IIEEJ Transactions on
IIEEJ Transactions on
Image Electronics and Visual Computing

Image Electronics andVisual Computing

Vol. 11, No. 1 2023

I.I.E.E.J

Editorial Committee of IIEEJ
Editor in Chief
Osamu UCHIDA (Tokai University)

Vice Editors in Chief
Naoki KOBAYASHI (Saitama Medical University)
Yuriko TAKESHIMA (Tokyo University of Technology)

Advisory Board
Yasuhiko YASUDA (Waseda University Emeritus)
Hideyoshi TOMINAGA (Waseda University Emeritus)
Kazumi KOMIYA (Kanagawa Institute of Technology)
Fumitaka ONO (Tokyo Polytechnic University Emeritus)
Yoshinori HATORI (Tokyo Institute of Technology)
Mitsuji MATSUMOTO (Waseda University Emeritus)
Kiyoshi TANAKA (Shinshu University)
Shigeo KATO (Utsunomiya University Emeritus)
Mei KODAMA (Hiroshima University)

Editors
Yoshinori ARAI (Tokyo Polytechnic University)
Chee Seng CHAN (University of Malaya)
Naiwala P. CHANDRASIRI (Kogakuin University)
Chinthaka PREMACHANDRA (Shibaura Institute of Technology)
Makoto FUJISAWA(University of Tsukuba)
Issei FUJISHIRO (Keio University)
Kazuhiko HAMAMOTO (Tokai University)
Madoka HASEGAWA (Utsunomiya University)
Ryosuke HIGASHIKATA (FUJIFILM Business Innovation Corp.)
Yuki IGARASHI (Ochanomizu University)
Tomokazu ISHIKAWA（Toyo University）
Masahiro ISHIKAWA （Saitama Medical University）
Naoto KAWAMURA (Canon OB)
Shunichi KIMURA (FUJIFILM Business Innovation Corp.)
Shoji KURAKAKE (NTT DOCOMO)
Kazuto KAMIKURA (Tokyo Polytechnic University)
Takashi KANAI (The University of Tokyo)
Tetsuro KUGE (NHK Engineering System, Inc.)
Koji MAKITA (Canon Inc.)
Tomoaki MORIYA (Tokyo Denki University)
Paramesran RAVEENDRAN (University of Malaya)
Kaisei SAKURAI (DWANGO Co., Ltd.)
Koki SATO (Shonan Institute of Technology)
Syuhei SATO（Hosei University）
Masanori SEKINO (FUJIFILM Business Innovation Corp.)
Kazuma SHINODA (Utsunomiya University)
Mikio SHINYA (Toho University)
Shinichi SHIRAKAWA (Aoyama Gakuin University)
Kenichi TANAKA (Nagasaki Institute of Applied Science)
Yukihiro TSUBOSHITA (Fuji Xerox Co., Ltd.)
Daisuke TSUDA (Shinshu University)
Masahiro TOYOURA (University of Yamanashi)
Kazutake UEHIRA (Kanagawa Institute of Technology)
Yuichiro YAMADA (Genesis Commerce Co.,Ltd.)
Norimasa YOSHIDA (Nihon University)
Toshihiko WAKAHARA (Fukuoka Institute of Technology OB)
Kok Sheik WONG (Monash University Malaysia)

Reviewer
Hernan AGUIRRE (Shinshu University)
Kenichi ARAKAWA (NTT Advanced Technology Corporation)
Shoichi ARAKI (Panasonic Corporation)
Tomohiko ARIKAWA (NTT Electronics Corporation)
Yue BAO (Tokyo City University)
Nordin BIN RAMLI (MIMOS Berhad)
Yoong Choon CHANG (Multimedia University)
Robin Bing-Yu CHEN (National Taiwan University)
Kiyonari FUKUE (Tokai University)
Mochamad HARIADI (Sepuluh Nopember Institute of Technology)
Masaki HAYASHI (UPPSALA University)
Takahiro HONGU (NEC Engineering Ltd.)
Yuukou HORITA (University of Toyama)
Takayuki ITO (Ochanomizu University)

Masahiro IWAHASHI (Nagaoka University of Technology)
Munetoshi IWAKIRI (National Defense Academy of Japan)
Yoshihiro KANAMORI (University of Tsukuba)
Shun-ichi KANEKO (Hokkaido University)
Yousun KANG (Tokyo Polytechnic University)
Pizzanu KANONGCHAIYOS (Chulalongkorn University)
Hidetoshi KATSUMA (Tama Art University OB)
Masaki KITAGO (Canon Inc.)
Akiyuki KODATE (Tsuda College)
Hideki KOMAGATA (Saitama Medical University)
Yushi KOMACHI (Kokushikan University)
Toshihiro KOMMA (Tokyo Metropolitan University)
Tsuneya KURIHARA (Hitachi, Ltd.)
Toshiharu KUROSAWA (Matsushita Electric Industrial Co., Ltd. OB)
Kazufumi KANEDA (Hiroshima University)
Itaru KANEKO (Tokyo Polytechnic University)
Teck Chaw LING (University of Malaya)
Chu Kiong LOO (University of Malaya) F
Xiaoyang MAO (University of Yamanashi)
Koichi MATSUDA (Iwate Prefectural University)
Makoto MATSUKI (NTT Quaris Corporation OB)
Takeshi MITA (Toshiba Corporation)
Hideki MITSUMINE (NHK Science & Technology Research Laboratories)
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Kouichi MUTSUURA (Shinsyu University)
Yasuhiro NAKAMURA (National Defense Academy of Japan)
Kazuhiro NOTOMI (Kanagawa Institute of Technology)
Takao ONOYE (Osaka University)
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IIEEJ Transactions on
Image Electronics and Visual Computing
Vol.11 No.1 June 2023
CONTENTS

Invited Paper
1

Arithmetic Codes for Image Coding : Their Advantages and the Effort to Make
Them Practical

Fumitaka ONO

Contributed Paper
13

ECA-Resunet: Lung Segmentation of CT Images Based on an Efficient
Channel Attention Mechanism Deep Residual-Unet

Jincheng PENG, Ruigang GE, Guoyue CHEN, Kazuki SARUTA,
Yuki TERATA

Announcements
22

Call for Papers : IEVC2024 National Cheng Kung University, Tainan City,
Taiwan Mar. 11‐14, 2024

24

Call for Papers: Special Issue on Design and Implementation Technologies to
Support Immersive Video Communication and Distribution

Guide for Authors
25

Guidance for Paper Submission

Published two times a year by the Institute of Image Electronics Engineers of Japan（IIEEJ）
3-35-4-101，Arakawa，Arakawa-ku，Tokyo 116-0002，Japan
Tel：+81-5615-2893 Fax：+81-5615-2894 E-mail：hensyu@iieej.org http://www.iieej.org/

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

Arithmetic Codes for Image Coding: Their Advantages and the Effort to Make Them Practical
Fumitaka ONO (Honorary Member)Tokyo Polytechnic University<Summary> The arithmetic code has come to be used for image coding about 40 years ago. It was first adopted in bi-level image coding
standard and nowadays widely adopted in multi-level image coding and video coding standards. Arithmetic code is classified as a non-blockcode in the information theory, and quite powerful by its ability and robustness for various target images. In this paper, its largest advantage
for coding multi-context sources is described by comparing with the case using block codes. Additional advantage of arithmetic coding is its
robustness based on the affinity with statistic learning. Therefore, the separation of model and entropy coding can be easily done and thecurrent context and the observed symbol are enough for applying an arithmetic coding, while code set design and selection rule from theplural codes will be required in Huffman coding. Also, there were various efforts to make the arithmetic codes practical by referring to thedesigning parameters of known code including MELCODE.
Keywords: arithmetic code, Markov-model, entropy coding, international standard, MELCODE

1. Introduction
1.1 Background
The arithmetic code has come to be used for image
coding about 40 years ago. It was first adopted inimage coding standard about 30 years ago for JBIG.
And the target has expanded from bi-level image
standard to multi-level image and video standards.
Fig.1

Figure 1 shows the block diagram of image
coding/decoding

process.

The

image

codingimage coding standards in these days, such text booktechnology is composed of the image modeling andhas come to mention about it as the understanding of

the entropy coding. The image modeling is thesourceconversion

into

the

sourcehavingits essence is quite important.

less

In order to understand the reason why arithmetic

entropy than that of the original. The quantization ofcode has been adopted so widely in international

the parameter of the converted source can be also

standards recently, I would like to describe about theincluded.

advantage of the arithmetic coding by referring to

The entropy coding is the process of converting

the background of the initial

the observed event to the codeword having the

researchers have made continuous effort to put

The entropy coding can be classified into blockarithmetic code more practical.

coding (table base) and non-block coding (procedure

1.2 Basis of entropy codingbase) in information theory. The representative of thefirst type is Huffman coding and that of the second is

The entropy of the observed event having thethe arithmetic coding.
recently,arithmetic

adoption of thearithmetic code to the facsimile coding, and how

corresponding information amount (entropy).

