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Perceptual Properties of Vibrotactile Material Texture:
Effects of Amplitude Changes and Stimuli beneath Detection Thresholds
Shogo Okamoto and Yoji Yamada
Abstract— We investigate the perceptual properties of vibrotactile material textures: in particular, the effects of changes
in vibrotactile amplitudes and of vibratory stimuli that are
beneath detection thresholds. Existing knowledge of the perceptual properties of vibrotactile gratings does not hold forvibrotactile material textures because material textures arecomposed of many more frequency components than thoseof grating textures. We experimentally investigate perceptual
properties of wood and sandpaper textures through lossy datacompression of the textures. Lossy data compression is suitable for manipulating many independent variables of material
textures while maintaining their qualities. By quantizationof vibratory amplitudes, we find that subjective quality of
textures does not change until the number of quantization steps
reaches 12. This represents that data sizes of material textures
are reduced by up to approximately 75% while maintaining
their qualities. By cutting off the frequency components whose
amplitudes are beneath the shifted-threshold curve, we find thateven subliminal stimuli affect perception. Developers of vibrotactile material textures should be aware of these perceptual
properties.

I. I NTRODUCTION
Vibrotactile displays have many applications for information
terminal interfaces. For example, vibrotactile stimuli provide alternatives to audiovisual information or help deliver such information to users more reliably [1], [2]. Vibrotactile displays canbe virtual mechanical input devices such as a switch or slider
and can improve the usability of information terminals [3], [4].
They can also be used for emotional communication [5]. The
installation of vibrotactile displays on information terminals
could lead to further applications. If they are inexpensive, thedisplays benefit both users and suppliers of such terminals.
Existing techniques for vibrotactile displays are adequate
for presenting grating patterns or the asperity of textured
surfaces. On the other hand, several researchers have attempted
to deliver quality textures, such as those of specific materials(e.g., [6]–[10]). However, definitive methods have yet to be established. Although these attempts allowed users to discriminate
certain materials, such as cloth, paper, or metal, the textures
presented by existing methods differ from those experienced
by exploring the actual materials. Methods of vibrotactile
presentation of material textures are still under development.
One difficulty is the lack of knowledge about the perceptual
properties of vibrotactile material textures.
Thus far, the studies on the perceptual properties of vibrotactile textures have focused mainly on gratings. For example,
the detection and differential thresholds for the amplitudes
or spatial frequencies of sinusoidal or square gratings werestudied by Tan et al. [11], Cholewiak et al. [12], and Biet etal. [13]. The psychophysical aspects of roughness perception ofvirtual gratings were reported by Otaduy et al. [14], Klatzky
S. Okamoto and Y. Yamada are with the Department of Mechanical Science, the Graduate School of Engineering, Nagoya University,
Japan okamoto-shogo@mech.nagoya-u.ac.jp This work
was partly supported by MEXT KAKENHI 22800030.

978-1-4244-9314-2/10/$26.00 ©2010 IEEEand Lederman [15], and Kyung et al. [16]. However, studies on
the perceptual properties of vibrotactile material textures have
yet to be reported.
One essential difference between gratings and material textures is the number of frequency elements included in the
textures: material textures are composed of many frequency
elements. The perceptual properties of material textures cannot
be understood by the existing knowledge of gratings. The
effects of masking on detection thresholds are a good example.
When many frequency elements such as noise are included intactile signals, they influence the detection thresholds of thesignals [17], [18]. The elements mutually affect how they areperceived.
The objective of this study is to investigate the perceptual
properties of vibrotactile material textures. In particular, weinvestigate the effects of changes in the vibrotactile amplitudes
and of amplitudes beneath the detection threshold on perceptions of texture. We investigate the perceptual properties using
lossy data compression of vibrotactile textures. Conventional
psychophysical approaches are not suitable for material textures that have many independent variables. Data compression
techniques manipulate these variables simultaneously while
maintaining subjective quality, which is a suitable approach forstimuli in the real world. The techniques have contributed to theunderstanding of perceptual properties in audiovisual media, as
well as to reductions in the data size of media files. Lossy datacompression may also facilitate understanding of the perceptionof vibrotactile textures. We prepare three different compression
algorithms that are designed for vibrotactile textures. The
subjective quality of the compressed material textures is experimentally investigated. To the best of our knowledge, studies
of compression of vibrotactile textures have not been reported,and no studies have focused on the perceptual properties ofvibrotactile material textures.

