1

Variable Macropixel Spectral-Spatial Transforms
with Intra- and Inter-color Decorrelations for
Arbitrary RGB CFA-Sampled Raw Images
Taizo Suzuki, Senior Member, IEEE, and Seisuke Kyochi, Member, IEEE

Abstract—A raw image captured by a color filter array (CFA),
such as a Bayer pattern, is usually compressed after demosaicing
with some processings (denoising, deblurring, tone-mapping, andso on). However, since photographers, designers, and high-end
users prefer to work with the raw image sampled by CFA(referred to as “raw image”) directly, a raw image should
be compressed before demosaicing. For effective raw image
compression, this study introduces variable macropixel spectralspatial transforms (VMSSTs), that can successfully decorrelate
not only Bayer raw images but any other pure-color (RGB) ones.
The proposed VMSSTs are designed by the following two steps:
1) intra-color decorrelation and 2) inter-color decorrelation. In
lossless compression with JPEG 2000, compared with methods
which do not use transforms, the VMSSTs reduced the average
bitrates of three types of CFAs: from approximately 0.09 to 0.12
bpp for the modified Bayer CFA, from 0.25 to 0.65 bpp for the
diagonal stripe CFA, and from 0.33 to 0.70 bpp for the Fujifilm
X-Trans CFA due to their high color decorrelation efficiency. In
addition, in lossy compression with JPEG 2000, compared with arearranged method, the VMSSTs improved the average bitratesof the Bjøntegaard delta by around 3.97 %, 14.95 %, and 18.65% for each CFA model, respectively. Although a data-dependent
adaptive transformation, the Karhunen-Loève transform (KLT),
showed the best performance in lossy compression, the introduced
VMSSTs have shown performances comparable to those of the
KLT in lossless compression, despite their simple structures.
Index Terms—Color filter array, color transform, macropixel,
pure-color, raw image, spectral-spatial transform.

I. I NTRODUCTION
N the image acquisition procedure, image data is first
captured by a color filter array (CFA), then it is converted
to RGB full-color images with demosaicing. The most popular
CFA is a Bayer CFA [1] consisting of 2 × 2 macropixels
with an RG2 B (red, two greens, and blue) CFA, as shownin Fig. 1(a). Other pure-color (RGB) CFAs consisting of
only certain red, green, and blue components have been
introduced, for example, modified Bayer CFA [2], diagonal
stripe CFA [2], and Fujifilm X-Trans CFA [3] (Fig. 1(b)-(d)).
Moreover, various non-pure-color (panchromatic) CFAs havebeen presented as alternatives to the RGB CFAs to obtainhigher accuracy images, for example, Kodak CYYM (cyan,
two yellows, and magenta) [4] CFA, Sony RGBE (red, green,
blue, and emerald like cyan) [5] CFA, Kodak RGBW (red,I

T. Suzuki is with the Faculty of Engineering, Information and
Systems, University of Tsukuba, Tsukuba 305-8573, Japan (e-mail:taizo@cs.tsukuba.ac.jp).
S. Kyochi is with the Department of Information and Media Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan (e-mail: skyochi@kitakyu-u.ac.jp).

(a)

(b)

(c)

(d)

Fig. 1: RGB CFA types (each pixel set framed with a whitesquare is a macropixel): (a) Bayer, (b) modified Bayer CFA,(c) diagonal stripe CFA, and (d) X-Trans CFA.

Fig. 2: Diagram of raw image compression: (i) color decorrelation, (ii) grouping components, and (iii) encoding parts.

green, blue, and white) [6] CFA, and Hirakawa’s CFA [7].
This study focuses on only RGB CFAs for simplicity.
Photographers, designers, and high-end users prefer thatthe raw images sampled by CFA (hereinafter referred to as“raw images”) are not pre-processed. Many studies [8–17]
have presented methods of compressing raw images before
processing. With compression of full-color images, the spectral
components of a pixel with a high inter-color correlation are
generally decorrelated with a color transformation, converting,as a preprocessing step, the initial data from RGB color space
into YUV color space (luma and two chroma components).
Similarly, in the case of the compression of a raw image,the color components should also be decorrelated as shownin Fig. 2. We refer [12–17] for further discussion on the
topic. Our previous work [17] indicated that SSTs can be
represented by cascading discrete wavelet transforms, such as
the Haar, 5/3-tap, and 9/7-tap wavelet transforms. The waveletbased SST (WSST) in [17] covers other SSTs [12–16]; thatis, the existing SSTs are obtained by bypassing parts of
the wavelet transforms in the WSSTs and/or by replacing
them with Haar transforms. Among them, the macropixel SST(MSST) [13], constructed from only three Haar transforms inthe smallest pixel pattern composing a CFA (macropixel), haslower complexity than WSSTs with other wavelet transforms
because a Haar transformation can simply be performed withan adder, a subtracter, and a bit shift.
Although the existing SSTs effectively work for Bayer raw
images, they would not work for other types of CFAs, because
design methods of those transforms significantly depend on