Until

Block diagram of coder and decoder

probability p is calculated by log 2 (1/p) bit. So thecodingwasnot

aim of entropy coding is how to assign codeword

introduced in the source coding chapter of thehavingtextbook of information theory for beginners. But asprobability p.

arithmetic coding has come to be adopted in various(log 2 (1/p))

bit

for

the

event

having

The entropy coding process can be composed of1

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

following three steps:
1)

Recognition of the probability：Recognition ofthecontext

inconnection

withsourceoftheprobability:

Staticmodeling.
2)

Estimation

estimation and dynamic estimation can beconsidered. The former is based on statistic
data, and the latter will use such as Bayes
estimation, state transition rule, etc.
3)

Realization of codeword having (log 2 (1/p))
bit: Technique to assign appropriate codeword.
The performance of 2) and 3) will correspondFig.2 Most simple NLC example

to the coding efficiency.
theory and coding” by Norman Abramson 1) . Its

As stated above, the entropy coding technology canefficient coding scheme proposed by P. Billingsley is

be classified into block coding (such as Huffman

also referred in the book.

code) and non-block coding (such as arithmetic code).

If we use the code table below, the range of the

Huffman code is further classified to, a) Custom

address X (binary) will be,

Huffman code, b) General Huffman code set, and c)

if the first symbol is A, 0.0≦ X<0.1, andFixed Huffman code. The first one is the primary

if the first symbol is B, 0.1≦ X<1.0

meaning of Huffman code but constructing Huffman

The decoding of the first symbol can be done bycode for individual source will not be so realistic.

comparing the binary number (corresponding to

The second one can be regarded as true Huffman

codeword sequence) with 0.1 (boundary address). So,code if correct code selection algorithm is applied.

the area of symbol sequence will be divided to upper

The third one is most simple one but not robust

half and bottom half of the numerical line, according

depending on the target source.

to the fact whether first symbol is A or B. The area

2. Basic Knowledge of Arithmetic Coding

between 0.0and 0.1(binary) is for A, and the area
between 0.1(binary) and 1.0 is for B.

2.1 The origin of arithmetic coding

2.2 Principle of arithmetic coding

The origin of the arithmetic coding is considered
as “Numerical line coding (NLC)]” to represent the

The codeword in NLC can be expressed by its

area in the interval of [0,1] line, when the line isaddress, but the necessary number of address bits

divided into areas of symbol sequences based on thewhich corresponds to the length of codeword must be

probabilities. For example, consider a memorylessrecognized. For example, the address of 0.1 and thesource with two symbols A and B, occurring withaddress of 0.100 should be distinguished, as theprobability 0.5 and 0.5 respectively, as shown incodeword of the former is “1”, and the codeword of

Fig.2. If we assign “A” area to the bottom and “B”

the latter is “100”. The address of 0.1 will mean “B”,

area to the top according to the ratio of theand the corresponding area size is (1/2) because theprobability, arbitrary sequence starting with “A”,

probability of observing B as the first symbol is 0.5

from such a source, will be assigned in the interval

and any sequence starting with B is allocated[0, 0.1](binary). Also the sequences starting with

between 0.1 and 1.0, and the address of 0.1 will“AB” are assigned to the interval [0.01, 0.1] (binary)

include all sequences starting with B. On theetc.

contrary, the address 0.100 will mean “BAA”, andIn NLC, to interpret the binary expressed address

the area size expressed by the address of 0.100 is 1/8of the point in the final area, just below the decimal

and will mean the probability of all sequences

point, as the codeword sequence will be possible.

starting with BAA. Therefore it is important toElias 1 ) ,and hisconsider the area size expressed by the address,

idea was introduced in the textbook “Information

which is the codeword itself. It should be noticed

This idea was proposed by Peter

2

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

that the area size of (1/8) will not guarantee that
corresponding address can be expressed by 3bit. It
will be discussed later in 2.4 but the difference ofnecessary bit length in the worst case is at most one.
The principle of the arithmetic coding is simply
given by the following fact. The message of which
codeword is n bit can be considered to be given thearea of (1/2) n in the numerical line. The information
theory tells that, the ideal codeword length n for themessage having probability p is given by
n =log(1/p) bit
the minimum s will be

bit codeword (either 011 or 100) will be needed. Thiss= (1/2) nfact corresponds to the case mentioned in 2.2.

Then the ideal area size s for the message, of

In Huffman code, necessary code length L B will bewhich probability is p, is given by
s= 1/2 lo g ( 1/ p ) =p∴log(1/P) ≦ L B ＜ log(1/P)＋ 1

s=p

By the way, in the block coding method restricting

It is very simple result that ideal area size s in

the binary order of codeword to follow symbols’

arithmetic coding is its probability p. Therefore, inalphabetical

NLC, the area of each symbol should be proportionalorder(knownasGilbert-Moore

coding 3 ) ) necessary code length L G will beto its probability 2 ) .

log(1/P)≦ L G ＜ log(1/P)＋ 2

2.3 General definition of arithmetic coding

2.5 Renormalization and flush

In arithmetic coding, the symbol area will bea) Renormalization

divided according to its symbol probability in the

As coding procedure progresses, the valid area,

numerical line [0,1), as encoding procedure will

which is called as “augend” decreases. If the augend

proceed.

decreases less than half of the full augend size, theIf the final symbol in the sequence is given, theaugend is doubled and the accuracy of the area of the

final area corresponding to the symbol sequence, insymbol will be kept. This procedure is called

the numerical line [0,1), will be checked. A binary“renormalization”. If the augend is still less than half

number γ, of which under decimal point bit is N, willof the full size, such procedure will be repeated.

express the area [γ,γ+2 - N ). The minimum number ofIn binary arithmetic coding, MPS (More Probable

bits to be able to express the part included in the

Symbol) /LPS (Less Probable Symbol) recognition is

final area will be used as the codeword of theconvenient because the probability of MPS can bearithmetic coding.

2.4

The difference of the necessary code length

Fig.3

If the area size s is corresponding to n bit code,

Necessarycoding 2)

restricted to ≧ 0.5, and the probability of LPS can

code

lengthinbe restricted to ≦ 0.5.

arithmetic

If LPS occurs, renormalization will always happen,
and if MPS occurs, renormalization will not always

Let the probability of symbol sequence as P, thehappen.

necessary code length L A in the arithmetic coding
will be as following, because the assignment of

Figure 4 shows an example of the renormalization.

symbol areas should follow alphabetical order (orLet the probability of MPS to be 3/4. If MPS will

predefined order) and rearranging is not allowed.

happen, the valid area will be decreased to 3/4, aslog(1/P) ≦ L A ＜ log(1/P)＋ 2

shown in the area of right side described as the

Figure 3 shows the example of the difference ofcombination of (00,01,10), and the area will be

the necessary codelength. If the final area size of B

further divided according to the probabilities ofis 1/4 and it locates between 0.01 and 0.10, as left,

coming symbols. If LPS will happen, the valid area

the codeword of 01 (0.01≦ x<0.10) can be used. But

will become 1/4 of the original as painted yellow.

if it locates between 0.011 and 0.101, as right, three

Then the valid area shall be enlarged 4 times (2 more

3

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

bit in the address) to keep the necessary augend
accuracy. In the Figure, as the top two bits of theyellow area is determined as “11”, we can put out thecodeword of “11” to the channel and we can use thefull area just as shown in left, for coming symbols.
b) Flush
Flush is the activity to put out the final code
sequence finishing or quitting the coding procedure.
In the type of flush, so-called minimum flush will
put out the minimum code sequence to the channel
for decoding the last symbol. There is a possibility

Fig.4

An example of renormalizationthat additional MPS will be decoded if the number ofsymbols is unknown to the decoder. By the way, suchinto three categories. They are 1) Bi-level images

phenomenon is not due to the arithmetic coding

including text/line-art and halftone 2) Still colour

procedure and it will also happen in block code withimages including natural photos, CG and compoundsource extension too. In order to inform the number

images, and 3) Motion pictures including naturalof symbols, using the control code beforehand isvideo, and animation (CG). The standardization andpopular and other methods to devise the flush format

the application are also classified into these fields.

is also possible.

3.1 Bi-level image coding

2.6 Decoding procedure 4)

For two-dimensional bi-level image coding, there

At first, the lowest address and the highest

were two ideas. The first one was two-dimensional

address of the valid area will be set at the decoder.