II. E XPERIMENTAL E QUIPMENT: V IBROTACTILE
T EXTURE D ISPLAY
A. Equipment
For displaying complex tactile information, tactor arrays
may be more effective than a single tactor. However, forsimplicity, a vibrotactile display composed of one tactor is usedin this study. One tactor would be preferable for commercial
products because it is less expensive. Figs. 1a and 1b show thevibrotactile display and its use. As a vibrotactile stimulator,a piezo-stack actuator (NEC/TOKIN, AHB800C801FPOLF,
Sendai, Japan) was installed on a linear slider (SS series, NSK,Tokyo, Japan). The slider’s position on the guide was measured
by an optical linear encoder (SR-P1000, Canon, Tokyo, Japan)
whose resolution was set to 0.4 μ m. When a participant in theexperiments placed his/her finger on the vibrator and moved
his/her hand along the X-axis, the vibrator slid along the guideand produced displacements along the Y-axis, reproducing thesurface profile of the material’s texture. The refresh rate forthe voltage output to the vibrator was 3.0 kHz.
The output force of the stimulator is approximately 800 N,
which is large enough compared to the force applied by the- 384 -SI International 2010

that amplitudes can be somewhat coarsely quantized. The
detectability suggests how to handle subliminal stimuli. Because
subliminal vibrotactile stimuli may not be necessarily delivered
to users, the compression algorithm eliminates amplitudes
smaller than detection threshold. These two types of properties
have been discussed in frequency space. Hence, in this study, thecompression of material textures is also performed in frequency
space.

B. Data Compression using Discrete Cosine Transformation
A discrete cosine transformation (DCT) is used to convert the
texture profile to a frequency spectrum. By computing the DCTof texture height samples yn (n = 0, N − 1), amplitude spectra
Ck (k = 0, 1, ...N − 1) are acquired (Figs. 2a and 2b). Ck is given
by
Ck

=

2 N−1
(2n + 1)kπ
∑ yn cos ( N ) k = 1, ...N ′
N n=0

(1)

Ck

=

0

(2)

k = 0, N ′ + 1, ...N − 1

where N is the number of samples: N = 20000. The DC
component C0 is not used, since it does not influence perception.
The N ′ -th DCT index corresponds to the amplitude of the
400 Hz component. Because the command refresh rate of thevibrotactile display is 3.0 kHz, the elements with a frequency
higher than 400 Hz are removed. The frequency fk for the k-th
DCT index is determined by
fk =Fig. 1: Experimental setup. a) Vibrotactile display usage. b)
Linear-slider-type vibrotactile display.

finger that the latter does not cause the output displacements
to decay. The maximum output displacement is approximately
80 μ m when the applied voltage is 150 V. The displacementchanges linearly with the input voltage when the frequency isfixed. The frequency response curve is relatively flat and does
not exhibit resonance in the range used for the experiments(up to 400 Hz). The frequency response reaches -3 dB at
270 Hz. To compensate for attenuation at high frequencies sothat the desired displacements are output, the voltage inputs
to the stimulator are multiplied by the inverse function of thefrequency response curve. Voltage is applied to the stimulator
through a bipolar amplifier (FRA5014, NF, Yokohama, Japan).
The contactor is circular with a diameter of 11.6 mm.

v⋅kN/L ⋅ v
Sampling frequency
⋅k =
⋅k =
Number of samples
N
L

where L and v are the distance over which the texture surface
was measured (10 mm) and the average velocity of participants’
exploratory hand movements, respectively. The average velocity
was set in a range in which natural explorations are notdisturbed (50 mm/s). In the experiments described below,
participants conducted their exploration following the rhythmof a metronome and used a specified stroke length to realize thisaverage velocity as far as possible (as described in section IV-A).
N/L is equivalent to the number of samples per millimeter.
The DCT indices are converted by the compression methods
described in section III-C (Fig. 2c). The converted DCT indices
Ck′ are re-converted to texture height profiles y′n through aninverse DCT (Fig. 2d). y′n is acquired byN′

y′n = ∑ Ck′ cos (k=1

B. Material Textures
The surfaces of 11 materials, including woods, paper, andcloth, were measured. A board made from the wood of theJudas tree and a piece of sandpaper (#1500) were chosen for theexperiments, in part because they showed good correspondencewith reality when they were explored by experts using theequipment. A non-contact surface measure (NH-3SP, Mitaka
Kohki, Mitaka, Japan) was used to measure the surfaces. It
measured the surface height profile of a randomly selected
portion of each texture for 10 mm with an interval of 0.5 μ m.