2

the configuration of a Bayer CFA. One of the possible ways
is to apply a data-dependent adaptive transformation, theKarhunen-Loève transform (KLT). However, this approachrequires additional computational/bit cost to derive the KLT
matrix and transmit it to the decoder.
This study introduces variable MSSTs (VMSSTs) for arbitrary RGB raw images, not constrained by a Bayer CFA. The
novelties of this study are summarized as follows:
1. We propose a universal design approach for the VMSSTs
that consist of two steps: 1) intra-color decorrelation and2) inter-color decorrelation.
2. To easily achieve lossy-to-lossless compression, we design the VMSSTs with lifting-based Haar transforms.
Despite of their simple structures, the experiments at compression with the VMSSTs and JPEG 2000 [18] show the VMSSTs
achieves high performance for non-Bayer RGB raw images.
Notation: Boldface letters represent vectors and matrices and non-boldface ones represent scalars. I, J, O,
diag(M0 , M1 , · · · ), and superscript ·⊤ denote a 2 × 2 identity
matrix, 2 × 2 reversal (permutation) matrix, zero matrix, block
diagonal matrix with Mn ∈ RNn ×Nn (Nn ≥ 1), and transposeof a matrix/vector, respectively.

Fig. 3: VMSST in a macropixel: (Step-1) intra-color decorrelation and (Step-2) inter-color decorrelation.

where
Tycocg = diag(1, J) · diag(HJ, 1) · diag(1, H).

(4)

As described in Section II-A, we can consider that the transform has very low complexity because it is composed of only
two Haar transforms.

II. R EVIEW AND D EFINITIONS
C. Macropixel Spectral-Spatial Transform (MSST)

A. Haar Transform
The Haar transform H, the simplest wavelet transform, isexpressed in [19] as follows:
[
]⊤
[
]⊤
Mx , Dx = H X1 , X2 ,
(1)
where

[

] [
][
]
1/2 1/2
1 1/2
1 0
H=
=
.
−1
1
0 1
−1 1

(2)

This study focuses on MSST [13], which changes a Bayerraw image from RG2 B color space to YDgCoCg color space,
where Dg means the difference green component. The transform is implemented within a 2 × 2 macropixel consisting of
RG2 B components R, G1 , G2 , and B in a Bayer CFA. The
conventional MSST Smsst is represented as follows:
[
]⊤
[
]⊤
Y, Dg , Co , Cg = Smsst G1 , G2 , B, R ,(5)

The above means that the calculation simply consists ofcalculating the “mean” Mx and “difference” Dx between thetwo input signals X1 and X2 . By using lifting steps (shownin (2)) with a rounding operation, the Haar transform can be
an integer-to-integer reversible transform, which is necessary
for lossless compression. Moreover, the lifting-based Haartransform can be implemented by only an adder, a subtracter,and a bit shift.

where

B. Reversible Color Transform (RCT)Again, it has very low complexity because it is composed ofonly three Haar transforms.

As a preprocessing, a color transform is generally implemented to decorrelate the RGB components R, G, and
B of a full-color image. Conventional color transforms areclassified into reversible and irreversible classes. Since theapplication of this study is both lossless and lossy raw image
compression, we focus on reversible color transforms (RCTs).
A typical RCT is the YCoCg-based RCT (YCoCg-R) [20],which changes a full-color image from RGB color space into
YCoCg color space composed of a luma component Y , achrominance orange component Co , and a chrominance green
component Cg . The YCoCg-R Tycocg is composed of only two
Haar transforms in (2) as follows:
[
]⊤
[
]⊤
Y, Co , Cg = Tycocg G, B, R ,(3)