Run Length coding 5 ) - 8) , which utilizes the relation of

By comparing the boundary address of symbols andtwo-dimensional

the address given by the received code sequence,

Markov-model coding 9 )- 1 3 ) in which the conditional

initial symbol will be decoded. In the decoding

entropy of neighboring reference pixels will become

register, the current area will be detected and if

the target entropy. The character and line-art images

renormalization is needed we will double the area aswill be efficiently coded in both models, and in therequired, and read the necessary bits of the code

secondsequence into the decoding register. The decodability

efficiently coded.

model,

edges.

halftone

The

images

other

one

will

be

is

also

is guaranteed whatever code sequence will follow in

In two-dimensional Markov-model coding, source

the future. If renormalization is needed and there areextension of multi-context source is needed, which

no more data in the code sequence, we can know thathas not been theoretically treated, when this issue

we can stop decoding. At that time, it will happen

was studied world-wide about middle of 1970s, andthat dummy MPSs can be decoded at the decoder.

various practical methods were proposed. We have

But it is not unique to arithmetic coding, sincemade clear that the source extension of multi-context

similar case will happen in block code which adopts

source can be done by either sequential extension orsource extension, too.

context-based extension 4 ) . In sequential extension,

In some coding standards, the length of the code

the coming context has to be recognized beforehandsequence may be restricted to the multiplication of

based on the watched symbol, and the Huffman codes

eight. In that case, additional zero data will be added

corresponding the combination of various MPS

or primarily necessary zero data will be suppressed,

probability should be prepared. In context-based

if possible. In such a standard, the number of picture

extension, codewords for different contexts will beelements

sentcreated in parallel, and to keep the right order of the

beforehand to let the decoder decode the correcttransmission, waiting for the completion of thenumber of events.

codeword

tobedecoded

must

have

been

of

other

contexts

andcodewordinterleaving will be needed. Also memory control of

3. Markov-model Coding

codeword

data

will

memory circumstances.

From coding technology aspect, image is classified

4

berequiredunder

limitedIIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

binary Huffman
Table 1messagecode tothe binary sources 1 5 ) .

Block type MELCODE

Though various breaking down method can benumber ofsymbols

considered, N-level source will be divided into (N-1)

000･･･
1
01
・
・
・
000･･
000･･
・
・
000･･･

codeword

M
1
2
・
・
・
M1
M 1 +1・・M

binary sources to keep the entropy.

1m

4. MELCODE

・・

4.1 Block type MELCODE 14)

m
m+1
・
・
m+1

proposed by us more than 40 years ago. In studying

MELCODE is a general binary Huffman code set
bi-levelMarkov-model

image

codingusing

context-based extension, general Huffman code set to
be chosen for the probability model of the context isneeded to develop. Let us call an integer parameter
M as “order”. In MELCODE, consecutive M MPSs

Table 2will be coded by one bit codeword. Less than M MPS

Examples of Block type MELCODE

plus one LPS will be coded by fixed length code of(m+1) bit, if M is 2^m.
If M is not a power of 2, we will define m and M 1 ,satisfying the following formulas,2^(m-1)<M≦ 2^m
M 1 =2^m-M

So, it can be said that context-based extension ismore practical, but sequential extension is also

The code for any M is defined in Table 1. Some code

possible if the Huffman code set to use is restricted

examples for M can be found in Table 2.
In our study of binary Markov-model coding,by tolerating the degradation of the efficiency.
Our

using

context-based extension method will need a code set

shown in Chapter 4 has achievedfor group of the context, and MELCODE works very

Markov-model

MELCODE 1 4 )codingmethod 1 0)

well for that purpose, as shown in 3.1.

almost same performance compared with the best of
two-dimensional RL coding

methods 6) , 7)

If switching the set of predefined orders for thein facsimile

distribution

coding domestic standardization discussion.

range

of

assumedprobability,theLater, the nature of the arithmetic coding has beenminimum efficiency will be given at the switching

made clear 1 5 ) , and if we use arithmetic coding,point. In case of block-type MELCODE, if the ordercoding procedure equivalent to sequential extension

is limited to 2’s power, the minimum efficiency will

can be executed without worrying about complex

be given at the switching point of orders 1 and 2, andcode design needed in sequential extension whenminimum efficiency will be about 0.959, as shown inusing

Fig.5.

block

code,

or

codewordinterleaving

procedure needed in context-based extension when

4.2 Arithmetic type MELCODE 16)

using block codes.

After the discovery of the nature of arithmetic

So, it can be said that multi-context source coding

code, which is the convenience for Markov-modelwith source extension was elegantly solved by thecoding,use of arithmetic coding 2 ) .

we

have

tried

to

extend

block

type

MELCODE to arithmetic type MELCODE, keeping

3.2 Multi-level image coding

the same efficiency in multi-context coding. It has
been proposed by us more than 30 years ago, and has

Even in multi-level images, there will be a

been adopted in several standards.

context which needs source extension to keep thecoding efficiency. In order to utilize the study result

Block type MELCODE, when order M=2^m, canof bi-level image coding, described in 3.1, we havebe considered as arithmetic code of constant LPS

proposedthearea regardless to the size of the augend. In Fig.6,

multi-level sources into binary sources and using

the case of M=4, where LPS area width is 1/8

themethodofbreaking

down

5IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）(=1/(2*M)), is shown. In single context coding, theaugend will take one of (1, 7/8, 6/8, 5/8), and the
LPS probability can be considered to alter as 1/8, 1/7,
1/6, and 1/5 according to the augend.
a)

How to define arithmetic MELCODE
In block type MELCODE, the augend will takelimited value, because of the context-based extension,and LPS area size will be always 1/(2*M) for M,
when it satisfies the power of 2. In consideringarithmetic

MELCODE,extensionofaccompanied,assequential

multi-context
theaugendsources

will

take

Fig.5

sourcewill

any

Coding efficiency of MELCODE

be
value

between 1/2 and 1. Therefore the LPS area size
corresponding to any augend size should be defined.
For the case of M=4, the augend is restricted to
one of (1, 7/8, 6/8, 5/8) in single context coding and
LPS area size is always 1/8. So, it will be natural todefine LPS area size to be 1/8 if the augend is
between 1 and 5/8. As the augend will not be lessthan 1/2, we have to define the LPS area size when

Fig.6 MELCODE of M=4 treated as arithmetic code

the augend area is between 1/2 and 5/8. If weconsider the case of the augend is 1/2, the LPS areasizeshouldbe

1/16,

becauseafterarithmetic MELCODE is introduced.

theConcerning

renormalization to follow, the augend size will be 1,

size

z

will

satisfytheshows the efficiency when the orders of 1, 1.5, 2 andfollowing

3 are used as a set, the minimum efficiency will beinequality.
q=z/2-3/16

ifraised to about 0.985 which will be given at the

1/2 <z<5/8

switching point of the orders of 1 and 1.5.

Generally when the order is M and the augend is z,

By the way, for block-type MELCODE, of which

the LPS area size q will be given by following rule.
q=(1/M 2 )arithmetic

considered between the order of 1 and 2. Figure 7

be natural to define LPS area size q as following if
augendorder

MELCODE, for example, order value of 1.5 can be

and LPS area size will be 1/8. From this fact it willthenon-integerfororder is not the power of 2, the LPS size is notz>{1/2+(1/M 2 )}constant. Figure 8 shows the case when the order isq=(1/M 2 )-(1/2){1/2-(z-1/M 2 )}3, where symbol “0” means MPS, and symbol “1”for {1/2+(1/M 2 )} ≧ z ≧ 1/2

means LPS. If we introduce arithmetic MELCODE ofwhere M 2 =2*M

which order is 3 according to the definition given

We are calling the operation when {1/2+(1/M 2 )} ≧ zabove, the LPS size will be 1/6, that is 1/M 2 , and theas Over Half Processing (OHP) because the LPS areaaugend value in single context coding is shown as

above the address of 0.1 (binary) should be madeleft. From Fig.8, it can be said that the efficiency

half. It should be noted that OHP will always

will be same for block type coding (right) and foraccompany renormalization whether observed symbol

arithmetic coding (left) since LPS probability is one

is MPS or LPS. Therefore, when OHP occurs, theof (1/6,1/5,1/4) set in both cases.

occurred symbol area (despite of MPS or LPS) must

4.3 Set of probability models

be at least doubled. So, the halving operation isIn block type MELCODE, the average efficiency

actually not needed.

or minimum efficiency can calculated for the set ofb) Introduction of OHPtheOHP can be considered as the solution to theordersrestricted

to

two’s

power.

Forthedefinition of arithmetic MELCODE for applying

arithmetic MELCODE, to keep the consistency withmulti-context coding and also if non-integer order

block-type MELCODE, the order was limited to6

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

two’s power at first. However, since arithmetic
MELCODE can define non-integer value of the order,
it will be natural to design the set of orders from theviewpoint of minimum efficiency. Such optimization
will be effective especially for large LPS probability
case 1 7 ) .

