analysis andMachine Intelligence , Vol. 11, No. 7, pp. 674-693, 1989.
[8] P. Schroder and W. Sweldens, “Spherical Wavelets: Efficiently Representing Functions on the Sphere,” Proc. SIGGRAPH’95, Los Angeles, U.S.A., pp. 161-172, Aug. 1995.
[9] W. Lee, W. Sweldens, P. Schroder, L. Cowsar, and D. Dobkin,“MAPS: Multiresolution adaptive parameterization of surfaces,”
Proc. SIGGRAPH’98, Orlando, U.S.A., pp. 95-104, July 1998.
[10] A. Said and W. Pearlman, “A New, Fast, and Efficient Image Codec
Based on Set Partitioning in Hierarchical Trees,” IEEE Trans. Circuits and Systems for Video Technology , Vol. 6, No. 3, pp. 243-250,
1996.
[11] A. Khodakovsky, P. Schroder, and W. Sweldens, “Progressive Geometry Compression,” Proc. SIGGRAPH’00, New Orleans, Louisiana,
U.S.A., pp. 271-278, July 2000.
[12] B. Grung and R. Manne, “Missing values in principal component
analysis,” Chemometrics and Intelligent Laboratory Systems, Vol.
42, pp. 125-139, 1998.
[13] D. Zorin, P. Schroder, and W. Sweldens, “Interpolating Subdivision
for Meshes with Arbitrary Topology,” Proc. SIGGRAPH’96, Los
Angeles, U.S.A., pp. 189-192, Aug. 1996.
[14] C. Buehler, M. Bosse, L. McMillan, S. Gortler, and M. Cohen,“Unstructured lumigraph rendering,” Proc. SIGGRAPH’01, Los
Angeles, U.S.A., pp. 425-432, Aug. 2001.
[15] http://www.intel.com

Masaki Kitahara
Research Engineer, Visual Media Communications Project, NTT Cyber Space Laboratories.
He received the B.E and M.E degrees in industrial and management systems engineering fromWaseda University, Tokyo in 1999 and 2001,
respectively. He joined NTT in 2001 and has
been engaged in R&D of model-based data compression for image based rendering and H.264
video compression algorithms. His researchinterests include signal processing methods for
3D applications, data compression, and hardware-assisted rendering algorithms for imagebased rendering, and mesh parameterization. He
is a member of the Institute of Electronics, Information and Communication Engineers of Japan(IEICE).

Hideaki Kimata
Research Engineer, Visual Media Communications Project, NTT Cyber Space Laboratories.
He received the B.E. and M.E. degrees inapplied physics from Nagoya University,
Nagoya, Aichi in 1993 and 1995, respectively. In
1995, he joined NTT Human Interface Laboratories, where he has been engaged in R&D of low
bitrate video coding and error resilient video coding algorithms and visual communication systems. His research interests also include free
viewpoint video coding and pre- and post-processing for video coding. He acts as a co-chair of
MPEG 3DAV AHG. He is a member of IEICE
and the Institute of Image Information and Television Engineers of Japan(ITE).

Kazuto Kamikura
Senior Research Engineer, Visual Media Communications Project, NTT Cyber Space Laboratories.
He received the B.E. and M.E. degrees in electrical engineering from Tokyo Science University, Tokyo in 1984 and 1986, respectively. He
received the Ph.D. degree in electrical engineering from the University of Tokyo, Tokyo in
2000. He joined NTT in 1986 and has been
engaged in R&D of video coding systems. His
current research interests include digital image
processing and video sequence coding. He is amember of IEICE and ITE.

Yoshiyuki Yashima
Senior Research Engineer, Visual Media Communications Project, NTT Cyber Space Laboratories.
He received the B.E., M.E., and Ph.D degrees
in electronics engineering from Nagoya University, Nagoya, Aichi in 1981, 1983, and 1998,
respectively. In 1983 he joined the ElectricalCommunications Laboratories, Nippon Telegraph and Telephone Public Corporation (nowNTT), Kanagawa, where he has been engaged in
R&D of high-quality HDTV signal compression,
MPEG video coding algorithms, and losslessimage coding systems. His research interests also
include pre- and post-processing for video coding, processing of compressed video, compressed video quality metrics, and image analysis
for video communication systems. He is a member of the IEEE Signal Processing Society, theInformation Processing Society of Japan, IEICE,
and ITE.

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NTT Technical Review