A. Perceptual Characteristics for Vibrotactile Stimuli
The compression methods use the discriminability anddetectability characteristics of the amplitudes of vibrotactile
stimuli. The discriminability describes supraliminal stimuli. The
differential threshold for the vibratory amplitudes is a Weberratio of 10–20% (computed from [19] and [20]). This indicates(2n + 1)kπ)N

n = 0, 1, ...N − 1.

(4)

C. Data Compression Algorithms for Vibrotactile Textures
1) Linear Quantization: Quantization is one of the mostpopular strategies for lossy data compression. The quantized
amplitude spectrum Lk is determined by
Lk = round(

Ck
)⋅Δ
Δ

(5)

where Δ is the quantization step. Δ is
Δ=

III. L OSSY DATA C OMPRESSION OF V IBROTACTILE
T EXTURES(3)

max(C1 , ...,CN ′ ) − min(C1 , ...,CN ′ )
L−1

(6)

where L is the number of quantization steps and determines
the compression level. Fig. 3 shows an example of the DCT
indices and the surface profile of the uncompressed and linearly
quantized (L = 4) wood textures. Given that each DCT index
of the uncompressed texture is written in 2 bytes, the data sizeof the compressed texture in the figure is 12.5% of the size ofthe uncompressed data.

- 385 -SI International 2010

Fig. 2: Process flow of the compression of vibrotactile material textures

2) Log Quantization: Log quantization has been usedin sound compression. The application of fine quantization
steps for small amplitudes and coarse quantization steps forlarge amplitudes may effectively reduce the data size while
maintaining the quality. We use a log function to determine thestep sizes. Positive and negative amplitude values are quantizedseparately. For positive Ck (k = 1, . . . , N ′ ), the log-quantized
amplitude Nk is determined bylog10 Ck − log10 S) ⋅ Δ+ + log10 S
Δ+
if Ck > S
max (log10 Ck ∣Ck > 0, k = 1, . . . , N ′ ) − log10 S+.
Δ =L/2 − 1log10 Nk = round(

(7)

(8)

For negative Ck (k = 1, . . . , N ′ ), Nk is determined bylog10 (−Ck ) − log10 S) ⋅ Δ− + log10 S
Δ−
if Ck < −S
max (log10 −Ck ∣Ck < 0, k = 1, . . . , N ′ ) − log10 S−
Δ =.
L/2 − 1log10 Nk = round(

(9)

(10)

In the linear quantization method mentioned previously, theamplitudes are quantized between the maximum and minimum

Fig. 4: Log-quantized sandpaper texture: L = 6

Ck (k = 1, . . . , N ′ ). On the other hand, for log quantization, theamplitudes are quantized between the maximum or minimum
Ck and S. S is an amplitude small enough that it does not
influence the perceived texture (here 0.05 μ m). For Ck with anamplitude smaller than S, Nk is determined by
Nk = sign(Ck ) ⋅ S

Fig. 3: Linearly quantized wood texture: L = 4

if ∣Ck ∣ ≤ S.

(11)

Clearly, S affects the performance of the compression algorithm.
S needs to be small enough, but if it is too small, it increases
quantization errors. A reasonable determination of S remains
as future work.
Fig. 4 shows an example of the log-quantized sandpaper
texture and its amplitude spectrum. The amplitudes were
quantized between the maximum amplitude and S in six steps.
The larger amplitudes were quantized more coarsely. The datacompression ratio of the texture in Fig. 4 is 16.2%.
3) Truncation of Data beneath Shifted Thresholds: Assuming that stimuli beneath the detection threshold level canbe removed while maintaining subjective quality, the thirdcompression method eliminates vibratory amplitudes smaller
than the thresholds. When they are removed, the DCT indices- 386 -SI International 2010

TABLE I: Compression level of textures used for evaluation
Wood
Sandpap.