5. Design issues of Practical Arithmetic
Codes
Fig.7 Introduction of no-integer order MELCODE

5.1 Design parameters of AC
Major design parameters of AC (Arithmetic Code)will be given as following.
1)

Binary or M-ary: Binary is currently quite
popular, and even for multi-level sources, usageof binary AC with the process of breaking down
the

M-ary

sourceintobinarysourcesisdominant.
2)

Register size (bits): This is corresponding tosymbol probability accuracy. Long register size

Fig.8 MELCODE of M=3:Block-type(left) and arithmetic
type (right)

is required for high skew data, such as highresolution

facsimile.

Theregister

size

of
8)

QM-coder 1 8) used in JBIG standard is 16 bit, andstate transition diagram is popular. This is

this will be considered to be satisfying.
3)

related

Symbol area size assignment rule: This will be

Carry control method: Various methods are7)

dynamiccodingefficiency,

In the AC, symbol area width will be ideally

proposed as described later.

calculated

Adoption of MPS/LPS in binary AC: MPS/LPS

probability]. Valid area width will be varying among

concept

1/2 and 1, when maximum augend is defined as 1,

isconvenient

since

the

symbolprobability range can be halved as probability of

6)

the

5.2 Symbol area size assignment rulecharacteristic of the individual AC.

5)

totogether with 3) and 6).

most important since this issue can be the main
4)

Probability estimation and update timing: To use

by[valid

area

width

(Augend)×under the usual usage of renormalization.

MPS will be ≧ 0.5 and the probability of LPS

The multiplication procedure is not practical as

will be ≦ 0.5, with judging process of MPS/LPS.

the area size to be assigned should be digitalized into

Which symbol of MPS/LPS should be allocatedgiven accuracy of register size and it must be

to the larger/smaller side of the numerical line iscoincident between encoder and decoder. From thatup to the designer of the system.

sense, the area size should be tabularized, and using

Number of probability models: For the given

the less table size will be more convenient.

range of LPS probability, we will assume a set of

The first practical arithmetic code is LR code 1 9) ,model sources having representative values of an

proposed by G. G. Langdon & J. J. Rissanen, from

LPS probability. The target source will beIBM. In LR code, LPS probability is limited to theapproximated by one of the model sources. Thispower of (1/2). So, the area size of LPS can beparameter is related to the statistic coding

calculated by shifting operation of the augend. The

efficiency, together with 3).

second practical arithmetic code is Q-Coder 2 0) also

Renormalization timing: Renormalization will be

proposed by IBM. As the LPS size is fixed regardlessdone soon after the augend has decreased to lessof the size of the augend, this is called as thethan half of the full augend size, but some AC

subtraction type arithmetic coding. MELCODE as

will

described in 4. is the combination of fixed size LPS

do

the

renormalization

less

often

for

simplification and also for speed-up

7

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

and OHP. QM-coder 1 8 ), 2 1 ) and MQ-coder 2 2) are both

source will be approximated by one of the model

adopting fixed size LPS and MPS/LPS conditionalsources. Therefore, the minimum efficiency for theexchange. It can be said the performance of fixed

given range of the LPS probability will be given atsize

the switching point of two

LPS

will

be

degradedaroundthe

LPS’sneighboring

model

probability is 0.5, and MELCODE and QM/MQ hassources. The multi-context source will be composed

the effect of improving such case.

of multiple sources depending on the context.
In

5.3 Carry over problem

sequence

will

and

highest0.0111111…,

the

be

0.100000…
bit

will

Concerning the dynamic efficiency, state transition
rule to move one of the model sources will be

In order to solve this issue, various controllingdesigned 2 0 )- 2 2 ) . The number of states can be larger

methods have been studied as following. In all

than the number of model sources. The timing of themethods, the encoder and the decoder will take sametransition of the states will be the appearance of LPS

procedure.

or MPS, and at least counter will be needed forBit stuffing method 2 0 ) : Tentative result will be

MPS 2 4) . The counter will be also necessary for LPS

sent out not waiting the final result. The final

having larger probability. To utilize renormalization

result will be sent later using controlling code.

timing will be quite effective as in Q-coder 2 0) .

Moving the boundary method 2 3) : To move the

5.5 Basic comparison of AC and HC

MPS/LPS boundary to just 0.1 compulsorily,sacrificing the loss of some coding efficiency.
3)

Discard the smaller area

method 2 3 ) :models,curve of switching best of the model sources.

the codeword into the transmission line will happen.

2)

probability

The static coding efficiency will be given by thebedetermined for long time, and the delay to put out

1)

the

efficiency of the switching point same will be usual.

or

not

designnumber of models is assumed first, to let theand lower side of 0.1, there is the possibility of the
code

to

minimum efficiency can be first assumed. If the

For example, if the valid area covers both upper
final

order

As described in Chapter 4, basically AC can be

To change

considered as the extended form of HC (Huffman

the top address or bottom address intentionally

Code). In HC, the all symbol sequence is divided by

to be 0.1 by discarding either of upper or lower

the collection of messages, and the probability ofarea of 0.1, sacrificing the loss of some coding

each message is limited to be (1/2) n , where n is anefficiency.

integer. On the contrary, in AC, there is no concept

By the way, in order to create a set of control codesof message,

starting FF, zero-insertion will be taken place. Thissequence will be limited to (1/2) n , where n is an

issue

integer.

and

carry

over

problemareprimarily

and the probability of only total

Therefore,

AC

will

approximate

theindependent but bit stuffing will be done as a part ofprobability of all symbol sequence by the power ofthe former work.

(1/2), the efficiency will be theoretically higher than

5.4 Coding efficiency of binary ACthat of HC.
For the adaptability, the update timing of HC is

The multi-context source will be composed oflimited to a break between messages, while AC can

multiple sources depending on the context.
For

the

distribution

of

LPS

probability

choose any timing, for example, timing when theofsymbol counter reaches predefined value, timing ofmultiple sources, we will prepare a set of model

renormalization, etc.. Concerning the separation ofsources having representative values of an LPS

model and entropy coding, the current context andprobability. The target source will be approximatedthe observed symbol are enough to be given for AC.

by one of the model sources. Therefore, the minimum
efficiency

for

the

assumed

range

of

the

While code design and selection rule from the plural

LPS

codes will be required in HC.

probability will be given at the switching point of

In HC, source extension is done through thetwo neighboring model sources.

definition of the message. As described in 3.1, theFor the distribution of LPS probability, we willsequential source extension of multi-context sources

assume a set of model sources having representative

will ask to prepare enormous kinds of messages

values of an LPS (MPS) probability. The target

8

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

because the context to follow will take various
valuesofreferenceprobabilities.
events(pixels

So,

the

in

restriction

images)

will

1.00

of

0.99

be

accompanied in many cases, and still it is difficult to

0.97

design0.96

appropriate

codes.

As

for

the

Coding Efficiency

theideal coding efficiency

0.98

context-based extension, extended events of the samecontext will have same probability model and set ofcodes corresponding to the number of probability
models will be needed.
On the other hand, sequential source extension is

16 states

0.95

conventionaltransition (g=1)

0.94
8 states

0.93

g=0.9

0.92

g=0.8

0.91

g=0.7

0.90
1.00

possible by nature in arithmetic code, and the

g=0.91/2
g=0.81/2
g=0.71/2

extension of total sequence can be also possible. By

2.00

3.00

4.00
5.00
-log2(LPS probability)

6.00

7.00

Fig.9 The performance of STT-coder

this nature, arithmetic coding can be the powerfulsolution for Multi-context source coding.

is needed. Therefore, the trial to avoid the loss in

5.6 The well-known AC

flush will be required. Such idea will make the

The

first

arithmeticcode

appliedtoarithmetic coder possible to be implemented by statethetransition table.

Markov-model images (facsimile images) was LR

In studying STT-coder, a specific parametercode 1 9 ) , and it has succeeded to let researchers know

called “offset” is introduced. This parameter shows

the potency of the arithmetic code for Markov-modelthe difference between the bottom address of thesources. In the method, LPS probability is limited tocurrent valid interval and the address given by thethe power of (1/2). So, the area size of LPS can becalculated by shifting operation of the augend.

already determined code bit stream. It was found that

The arithmetic coding standardization of JBIG andthere is an optimum number of offset, which is four,and the best combination is (0, 16, 24, 28) in 6bit

JPEG standards was took place in ISO and CCITT.
For the purpose, Q-coder 2 0 ) (from IBM), arithmetic

register system. The coding efficiency of 6bit system

MELCODE 1 6 ) (from Mitsubishi) and MM-coder 2 5 )using 8 probability states shows 98.5% covering the(from AT&T) were proposed and QM-coder 1 8) was

range of MPS probability between 0.5 and 0.95.
For dynamic coding 2 8 ) , a concept of “transit LPS

newly defined as the sole standard. In QM-coder,
fixed size LPS area (by IBM) is modified byratio” was newly introduced especially for high LPS

MPS/LPS conditional exchange 1 8 ) (by Mitsubishi)probabilityand 113 states transition rule

21)

range,

and

using

16

statesand8

probability models, the efficiency of 96% was(by AT&T) was

adopted. Later in standardizing JPEG2000 and JBIG2,

achieved as shown in Fig.9.