Smsst = P2 · diag(H, I) · P1 · diag(H, H).
Here, P1 and P2 are permutation matrices:
0 0 1 01 0
 1 0 0 0
0 0
P1 = 
0 1 0 0 , P2 = 0 0
0 0 0 1
0 1

0
1
0
0


0
0
.
1
0

(6)

(7)

III. VARIABLE M ACROPIXEL S PECTRAL -S PATIALT RANSFORMS (VMSST S )A. Proposed Framework: Intra- and Inter-color Decorrelations
The proposed VMSSTs for arbitrary RGB raw images arecomposed of the following two steps (see Fig. 3).
[(Step-1) Intra-color decorrelation] The color components
Xn = (Rn , Gn , Bn ) (n ∈ N depending on each color)in a macropixel are separately changed into the mean color
components Mx = (Mr , Mg , Mb ) and the difference color
components Dxn = (Drn , Dg n , Db n ) by using Haar transforms H. Although the intra-color decorrelation allows not3only Haar transforms but also any other transform, we selected
Haar transforms to achieve a simpler structure for each CFA
type. Note that if n for a color component Xn in eachmacropixel is 1 (n = 1), the n is omitted and the mean anddifference color components are set to Mx = X and Dx : N/A.
[(Step-2) Inter-color decorrelation] The mean color components Mx in a macropixel are changed into a luma component
Y and two chroma components U and V with an RCT Tyuv :
[
]⊤
[
]⊤
Y, U, V(8)
= Tyuv Mg , Mb , Mr .
For the RCT Tyuv , we can select not only YCoCg-R Tycocg ,but also any other RCT such as the YCbCr-based RCT usedin JPEG 2000. This study uses the YCoCg-R Tycocg , which
consists only of the Haar transforms.
1) Revisit of MSST for Bayer Raw Images: The conventional MSST [13] can also be considered as an example ofthe proposed VMSSTs. To be specific, we can reorganize
the conventional MSST for Bayer raw images in (6) into thefollowing two steps using the YCoCg-R Tycocg in (4):Smsst = diag(J, I) · diag(1, Tycocg ) · diag(JH, I) .
{z
}
|
{z
} |
(Step-2)(9)(Step-1)[(Step-1) Intra-color decorrelation] Two green components
G1 and G2 in a macropixel are changed into the mean green
component Mg and the difference green component Dg byusing a Haar transform H:
[
]⊤
[
]⊤
Mg , Dg = H G1 , G2 .
(10)[(Step-2) Inter-color decorrelation] The red component R,
mean green component Mg , and blue component B in amacropixel are changed into the luma component Y , chrominance orange component Co , and chrominance green component Cg by using the YCoCg-R Tycocg :
[
]⊤
[
]⊤
Y, Co , Cg = Tycocg Mg , B, R .
(11)
B. Application for Non-Bayer RGB Raw Images
This subsection shows applications of intra- and inter-color
decorrelations for non-Bayer RGB raw images. In this work,
we modify the intra-color decorrelation according to the CFA
type, and do not modify the inter-color decorrelation. Thus,
we show only (Step-1) part for each CFA type.
1) For Modified Bayer CFA: We can consider that a modified Bayer CFA [2] to be composed of 4 × 4 macropixels withfour red components, eight green components, and four blue
components as shown in Fig. 1(b). For low complexity, we
subdivide a 4 × 4 macropixel into four 2 × 2 macropixels (submacropixels) with RG2 B components R, G1 , G2 , and B and
implement the VMSST in each of the 2 × 2 submacropixels.
Since a submacropixel is the same or the transposed version of
the other submacropixels, it is sufficient to design the intracolor decorrelation for a submacropixel. For (Step-1) in the
VMSST, the mean color components Mx and the differencecolor components Dx are calculated using a Haar transform
H for only the two green components G1 and G2 :
[
]⊤
[
]⊤
Mg , Dg = H G1 , G2 , Mr = R, Mb = B,
(12)

(a)

(b)

Fig. 4: Examples of intra-color decorrelation in a macropixel:(a) for diagonal stripe CFA and (b) for X-Trans CFA.