Wood

Fig. 5: Sandpaper texture of which amplitudes beneathlog10 b(F) + b0 ∣b0 =0 are truncatedare determined by
{
0
Tk =
Ck

b0
if log10 Ck < log10 b(F) +20otherwise(12)

where b(F) and b0 are the detection thresholds at frequency F
and a variable for shifting the threshold curve, respectively. b0
determines the compression level of the method. For example,
when b0 = 0 dB, amplitudes smaller than the threshold curve
are truncated. When b0 = −2 dB, the amplitudes smaller thanthe -2-dB-shifted threshold curve are truncated. For the lattervalue, the compression ratio is worse than for b0 = 0 dB.
The function b(F) was preliminarily acquired by a psychophysical method of adjustment involving six participants,
using the same equipment as this study. b(F) was acquired
while the hand was still.
Fig. 5 shows the sandpaper’s texture compressed by thismethod when b0 = 0 dB. Fig. 5b shows that the compressed
texture does not include DCT indices smaller than the thresholdcurve. In this example, since 45% of the indices were truncated,the compression ratio is regarded as 55%.

IV. E XPERIMENT: S UBJECTIVE C OMPARISON OFDATA -C OMPRESSED T EXTURES
A. Tasks and Participantsa) General Design: The participants evaluated the subjective similarity between the uncompressed and compressed
textures. They rated the similarity on a four-graded point
scale. The compression level of the textures varied by 5–7
grades. To acquire more reliable indices, for the wood texture,a two-alternative forced choice task was performed in which
participants described the similarity by identifying sameness ordifference. The wood and sandpaper textures were evaluatedby different participants.

Four-point-scaled similarity rating
linear quant. [step] L = 14, 12, 10, 8, 6, 4
log quant. [step]L = 14, 12, 10, 8, 6
threshold-cut [dB]b0 = −12, −8, −4, 0, 4, 8
linear quant. [step] L = 14, 12, 10, 8, 6, 5, 4
log quant. [step]L = 14, 12, 10, 8, 6
threshold-cut [dB]b0 = −12, −8, −4, 0, 4, 8, 12
Two-alternative forced choicelinear quant. [step] L = 12, 10, 8, 6, 5, 4b) Tasks: The participants explored and compared theuncompressed and compressed textures. In the trial, eachtexture was presented to the participants for 5 s. To adjust
the average hand velocity of the participants to 50 mm/s (seesection III-B), the participants moved their hands in strokes of
40 mm in accordance with the rhythm of a metronome (1.25 Hz)
in a pattern of one stroke for one beat and reciprocation for twobeats. Marks on the desk indicated the stroke distance (Fig. 1a).
After exploring the two textures, the participants made theiranswers. When the similarity scale was used for the evaluation,
the participants manually recorded the similarity as “same,”“possibly the same,” “possibly different,” or “different.” For the
two-alternative forced choice tasks, the participants reported
whether they felt the textures were the same or different.
c) Test Stimuli: The texture compression levels were 5–
7 grades for each compression method. Table I shows thecompression levels used in the experiments for each type of
texture. For four-point similarity scaling, each test stimulus
was presented four times in total. Hence, for the wood texture,
72 trials were performed in total (18 textures × 4 repetitions)for each participant. For the sandpaper, 80 trials were performed (20 textures × 4 repetitions). For the two-alternative
forced choice task, only the linearly quantized wood textures
were tested, and 140 trials were performed (7 textures × 20
repetitions).
d) Participants: For each of the wood and sandpaper
textures, the participants were eight volunteers in their 20s who
gave an informed consent. For the two-alternative forced choicetask and four-point similarity scaling for the wood texture,
the same eight participants were recruited, and the order ofthe experiments was counter-balanced. Each experiment was
conducted on different days. To shut out the sounds generated
by the stimulator, the participants listened to pink noise throughnoise-cancellation headphones. New eight participants wererecruited for the sandpaper textures.
B. Results
Subjective Dissimilarities of Compressed Wood Textures:
Fig. 6 shows the averages and standard deviations of thedissimilarity rates of wood textures that were compressed using
the three methods. The values in the figure were computedby assigning a value of 0, 1, 2, or 3 to “same,” “probably
the same,” “probably different,” and “different,” respectively.
In general, the score increased (similarity decreased) as thedata size of the compressed textures decreased. However, thechanges in the scores were not monotonic. For linear and log
quantization, the changes showed a slight plateau for L = 8. For
small compression rates, the scores increased exponentially.
As reliable indices for subjective distances, d ′ was computedfrom the results of the two-alternative forced choice tasks. d ′is the index of discriminability, which represents how easy it is
to discriminate two stimuli [21]. The profiles of d ′ values anddissimilarity scores were close with a correlation coefficient of
0.945, which indicates that the four-point dissimilarity scorescan be alternative for d ′ indices.