MQ-coder 2 2) has been defined. In MQ-coder, 47

6.2 Multi-alphabet arithmetic coder 29)
The study of multi-alphabet arithmetic coder has

states transition rule was newly adopted. In JPEG LS
isnot been so popular. For the arithmetic coding of

combining CJ-coder and MELCODE was adopted.

multi-level sources, most popular method is to break

For H.264, CABAC 2 6 ) was introduced, and the LPS

down the multi-symbol source into binary symbol

area size in CABAC is fixed according to the 4sources. The advantage of this method is to utilize

classes of the augend size.

the knowledge of binary arithmetic coding. The

standard

part-2,arithmeticcodingwhich

disadvantage is taking longer time than multi-level

6. Recent studies

arithmetic coding.

6.1 STT-Coder (State Transition Table coder)

In designing multi-symbol arithmetic coding, theWe have proposed STT-Coder 27 ) to improve thefirst issue to be considered is source modeling. The

simplicity of the binary arithmetic codes. Its basic

mostidea is the trial to make retaining the bottom address

probability ratio of Nth and (N+1)th symbol R to beunnecessary, when renormalization is done. In otherconstant. Then we assume single parameter forwords, flush will be done whenever renormalization

modeling. Concerning the calculation of area size of9

simplemodeling

will

be

to

assumetheIIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

symbols, areas of 2nd to last symbols to be fixed

Table 3 Coding efficiency of multi-level arithmetic code

independent to the augend will be most practical. So
HuffmanArithmetic

Arithmetic

R

14bitsSingle table

Double table

If its efficiency is not satisfactory, classifying the

0.841

0.988

0.946

0.971

range of augend into two or more categories will be

0.707

0.985

0.958

0.973

necessary (for example, two tables will be prepared

0.5

0.986

0.964

0.973

for the smaller augend, which is between 0.5 and say

0.382

0.955

0.964

0.97

0.7, and larger augend, which is between say 0.7 and

0.293

0.871

0.961

0.966

1.0 ).

0.245

0.803

0.959

0.963

we

call

such

arithmetic

code

as

“Fixedtype

arithmetic code”.

We assume that the probability ratio of Nth and(N+1) th symbol to be constant as R. If the numberof symbols is enough large, the probability of the top

issue was solved in elegant way.

symbol will be (1-R).

symbol sequence can be created independent to otherefficiency with Huffman code not supposing source

symbol sequences. Also statistic update of theextension when the number of symbols is 256. In thecontext is possible at any timing. So, if the context

Table, for example, if R=0.245, the probability of top

and the occurred symbol are given, a universal code

symbol will be 0.755 and the advantage of thedesign is possible in arithmetic codes.

3

shows

In arithmetic coding, the codeword of occurred
codingTable

thecomparison

ofarithmetic coding over Huffman will be remarkable

These two issues can be considered as the main

because of source extension effect in arithmetic

advantage of the arithmetic codes for image coding.

coding. For larger R, the efficiency of arithmetic

Also, a lot of effort have been done to makecoding will be not so good as Huffman because ofarithmetic coding more practical. There are trade-off

the effect of fixed area allocation of arithmetic

between simplification and efficiency keeping, andcoding. The “double arithmetic” means the augend isfurther effort will be expected depending on theclassified

target sources and applications.

to

two

categories

and

the

“singlearithmetic” means that the augend is not classified.

Concerning the comparison of AC and HC, though

As the improvement of double arithmetic from single

the codeword creating mechanism is different, thearithmetic is not so large, single arithmetic sounds

relation of the entropy and mean code length per

enough.

symbol is theoretically same, if HC is created withalphabetical order as shown in 2.4. And though

7. Conclusion

codeword is given for the unit of message in HC andIn this paper, the origin of the arithmetic codes

for the total symbol sequence in AC, it can be said

was described at first. The basic study of arithmetic

that there are no theoretical differences in efficiency.

coding was mainly achieved by J.J. Rissanen 3 1 ) , G.G.

It will be possible to consider AC as the upper

Langdon 3 2 ) and R. Pasco 3 3) . J.J. Rissanen and G.G.

concept of HC, or both codes are constructing a kind

Langdon have done the work in IBM, and R. Pasco

of duality. There are some reviews considering thehas done the work in his thesis (Stanford University)

AC specifications of specific standards to be generaland he later joined IBM. They have contributedortheoretically and also practically how arithmetic

misunderstanding will disappear by letting readers

coding/decoding can be utilized.

know the concept of AC and HC more deeply.

unique

to

AC,

and

I

lastlyhopesuch

It can be said that the appearance of arithmetic

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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）(Honorary Member)
Fumitaka ONO
He has received BE, ME and Ph.D
from T he University of Tokyo
respectively. He has been working
with Mitsubishi Electric Corp., and
then
with
Tokyo
Polytechnic
University. He is currently Professor
Emeritus in TPU, and Visiting
Researcher of The Univ. of Tokyo. Hisinterested area covers image coding,and image processing. He is IEEE
Fellow, IEICE Fellow and IIEEJ
Fellow. He has been engaged in theinternational standa rdization work
since 1985, and had been ISO/IEC
JTC 1/SC29/WG1 JBIG Rapporteur.
He has received Award from Min istryof Education and Science, Award from
Ministry of Industry and Trade,
SCAT Chairman’s Grand Award, andContribution Award from IPSJ/ITSCJ
for distinguished achievements on
image coding and the contribution forstandardization activities.

Dynamic Coding Method for State Transition
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32) R. C. Pasco: “ Source Coding Algorithm for Fast
Data Compression ” , Ph. D. Thesis, Dept. ofElectrical Engineering, Stanford Univ. (May1976).

12

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

ECA-Resunet: Lung Segmentation of CT Images Based on an Efficient Channel
Attention Mechanism Deep Residual-UNet
Jincheng PENG† (Student Member) , Ruigang GE†, Guoyue CHEN‡ , Kazuki SARUTA‡, Yuki TERATA‡†Akita Prefectural University Graduate School, ‡Akita Prefectural University<Summary> Precise automatic segmentation of lung organs by computed tomography (CT) is a prerequisite for organ identification,
pathological localization and treatment of lung diseases. However, accurate lung segmentation remains a major challenge due to the shape,size and location of the lungs. In recent years, U-net network structures and variations have been applied to various medical image segmentation, but these networks still have limitations and shortcomings in solving the vanishing gradient problem and contextual semantic
feature extraction. In this paper, we propose a deep residual network called ECA-Resunet to segment lung organs with an efficient attention mechanism. The structure of ECA-Resunet is similar to Res-UNet. It uses deep residual units to form the entire encoder-decoder
network and adds an efficient channel attention mechanism to the encoder. Compared to other networks, the advantage of ECA-Resunet isthat it uses a deep residual network, which makes it difficult for the gradient to vanish. The encoder introduces an efficient channel attention mechanism to increase the weight of key regions in order to highlight key features and better learn the semantic features of the image
context. This way, we can design a network with fewer settings and achieve better semantic segmentation results without changing theoriginal image size. The results show that evaluation metrics such as Miou Score, Dice Effective Score, and Sensitivity of ECA-Resunet
outperform those of the comparison network.
Keywords: lung segmentation, Res-UNet, efficient channel attention network, computed tomography image

such as threshold-based segmentation, edge detection segmentation, and region-based segmentation, can replace
manual segmentation2)-4), and rough subdivide the target
organs and background within the image after digitizing theimage. However, these methods are inefficient and low
segmentation fine-grained, there is an urgent need for automatic and accurate lung organ segmentation methods.
In recent years, the application of convolutional neuralnetworks in image segmentation has made extraordinary
progress. The literature5)-7) has used various networks of
convolutional neurons to improve the precision of the semantic segmentation of images with good results. Meanwhile, the literature8)-10) has applied convolutional neuralnetworks to the segmentation aspects of medical-imagery.
In particular, the encoder-decoder network structure of
U-net and its variants has achieved state-of-the-art performance in various medical image segmentation tasks. The
U-net is a convoluted network image segmentation modelwith a U-shaped structure for biomedical applications. The
network pioneers a two-part encoder and decoder network
structure. However, the network effect on the location andboundary segmentation of organs is not satisfactory,
Achieved mean IoU of 0.921 on the 2015 ISBI Cell Challenge dataset. The literature 11) prooposed a deeper