where Dr and Db : N/A. It is clear that any VMSST for
arbitrary RGB raw images consisting of 2 × 2 macropixels
with RG2 B components R, G1 , G2 , and B is completely thesame as the revisited MSST for Bayer raw images as shownin Section III-A1.
2) For Diagonal Stripe CFA: A diagonal stripe CFA [2] canbe constructed from 3 × 3 macropixels with three components
in each color, as shown in Fig. 1(c). For (Step-1) in the
VMSST, the mean color components Mx and the differencecolor components Dxn are calculated using two Haar transforms for each of the color components X1 , X2 , and X3 (seeFig. 4(a)):
[
]⊤
[
]⊤
Mx , Dx1 , Dx2 = diag(H, 1) · diag(1, H) · X1 , X2 , X3 .
(13)3) For X-Trans CFA: An X-Trans CFA has 6×6 macropixels with eight red components, 20 green components, and eight
blue components as shown in Fig. 1(d). However, we subdivide
a 6×6 macropixel into four 3×3 submacropixels with R2 G5 B2
components R1 , R2 , G1 , G2 , G3 , G4 , G5 , B1 , and B2 andimplement the VMSST in each of the 3 × 3 submacropixels
as in the case of the modified Bayer CFA described in
Section III-B1. For (Step-1) in the VMSST, the mean color
components Mx and the difference color components Dxn
are calculated using a Haar transform H for each of two red
components R1 and R2 and two blue components B1 and B2 :
[
]⊤
[
]⊤ [
]⊤
[
]⊤
M r , D r = H R 1 , R 2 , M b , D b = H B 1 , B2 ,(14)
and five Haar transforms H for five green components G1 ,G2 , G3 , G4 , and G5 (see Fig 4(b)):[]⊤
Mg , Dg 1 , Dg 2 , Dg 3 , Dg 4
= diag(H, J, 1) · diag(1, H, H) · diag(I, J, 1)[]⊤· diag(1, H, H) G1 , G2 , G3 , G4 , G5 .

(15)

IV. N ON -BAYER RGB R AW I MAGE C OMPRESSION
We evaluated the proposed VMSSTs in terms of lossless
bitrate [bpp] and bitrate of the Bjøntegaard delta (BD-rate)[%] in non-Bayer RGB raw image compression. We used threemethods as conventional methods. The first method, “Direct,”directly inputs a raw image to the encoding part in Fig. 2(iii)(bypassing the color decorrelation and grouping components
parts in Fig. 2(i) and (ii)). The second method, “ReRGB,”
inputs a raw image to the grouping components and encoding
parts in Fig. 2(ii) and (iii) (bypassing the color decorrelation

4

TABLE I: Non-Bayer RGB raw image compression results.
Test Images
Akademie
Arri
Church..
.
Pool
Siegestor
Average
Akademie
Arri
Church..
.
Pool
Siegestor
Average

Modified Bayer CFA [2]Diagonal Stripe CFA [2]X-Trans CFA [3]Direct ReRGB
KLT
VMSST
Direct
ReRGB
KLT
VMSST
Direct ReRGB
KLT
Lossless compression results (lossless bitrate [bpp], where the cases of KLT are not true lossless compressions)
11.55
11.58
11.43
11.41
12.24
11.90
11.44
11.56
12.24
11.94
11.47
10.77
10.89
10.68
10.62
11.52
11.29
10.73
10.87
11.52
11.33
10.76
10.07
10.43
10.11
10.09
10.64
10.83
10.20
10.33
10.65
10.82
10.19
..
..
..
..
..
..
..
..
..
..
..
.
.
.
.
.
.
.
.
.
.
.
11.07
11.26
11.05
11.05
11.53
11.48
11.02
11.14
11.53
11.52
11.04
10.62
10.40
10.48
10.50
10.97
10.37
10.39
10.32
10.80
10.31
10.26
10.70
10.67
10.58
10.58
11.25
10.85
10.56
10.60
11.22
10.85
10.52
Lossy compression results (BD-rate [%] compared with Direct in 0.125-1.00 bpp)–
-47.23-61.03-47.13–-56.91-80.61-62.34–-64.74-84.69–-23.86-51.37-38.14–-6.66-68.25-34.17–-12.31-67.65–-24.46-61.57-48.65–17.99-65.59-27.16–
14.50-68.73
..
..
..
..
..
..
..
..
..
..
..
.
.
.
.
.
.
.
.
.
.
.
–
-23.82-52.94-37.40–7.31-64.43-20.35–
6.68-66.12–-50.87-47.75-34.57–-67.75-71.61-61.15–-57.34-64.92–-37.59-54.57-41.56–-35.42-73.51-50.37–-34.72-72.55