- 387 -SI International 2010

TABLE II: t-test applied to the results of compressed wood textures
Compress.
level
L = 14
L = 12
L = 10
L=8
L=6
L=4

Linear quantization
Compress. t0 (7)ratio [%]
23.8
0.81
22.4
3.87
20.8
4.06
18.8
2.79
16.2
4.20
12.5
14.3

p
0.445
6.11E-3∗∗
4.83E-3∗∗
0.0268∗4.03E-3∗∗
1.97E-6∗∗∗

Compress.
level
L = 14
L = 12
L = 10
L=8
L=6

Log quantization
Compress. t0 (7)ratio [%]
23.8
1.72
22.4
3.17
20.8
5.02
18.8
3.21
16.2
20.9

p
0.129
0.0158∗
1.53E-3∗∗
0.0149∗
1.46E-7∗∗∗

Truncation of data beneath threshold curve
Compress.
Compress.
t0 (7)
plevel (dB)ratio [%]
b0 = −12
75.0
0.049
0.962
b0 = −8
68.8
0.041
0.968
b0 = −4
60.0
3.61
8.60E-3∗∗
b0 = 048.8
3.03
0.019∗
b0 = 433.8
7.47
1.41E-4∗∗∗
b0 = 817.5
15.0
1.38E-6∗∗∗

TABLE III: t-test applied to the results of compressed sandpaper textures
Compress.
level
L = 14
L = 12
L = 10
L=8
L=6
L=5
L=4

Linear quantization
Log-quantization
Truncation of data beneath threshold curve
Compress.
t0 (7)
pCompress.
Compress. t0 (7)pCompress.
Compress.
t0 (7)
pratio [%]
level
ratio [%]
level (dB)ratio [%]
23.8
1.47
0.186
L = 14
23.8
1.84
0.109
b0 = −12
78.8
1.28
0.241
22.4
3.08
0.0179∗L = 1222.4
1.97
0.0897b0 = −8
67.5
1.667
0.139
20.8
1.22
0.260
L = 1020.8
4.84
1.87E-3∗∗∗
b0 = −4
63.8
0.974
0.363
18.8
2.40
0.0478∗
L=8
18.8
6.98
2.15E-4∗∗∗
b0 = 055.0
2.23
0.060816.2
5.58
8.3E-4∗∗∗
L=6
16.2
5.60
8.13E-4∗∗∗
b0 = 4
41.3
5.52
8.89E-4∗∗∗
14.5
6.98
2.15E-4∗∗∗
b0 = 828.8
5.88
6.13E-4∗∗∗
12.5
21.72 1.11E-7∗∗∗
b0 = 1212.5
17.37 5.16E-7∗∗∗*, **, and *** indicate the significance levels of 0.05, 0.01, and 0.001, respectively.

Fig. 6: Subjective dissimilarities between the compressed anduncompressed wood textures

To determine the significant differences between the scoresfor the uncompressed and compressed textures, a pair-wised
t-test was applied to the scores (Table II). For compression
levels of 22.4%, 22.4%, and 60.0% or less, significant increases
were observed in the scores for linear and log quantization andtruncation beneath the shifted threshold, respectively.

Subjective Dissimilarities of Compressed Sandpaper Textures: Fig. 7 shows the dissimilarity scores of compressed sandpaper textures. As in the general trend for the wood textures,the dissimilarity tended to increase as the compression ratio
decreased. However, the scores did not change monotonically.
For linear quantization, the score increased exponentially at
low compression rates. For log quantization, the scores showeda plateau at low compression rates (around 20.8–16.2%).
Table III shows the results of statistical tests applied tothe scores for the sandpaper textures. For linear quantization,
when the compression levels were 22.4% and 18.8% or less,significant differences were observed between the scores for theuncompressed and compressed textures. For log quantizationand truncation of data beneath the shifted thresholds, when thecompression rates were 20.8% and 41.3% or less, respectively,
significant differences in the scores were observed.