1. Introduction
In 2019, the COVID-19 swept the world. A quick andaccurate diagnosis of patients is an important part of preventing COVID-19. Besides regular RT-PCR detection,
analyzing lung CT images is also very important for thetreatment and diagnosis of the COVID-191). Therefore, ahigh-precision lung organ segmentation method is needed.
At the same time, the doctor can diagnose a few lung diseases consisting of tuberculosis, pneumonia, and lung cancer.
with the assist of accurate segmentation images of lung organs to diagnose and plan surgical treatment for a few lungdiseases. However, due to the complexity of the anatomy oflung organs, the low contrast of adjacent organs, the low
incidence, shape and size of lung tissues and organs varyfrom individual to individual, manual segmentation is vulnerable to errors.
With the development of modern tomography modalities
such as computed tomography (CT) and magnetic resonanceimaging (MRI), computer image-guided diagnosis of lungdiseases has been used for regular diagnosis and monitoringof lung organ diseases. High-resolution images facilitate
visualization of the entire lung organ concerning surrounding anatomy. Traditional image segmentation techniques,

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Res-UNet convolutional network plus random conditional
fields for segmentation of skin cancer regions. It learns fromthe ResNet network and replaces the normal convolutional
blocks in U-net with the residual convolutional blocks in the
network encoder. It can solve the problem that vanishinggradients with increasing depth. However, semantic segmentation is not fully equivalent to pixel-wise prediction
and pixel grouping. Contextual information between these
network image pixel channels connection is not spotlighted.
As researchers have studied attention mechanisms to obtain contextual information about image pixel channels
connection by attention mechanism. Reference 12) proposesa squeeze and excitation network. By learning channel attention of each convolutional block, it can increase the information weight of key regions to highlight the features oftarget regions, bringing significant performance improvements to CNN network architecture of different depths.
Based on SE-Net, Reference 13) proposes a lightweight
network strategy for local cross-channel interaction without
dimensionality reduction and adaptive selection of
one-dimensional convolution kernel size, that significantly
reduces the complexity of the SE-Net model while maintaining efficiency. The literature 14) proposed a Dual-Attention and Skip Connection to combine the featuremaps of the encoder and decoder to complete pixel-level rib
fracture detection. The method has attained a sensitivity
89.58%, and the average FROC score is 81.28%. Reference15) proposes using U-Net with attention mechanism forabdominal organ segmentation, that improves the sensitivityof foreground pixels useful for specific tasks and obtains
satisfactory results. Scored 84.08% CSD on the pancreas
dataset. From the network experimental analysis of Attention U-Net network in the deep network convergence, andthe acquisition of contextual information is still slightly inadequate.
Based on the above networks and research on the attention mechanism of ECA networks, this paper proposes an
ECA-Resunet networks: a deep residual encoder-decoder
network architecture with an efficient channel attention
mechanism for CT images of lung organs segmentation16).
Since medical CT segmentation requires more precision
segmentation, not just simple pixel classification. To define
these target lung organ boundaries, we must emphasize therelated pixels to improve the segmentation accuracy of key
regions. We used an Efficient Channel Attention mechanism.
The Efficient Channel Attention module proposes a local
cross-channel interaction strategy without dimensionality
reduction and a lightweight attention mechanism method foradaptively selecting the size of 1D convolution kernels. theattention mechanism is used to learn high-level semantic
features, increase the information weight of key regions toemphasize the properties of the target region, suppress theinterference of irrelevant regions in the image, and model
the feature mapping dependencies between different channels. Specifically, ECA-Resunet is similar to Res-UNet
network architecture that includes an encoder, a decoder,and a bridge-like layer skip connection. The encoder of
ECA-Resunet contains an efficient channel attention module.
Meanwhile encoder and decoder adopted the deep residual
structure improves the utilization of image features by passing depth gradient information back to shallow layers while
avoiding gradient vanishing and network degradation. Enables the network to obtain high accuracy while increasing
the robustness and stability of the network.
We trained our proposed network structure method on the2016 Lung Node Analysis (LUNA) competition dataset.
80% of the competitive Lung Node Analysis (LUNA) dataset was used as the training dataset and 20% of the data
was used as the validation set. The result shows that themean IOU, Dice coefficient (DC) and sensitivity score of
ECA-Resunet in the lung organ validation set were 93.84%,
96.82% and 94.63% respectively. The boundary of lungorgan segmentation results is more smooth and accurate.
The network addresses the stability of automatic segmentation and accuracy of segmentation results, and future studies
will investigate whether it can be used for clinical CT segmentation.

2. Related Work
2.1 Network structure
The entire network structure is shown in Fig. 1. The
Res-UNet with improved encoder and decoder structure isadopted as the backbone architecture of the network.
Res-UNet consists of three parts: encoding, bridge and decoding17). The encoder and decoder form a symmetrical U
shape. The encoder performs a downsampling process on
the input lung computed tomography image. High-level
semantic information in the image is extracted layer by layer,
and a small feature tensor is obtained. Before each deep
residual module, we add an ECA (Effective Channel Attention) module to improve the effectiveness of feature map
ping. The middle part serves as a bridge, connecting theencoding and decoding paths that connect the encoder andthe decoder. The decoder on the right also consists of a deep
residual module that performs 3 layers upsampling on the

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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

Fig. 2 Residual module with identity mapping used in the proposed
ECA-Resunet

residual group, and each residual module contains two
stacked residual groups. Each residual unit is connected
through residuals between layers of different depths, Insteadof a direct continuous long connection. The remaining
blocks can distribute information on each layer to build
deeper neural networks. The advantages of this structure are:1) The skip connection with the residual units and between
the low-level and high-level networks will facilitate information propagation without degradation, avoiding the problem of gradient dispersion in the encoder caused by deepening the network. 2) At the same time, the interdependence ofthe channels is improved, and the speed of training convergence is accelerated. The residual unit can simplify thetraining of the network, reduce the overall parameters of theneural network, reduce the computational cost, and design a
network with fewer parameters but better semantic segmentation performance.

Fig. 1 The architecture of the proposed ECA-Resunet

feature tensor. Each layer of the decoder predicts a segmentation map for calculating the loss function, and recovers theextracted features into a pixel-level classification. In thisway, the nearest neighbor up a sampling of the feature-map
can be obtained from the lower level and concatenated withfeature mappings obtained from their corresponding encoding paths, finally obtaining a binary image with the same
size as the original image.

2.3 Efficient Channel Attention module block
We begin with a study of the SE-Net network. SE-Net
filters the features of different channel dimensions throughglobal average clustering, and then uses the model to filter
the features and select important features for improvement.
The SE module first uses global mean pooling for eachchannel separately, and then uses two nonlinear fully connected layers, finally using the sigmoid function to generate
the channel weights. The two fully connected layers aredesigned to capture nonlinear interactions between channels.
In order to control the complexity of the overall feature, thedimension of the feature vector is reduced. However, thedimensionality reduction process reduces the detail of thefeature description, so this operation has a side effect on thechannel attention prediction, thus reducing the accuracy ofthe network.

2.2 Residual module block
CNN networks improve the performance of neural networks with increasing network depth. However, increasing
the network can hinder training at the same time, and the
network may suffer from degradation problems. To overcome these problems, literature 18) proposes a deep residual
neural network to facilitate training and solve the problem of
network degradation. The residual module design in ECAResunet is shown in Fig. 2, which consists of a BN layer,
Relu activation layer and 3×3 convolutional layers form a15

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

2.4 Loss function

Effective Channel Attention Network (ECA-Net) is an
improved channel attention module based on the SE-Net.
Through a local cross-channel interaction strategy without
dimensionality reduction and a method of adaptively selecting the size of one-dimensional convolution kernels, theperformance of complex attention modules can be improved
based on increasing the minimum parameters. At the sametime, reduces the complexity of the dimensionality reduction
operation of the SE-Net model. Figure 3 shows how theattention mechanism of the ECA channel work. The original
input image features are shown on the left. All features
without dimensionality reduction are obtained by global
mean pooling (GAP); local cross-channel interaction information is collected by considering each channel and its K
neighbors. Here, instead of using a fully connected layer in
the SE module, one-dimensional convolution is used tocapture local cross-channel interaction information.
One-dimensional convolution includes hyper parameters K,
i.e. the size of the convolution kernel, It represents the coverage of local cross-channel interactions. Since we needlarge-scale convolution to analyze the contour images of thewhole lung organ, the value of K is 5 in this experiment.
Then use the sigmoid function to generate a weight score foreach channel, and then the original input feature is combinedwith the channel weight to obtain the feature with channel
attention. The channel attention can be computed as:
CA(x)=δ ( f conv (AvePool(x)))=δ (W(AvePool(x)))