Fig. 5: Rearranged and decorrelated non-Bayer RGB raw images (Akademie): (left to right) modified Bayer CFA, diagonal
stripe CFA, and X-Trans CFA, (top to bottom) ReRGB, KLT,and VMSST.

part in Fig. 2(i)). The third method, “KLT,” inputs a raw
image to all parts in Fig. 2, where the KLT is used as a color
decorrelation.1 According to the previous works [13] and [17],we chose JPEG2000 codec as a coder (we used the MATLAB
built-in function imwrite.m without any modification).2 As
test images, we used ten 2880 × 1620 RGB full-color images
with a 16-bit dynamic range in each pixel from [23]. We
selected the modified Bayer CFA [2], diagonal stripe CFA [2],and X-Trans CFA [3] for arbitrary RGB CFA types of raw
images. To simulate the raw images, we subsampled the test
images in accordance with the CFA types in Fig. 1(b)-(d) andreduced their dynamic range from 16 to 14 bits because theactual sensor data often had only about 10 to 14-bit resolution
at most. Since the decorrelated color components except for
the luma component Y had both positive and negative values
and the JPEG 2000 codec does not allow input signals withnegative values, we transmitted the transformed coefficients,
which had 15-bit positive values by adding 214 , on the encoder
1 Note that raw images cannot be encoded/decoded losslessly by the KLT,
because the KLT is not a reversible transform. We just show the performanceof the KLT in lossless compression as reference results.
2 Other codecs such as JPEG [21] and JPEG XR [22] can also be applied.

VMSST
11.46
10.76
10.24
..
.
11.07
10.24
10.52
-72.40-45.76-34.65..
.
-33.13-49.88-53.37side and reconstructed the images after subtracting 214 on the
decoder side.
Figure 5 shows the rearranged and decorrelated images of
the Akademie. It is clear that the color components were highly
decorrelated by the KLT and the VMSSTs. Table I shows thelossless bitrates and the BD-rates with JPEG 2000 for three
types of non-Bayer RGB raw images. In lossless compression,
the VMSSTs reduced the average bitrates by around 0.09–
0.12 bpp, 0.25–0.65 bpp, and 0.33–0.70 bpp for each CFAtype, respectively, compared with the case of “Direct” and“ReRGB” (no color decorrelation). Also, the VMSSTs showedcomparable performance with the KLT despite their simple
structures composed of Haar transforms. Moreover, in lossy
compression, the VMSSTs improved the average BD-rates
by around 3.97 %, 14.95 %, and 18.65 % for each CFAtype, respectively, compared with the case of “ReRGB.” Inparticular, larger improvements were seen in the case of the3 × 3 macropixel/submacropixel, such as in diagonal stripe
CFA and X-Trans CFA, than in the 2 × 2 submacropixel case,such as in modified Bayer CFA. We consider that there isa trade-off between the effectiveness of color decorrelation
using nearby components and the complexity depending on
the size of the macropixel. Although the proposed VMSSTs
could not achieve comparable performance with the KLT inlossy compression, it can provide both lossless and lossy
compression with fewer computational operations.3
V. C ONCLUSION
We presented VMSSTs for arbitrary RGB raw images, notconstrained by a Bayer CFA. The transforms were derived byreformulating the conventional MSST for the most popular
Bayer raw images as the following two steps: 1) intra-color
decorrelation and 2) inter-color decorrelation. The experiments
at compression with the VMSSTs and JPEG 2000 showed
good compression performance because of the high effectiveness of color decorrelation.
3 The KLT matrix should be calculated and transmitted to the decoder.