Fig. 7: Subjective dissimilarities between the compressed anduncompressed sandpaper textures

V. D ISCUSSIONS
Effects of Changes in Vibrotactile Amplitudes: Forvibrotactile material textures, humans do not detect somewhat
large changes in vibrotactile amplitudes or quantization errors.
For both wood and sandpaper textures, subjective qualities
did not differ for a quantization number of 14, but they did
for a quantization number of 12 or less. The threshold of
quantization number lies in 14–12. Looking at compression
ratios, for the wood textures, the smallest ratio, at which thequality was maintained, was 23.8% for the linear- and logquantization methods. For the sandpaper texture, it was 22.4%
for the log-quantization method. The developers of vibrotactile
displays should be aware that the data size of material textures
can be compressed by approximately 75%.

Effects of Vibrotactile Amplitudes below the Detection Thresholds: The experimental results showed that evensubliminal vibratory amplitudes should not be automaticallyeliminated. For the wood texture, the truncation of data beneaththe -4-dB-shifted threshold curve caused significant changes
in the subjective quality. On the other hand, the quality ofthe sandpaper texture was not affected by truncation of thesubliminal vibratory amplitudes. Although the results dependon the type of texture, the truncation of subliminal amplitudes- 388 -SI International 2010

could influence the subjective quality of material textures.
Best Compression Method: The three compression methods are compared in terms of compression rates and subjective
quality. The method of truncating data beneath the shifted
threshold is clearly inferior to the other two quantization
methods (Figs. 6 and 7). For the wood texture, the performancesof the linear and log quantization methods are almost equal
except at the compression level L = 6. For L = 6, linear
quantization is superior to log quantization, since the subjective
dissimilarities for linear quantization were smaller than thosefor log quantization (t-test, t0 (7) = 6.03, p = 5.27E − 4, twotailed). For the sandpaper texture, for L = 10 (t-test, t0 (7) = 3.21,
p = 0.015, two-tailed) and L = 8 (t-test, t0 (7) = 3.08, p = 0.018,
two-tailed), the linear quantization method was better than thelog quantization method, and no significant differences wereobserved at the other compression levels. Overall, considering
the above comparison, the best of the three methods is linear
quantization, followed by log quantization and truncation ofdata beneath the threshold curve.
Nonlinear Effects of Quantization: The experimental
results showed that the subjective dissimilarities in the compressed textures did not change monotonically with the compression rates for either the wood or the sandpaper textures.
For example, for linear quantization of the wood texture, thedissimilarity at L = 8 was unexpectedly small. Also, for log
quantization of the sandpaper texture, the dissimilarity at L = 6
was not larger than those for L = 8 and L = 10. On the other
hand, for the method of truncating data beneath the shifted
threshold curve, the dissimilarities changed monotonically withthe compression ratio. In the truncation method, the energyof the stimuli decreases with the compression rate, but thisdoes not hold for the quantization methods; quantization can
increase the energy of the stimuli. The relationships between
the stimuli energy and the subjective quality of the compressed
textures are unknown. However, non-monotonic changes in theenergy might produce a texture that happens to be subjectively
similar to the uncompressed texture.

VI. C ONCLUSIONSThe objectives of this study was to investigate the human perceptual properties of vibrotactile material textures.
In particular, we experimentally investigated the effects of
sub-threshold vibrotactile amplitudes and of changes in theamplitudes on perception. Conventional psychophysical approaches are not suitable for material textures that have many
independent variables. We adopted lossy data compressionof textures. Data compression manipulates many variables
simultaneously while maintaining subjective quality, which isa suitable approach for stimuli in the real world. We applied
three lossy data compression methods to the surface texturesof a wood board and sandpaper: linear and log quantizationof vibratory amplitudes in frequency space and truncation ofdata beneath the shifted detection threshold curve. Participantsin the experiments rated the subjective similarities of thesecompressed textures compared to the uncompressed original
textures. No significant differences in the ratings were observed
until the number of quantization steps reached 12. Compression
ratios reached 23.8% and 22.4% for the wood and the sandpaper, respectively, while maitaining the quality, which represents
that the data size of the material textures can be reduced by
up to approximately 75%. Furthermore, the we showed thatvibratory amplitudes smaller than detection thresholds could
influence the perception of material textures. We conclude thatthe lossy data compression approach can reveal perceptual
properties of vibrotactile material textures.

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