The loss function is used to estimate the inconsistency
between the predicted segmentation map f(x) of the modeland the true background value y. In the actual segmentationof lung organs, due to the unbalanced proportions of foreground (lung) and background (non-lung), lung organs arelikely to occupy a small area in the image, resulting in adecrease in segmentation accuracy. Therefore, using only
the binary cross-entropy loss function (BCE loss) is not optimal for this type of task. This paper uses a combined loss
function of the Dice coefficient loss function plus binary
cross-entropy loss function to predict the similarity between
the lung organ segmentation region and the labeled true region, instead of the usual binary cross-entropy loss19). The
combined loss function is an overlap metric and is often
used to evaluate the segmentation performance. Even if thearea of the lung organ is smaller than the background area,
there is no effect on the Dice value. Many experiments haveconfirmed that the Dice loss function can solve the problem
of class imbalance. The Dice coefficient loss function andthe two Combining the loss function using the Dice coefficient loss function plus the binary cross-entropy loss function improves the training. The Dice coefficient loss function is calculated as follows:2∑ i p ( k , i ) g ( k , i )N

LDice = 1 −(1)k ∑ i p(2k ,i ) + ∑ i g (2k ,i )
N

N

(2)

Where N is the number of pixels, and p ( k , i ) ∈[0,1]and g ( k , i ) represent the prediction probability and Truth

where AvePool represents the global average pooling operation, W is the weight of the f conv convolution layer, and is the
sigmoid of the activation function. The network constructedwith this module is easier to extract the salient features of theimage based on channel dimensions, capture interaction information between channels, increase the weight of key regions,and obtain more abundant feature information of key regions.

label of class k , it takes 0 or 1. k is the class number. The
final loss f unction is defined as:

L=Lbce + LDice
loss

(3)

2.5 Evaluation method
In this paper, the most commonly used medical image
evaluation metrics, such as Dice coefficient (DC), Mean IoU
and sensitivity, are used to evaluate the training data. The
Dice Coefficient (DC) takes care of the class imbalance
problem and hence it has been used as the most common
evaluation metric. The Dice coefficient represents the similarity between the output (y) and the label (x) of the model'sprediction. The higher the lung organ similarity and label
confidence value, the higher the Dice coefficient, and thebetter the segmentation effect. The calculation formula of
the Dice coefficient is as follows:

Fig. 3 Diagram of efficient channel attention (ECA) module

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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

The problem of binary semantic segmentation of images isessentially the problem of binary classification of pixels. We
consider pixels in lung CT slices to be positive samples,background pixels to be negative samples, TP and TN indicate that the pixel is the predicted category matches the actual category, and FP indicates that the pixel is background
but is predicted to be a lung organ, indicating that the pixel
is incorrectly classified as a false positive. and FN indicatesthat the pixel is a lung organ but is predicted to be background. This indicates that the pixel is incorrectly classified
as a false negative.
The mean IOU average intersection union ratio is one of
the most commonly used evaluation metrics of semantic
segmentation20). The ratio of intersection and concatenationof the predicted and background true values for each class of
pixel segmentation in the lung organ CT slice is calculated
and then averaged. Using the above definition, the calculation formula is as follows:
MeanIoU =TP
1 k∑k i =0 FP + TP + FN

DiceCofficient ( DC ) =TP
TP + FN(6)

X +Y

in order to facilitate validation, 534 2D samples without
lung nodules were randomly sliced from the original dataset,and these samples has been threshold processed and threshold normalized to highlight the features of the lung organs to
obtain more accurate segmentation of the true labels. We use
this challenge dataset to evaluate the structure of our proposed ECA-Resunet model. The dataset contains 534 2D
samples and corresponding true labels, and the data size is
512×512. Since the image is too large and wastes storage
space, the data is cropped to 256×256. The processed data
were divided into 427 training datasets and 107 validation
datasets.

3.2 Experimental process
All training and testing experiments are performed onwork stations. The CPU used is Intel (R) Xeon (R) CPU
e5-2630V4@2.20 GHz. The GPU uses two NVIDIAforcedx1080ti with 12GB GPU memory, with a total
memory of 24G. All network architectures are implemented
with pytorch 1.7.1+cu101 framework. We start the training
with a batch size of 5. The proposed architecture is optimized by the Adam optimizer with the learning rate initially
set to 0.001. The loss function is used as the combined loss
function mentioned earlier.

(4)

where k is the division category.
Sensitivity is sensibility, which represents the ratio of
pixels correctly predicted as lung organ category to background real value pixels. The range of sensitivity coefficientis [0,1]. A value closer to 1 indicates that a higher percentage of lung organ pixels are correctly classified, i.e. thehigher the accuracy of the model. The sensitivity is expressed as follows:
S =2× X ∩Y

3.3 Comparisons with state-of-the-arts
Table 1 lists the experimental results obtained from several state-of-the-art segmentation networks. The table below
shows that ECA-Resunet outperforms U-Net and Attention-UNet on evaluation metrics such as average IOU, dice
efficiency (DC), and sensitivity, and the parameters are notsignificantly improved compared to U-Net. The results showthat the values of the ECA-Resunet network and CE-Net are
relatively close.
CE-Net is the current mainstream segmentation network,
using dense atrous convolution module (DAC) and a residual multi-pooling module similar to a pyramid structure22).

(5)

3. Experiments
3.1 Database
We use the 2016 Lung Nodule Analysis (LUNA) dataset
to evaluate our model. This is the segmentation of lung organ tissue structures in 2D CT images as part of Lung Nodule Analysis (LUNA) challenge21). The LUNA16 dataset
contains labeled low-dose lung CT image data from 888
patients. For each patient, the CT scan data consists of avariable number of images (typically around 100-400, each
image is an axial slice). Primary validation of nodule detection and reducing false positives. Since full segmentation oflung organs is a further basis for lung nodule detection, The
original data image is a 3D image, which is composed of aseries of 2D images with a slice interval of 2 mm. As thispaper is intended to perform validation of 2D lung contours,

Table 1 The evaluation results of all the models on Lung Nodule Analysis(LUNA) dataset
MIou

DC

Sensitivity

Parameter

U-Net

0.8935

0.94320.9274

5.88M

Attention-UNet

0.93370.96570.937835M

CE-Net0.93500.96640.935438M

ECA-Resunet

0.9384

0.96820.946312.73M

NET

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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

This design will increase the number of parameters, increase
the amount of computation, and increase the training andtesting time.
Figure 4 shows the comparison between ECA-Resunet
and the training set Dice coefficient (DC) of the current
more popular medical image segmentation algorithm. Figure 5 shows the evaluation results of the designed ECA
summary on foreground (lung) and background(non-lung)segmentation of CT images of clinical lung organs, andcompared with the current popular medical image segmentation algorithm U-Net, CE-Net, Attention-UNet. From left
to right, each column contains the original image, true labels,
and proposed method test results. As can be seen from theabove figure, ECA-Resunet improves segmentation performance by focusing on related key regions. Compared withother methods, ECA-Resunet reduces the area of prediction
error. The edge tissue segmentation of lung organs issmoother. It proves that the ECA-Resunet result best.

effective channel attention mechanism and the deep residual
structure adopted by the proposed network, U-Net, U-Net+SE module, Unet+ECA module, Res-UNet module,
Res-UNet+ECA module are compared respectively. The
details are shown in Table 2. As can be seen from the table,
the single SE-Unet and ECA-Unet can be seen from theTable 2 to be much better than Unet on MIou and DC. Dueto the lightweight attention mechanism, In the previous narrative, the ECA attention mechanism uses a local
cross-channel interaction strategy without dimensionality
reduction and adaptive selection of the one-dimensional
convolutional kernel size, because there is a loss of semantic
details in the process of dimensionality reduction, so the useof ECA-Unet network is better than SE-Unet network.
However, compared with Res-UNet, ECA-Resunet does notincrease the number of parameters, and the MIou of the partition increases by 1.5%. Compared with the U-net network,
the MIou increases by 4.5%, thereby obtaining higher results.
Therefore, despite the increase in U-net network computation costs, these network computational costs are necessary.
This shows that the attention mechanism significantly improves the network performance after the introduction of the
ECA module, and also has a certain effect on the overallmodel segmentation.