5

R EFERENCES[1] B. E. Bayer, “Color imaging array,” 1976, U.S. Patent 3 971 065.
[2] R. Lukac and K. N. Plataniotis, “Color filter array: Design andperformance analysis,” IEEE Trans. Consum. Electron., vol. 51, no.
4, pp. 1260–1267, Nov. 2005.
[3] “Fujifilm X-Trans sensor technology,” Fujifilm UK Official Website [Online], July 2003, Available: https://www.fujifilm.eu/uk/products/digitalcameras/model/x-pro1/features-4483/aps-c-16m-x-trans-cmos.
[4] “Kodak DCS620x review,” Digital Photography Review [Online], June
2000, Available: http://www.dpreview.com/reviews/kodakdcs620x/.
[5] “Sony announce new RGBE CCD,”
Digital Photography
Review[Online],
July
2003,
Available:
http://www.dpreview.com/articles/1471104084/sonyrgbeccd.
[6] E. T. Chang, “High-sensitivity infrared color camera,” 2005, U.S. Patent
20070145273.
[7] K. Hirakawa and P. J. Wolfe, “Spatio-spectral color filter array designfor optimal image recovery,” IEEE Trans. Image Process., vol. 17, no.
10, pp. 1876–1890, Oct. 2008.
[8] C. C. Koh, J. Mukherjee, and S. K. Mitra, “New efficient methods ofimage compression in digital cameras with color filter array,” IEEE
Trans. Consum. Electron., vol. 49, no. 4, pp. 1448–1456, Nov. 2003.
[9] K.-H. Chung and Y.-H. Chan, “A lossless compression scheme for Bayer
color filter array images,” IEEE Trans. Image Process., vol. 17, no. 2,
pp. 134–144, Feb. 2008.
[10] S. Kim and N. I. Cho, “Lossless compression of color filter array imagesby hierarchical prediction and context modeling,” IEEE Trans. Circuit.
Syst. Video Technol., vol. 24, no. 6, pp. 1040–1046, June 2014.
[11] M. Lakshmia, J. Senthilkumarb, and Y. Suresh, “Visually lossless compression for Bayer color filter array using optimized vector quantization,”
Appl. Soft Comput., vol. 46, pp. 1030–1042, Sep. 2016.
[12] N. Zhang and X. Wu, “Lossless compression of color mosaic images,”
IEEE Trans. Image Process., vol. 15, no. 6, pp. 1379–1388, June 2006.
[13] H. S. Malvar and G. J. Sullivan, “Progressive-to-lossless compressionof color-filter-array images using macropixel spectral-spatial transformation,” in Proc. DCC’12, Snowbird, UT, Apr. 2012, pp. 3–12.
[14] M. Hernández-Cabronero, M. W. Marcellin, I. Blanes, and J. SerraSagristà, “Lossless compression of color filter array mosaic images
with visualization via JPEG 2000,” IEEE Trans. Multimedia, vol. 20,
no. 2, pp. 257–270, Feb. 2018.
[15] Y. Lee, K. Hirakawa, and T. Q. Nguyen, “Camera-aware multi-resolution
analysis for raw image sensor data compression,” IEEE Trans. Image
Process., vol. 27, no. 6, pp. 2806–2817, June 2018.
[16] T. Richter and S. Fößel, “Bayer pattern compression with JPEG XS,”in Proc. ICIP’19, Taipei, Taiwan, Oct. 2019, pp. 3177–3181.
[17] T. Suzuki, “Wavelet-based spectral-spatial transforms for CFA-sampled
raw camera image compression,” IEEE Trans. Image Process., vol. 29,
no. 1, pp. 433–444, Dec. 2020.
[18] A. Skodras, C. Christopoulis, and T. Ebrahimi, “The JPEG2000 still
image compression standard,” IEEE Signal Process. Mag., vol. 18, no.
5, pp. 36–58, Sep. 2001.
[19] W. Sweldens, “The lifting scheme: A construction of second generation
wavelets,” SIAM J. Math. Anal., vol. 29, no. 2, pp. 511–546, 1998.
[20] H. S. Malvar, G. J. Sullivan, and S. Srinivasan, “Lifting-based reversible
color transformations for image compression,” in Proc. SPIE Appl. Digit.
Image Process. XXXI, San Diego, CA, Aug. 2008, vol. 7073, pp. 1–10.
[21] G. K. Wallace, “The JPEG still picture compression standard,” IEEE
Trans. Consum. Electr., vol. 38, no. 1, pp. xviii–xxxiv, Feb. 1992.
[22] F. Dufaux, G. J. Sullivan, and T. Ebrahimi, “The JPEG XR image coding
standard,” IEEE Signal Process. Mag., vol. 26, no. 6, pp. 195–199, 204,
Nov. 2009.
[23] S. Andriani, H. Brendel, T. Seybold, and J. Goldstone, “Beyond the
Kodak image set: A new reference set of color image sequences,” inProc. ICIP’13, Melbourne, Australia, Sep. 2013, pp. 2289–2293.