3.4 Ablation study
In this section, we use ablation experiments to evaluate
the various components of the proposed ECA-Resunet network model structure, and verified the effectiveness of theTable 2 The evaluation results of ablation study on Lung Nodule Analysis (LUNA) dataset
MIou

DC

Sensitivity

Parameter

0.8935

0.94320.9274

5.88M

SE-Unet0.92700.96210.9313

7.5MB

ECA-Unet

0.9306

0.96400.9357

7.5MB

Res-UNet

0.92350.96020.927912.73MB

ECA-Resunet

0.9384

0.96820.946312.73MBNET
U-Net

Fig. 4 Different model training dice on the Lung Nodule Analysis (LUNA) dataset

Fig. 5

Fig. 6 (a) Activation information of the ECA-Resunet network modelfeature map (b) Activation information of the U-net network modelfeature map

Different segmentation model segmentation effect18

IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

To verify the capture of contextual information between
image channels by the ECA attention mechanism module.
We extracted separately the feature map from the third layer
downsampling on a channel of two lung organ images in thedataset. From the comparison of the ECA-Resunet networkmodel in Fig. 6(a) on the left and the U-net model in Fig.
6(b) on the right, it can be seen that the ECA-Resunet network model has more colored activation information in thefeature maps. In this way, it is shown that the ECA-Resunet
network model is better than the U-net model in capturing
semantic information, and there is fine-grained feature mapwith less misfortune in semantic details.
From the loss function curves of Fig. 7(a) and Fig. 7(b),the fluctuation in the network validation loss assembly after
adding the residual structure is low, but the loss converts
quicker, more stable and smoother than the structure without
residual modules. It is proved that the deep residual model
not only can performance be improved but also can effectively overcome the problem of gradient vanishing/exploding in the initial stage of training.

coder-decoder network architecture, In the network encoder,
the normal convolutional blocks in U-Net are replaced byresidual convolutional blocks, which has the advantage ofsolving the problem of disappearing deep gradients as thedepth of the network increases. But only in this way contextual information between image channels is not improved.
So we are leveraging the strategy to improve the compression SE-Net network through effective channel attention.
Use a strategy for local cross-channel interaction between
channels with no reduction in dimensionality. A lightweightattention mechanism method with adaptive selection of
one-dimensional convolution kernel size. The attention
mechanism can emphasize the characteristics of the target
area by increasing the information weight of the key regions,suppress the interference of irrelevant areas in the image,and because the attention mechanism is a lightweight network structure, it will not increase too many network parameters and will not change the resolution of the original
image. Dramatically reduce the complexity of the original
extrusion-excitation design while maintaining performance.
This enhanced strategy acquires channel focus to improve
context information between image channels. With both of
the above enhancements to the U-shaped encoder-decoder
network.
We design a deep residual encoder-decoder called
ECA-Resunet Effective Channel Attention Mechanism
server network architecture. It maintains the overall
U-shaped network of encoders-decoders. By adding a residual module and an effective attention module, solve theproblem of capturing contextual information of image
channels and gradient vanishing explosion by adding a residual module and an effective attention module. A Dicecoefficient (DC) score of 96.82% was obtained in the 2016
Lung Nodal Analysis (LUNA) Challenge lung organ segmentation data. In addition, the network surpasses othersimilar methods for evaluation parameters, and with significant contextual semantic feature extraction, more accurate
and smoother lung organ contour segmentation, and lessfine-grained detail loss. In addition, because the network
parameters do not increase significantly, unnecessary network overhead is reduced, and perfect lung organ segmentation is achieved while ensuring accuracy, which can better
assist doctors in lung organ diagnosis and surgery planning.
Future improvements can be made to this structure by incorporating the relationship between long distance positionsof pixels. Other enhancements to the network framework forsemantic information capture.

4. Conclusion
In this paper, we aim to enable the network to better
learn more efficient features and obtain more accurate lungorgan segmentation results. We start with the U-shaped en-(a) Validation loss for U-net and Res-UNet(b) Validation loss for ECA-Unet and ECA-Resunet

Fig. 7 (a) Validation loss for U-net and Res-UNet on Lung Nodule Analysis (LUNA) dataset (b) Validation loss for ECA-Unet and
ECA-Resunet on Lung Nodule Analysis (LUNA) dataset

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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

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Jincheng PENG

tion Using Convolutional Neural Networks”, Bildverarbeitung fur die(Student Member)He received the ME degree from Chengdu
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Ruigang GE

text-Aware Operating Theaters, Computer Assisted Robotic Endoscopy,

He received his ME degree from Ashikaga
University, Japan, in 2019 and is currently
pursuing a PhD. in Systems Science and Technology at Akita Prefectural University Graduate
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IIEEJ Transactions on Image Electronics and Visual Computing Vol.11 No.1 （2023）

Guoyue CHEN

He received his ME and PhD degrees fromTohoku University, Japan in 1993 and 1996,
respectively. Then, he was an assistant professor
in Tohoku University, Japan. He is currently aprofessor in Akita Prefecture University, Japan.
His research interests include digital signal
processing and its applications to active noise
control, image processing and medical image
processing.

Kazuki SARUTA
He received his BE and ME degrees from
Akita University, Japan, in 1991 and 1993, and
his PhD degree from Tohoku University, Japan,
in 1996. Then, he was a lecturer in Yamagata
University. He is currently a professor in Akita
Prefecture University, Japan. He has been engaged in research on pattern recognition andimage processing.

Yuki TERATAHe received his BE degree from Akita University, Japan, in 1998, and the ME and PhD
degrees from the same university in 2000 and2008. He is an associate professor in Akita
Prefecture University, Japan. He is interested in
VR/AR for cognitive psychology of elderly
people.

21

Call for Papers
The 8th IIEEJ International Conference on
Image Electronics and Visual Computing 2024
(IEVC2024)
National Cheng Kung University, Tainan City, Taiwan / Mar. 11‐14, 2024
https://www.iieej.org/en/ievc2024/

Purpose:The International Conference on Image Electronics and Visual Computing 2024 (IEVC2024) is scheduled in Tainan
City, Taiwan, on March 11‐14, 2024, as the 8th international academic event of Image Electronics Engineers ofJapan (IIEEJ). Past IEVC conferences were held in Cairns, Australia (2007); Nice, France (2010); Kuching, Malaysia(2012); Koh Samui, Thailand (2014); Danang, Vietnam (2017); Bali, Indonesia (2019); and online (2021). The
conference aims to bring together researchers, engineers, developers, and students from various fields inacademia and industry to discuss the latest research, standards, developments, implementations, and application
systems in all image electronics and visual computing areas.

Topics:
The conference will cover a broad set of research topics including, but not limited to, the following: 3D image processing
 Modeling Bioinformatics and authentication
 Non‐photorealistic rendering Computer vision
 Rendering Data hiding
 Visual computing
 Image analysis and recognition
 Visualization
 Architectural industry mondiale Image and video coding Image and video retrieval Artificial intelligence and deep learning Image assessment
 Big data and cloud computing
 Image restoration
 Content delivery network
 Mobile image communication
 Digital museum, digital archiving
 Motion analysis
 Generative AI
 Object detection
 Hardware and software implementation
 Printing and display technologies
 Interaction
 Segmentation and classification
 International standards
 Smart display
 Security and privacy
 Versatile media appliance Social secured cybertechnology
 Animation
 Unmanned Aerial Vehicle
 Content production
 Visual and hearing impaired support
 Extended Reality
 Visual communication
 Metaverse

22Paper submission:
The official language is English and authors should submit their papers as PDF through the online submission
system, which will be available around Jun. 2023 at the following IEVC2024 official website:
https://www.iieej.org/en/ievc2024/ .
The paper submission guide and IEVC formats (TeX format/MS Word format) will be also provided at this site. The
organizing committee particularly encourages graduate students to present their works in the special sessions thatare now planned by the committee of the conference.

General Papers:
The general papers category is divided into two types: Journal track and Conference track.

Journal Track:
Journal track aims to publish the papers on the journal in addition to the publishing in the conference, with aquick review process. This type of papers will appear in a special issue on “Journal Track Papers in
IEVC2024“ in the IIEEJ Transactions on Image Electronics and Visual Computing, Vol. 12, No. 2 (June, 2024), if
accepted through journal review process. The authors have to prepare two types of papers different in theamount, the paper for the conference and the paper for the journal. The latter one is the extended version of
the former one. Note that paper for the journal should follow the “guidance for paper submission”
available from the web‐site of IIEEJ, to be finally published in the IIEEJ Transactions.
Important Dates‐ Pre‐Entry Submission (title, authors, 100 words abstract):
Sep. 15, Friday, 2023
‐ Paper Submission (2‐4 pages for the conference)
Sep. 29, Friday, 2023
Oct. 27, Friday, 2023
‐ Paper Submission (8 pages for the journal):‐ Notification of Conference Acceptance:
Nov. 17, Friday, 2023
‐ Camera‐Ready Paper (2‐4 pages for the conference):
Dec. 15, Friday, 2023

Conference Track:
Conference track aims to present the papers about recent results and preliminary work at IEVC2024. The
authors are required to submit a paper of which length is 2‐4 pages. Accepted papers will be published in
online proceedings of IEVC2024 (indexed by J‐stage) and in the USB proceedings. Rejected papers in theconference track can be resubmitted as late breaking papers according to the authors’ requests in advance.
Important Dates‐ Pre‐Entry Submission (title, authors, 100 words abstract):
Sep. 15, Friday, 2023
‐ Paper Submission (2‐4 pages):
Sep. 29, Friday, 2023
‐ Notification of Acceptance:
Nov. 17, Friday, 2023
Dec. 15, Friday, 2023
‐ Camera‐Ready Paper (2‐4 pages):

Late Breaking Papers:
All suitably submitted papers for this category will be accepted for the conference. The authors are required
to submit an abstract of which length is 1‐2 pages, and select one from the following two types: 1) Technicalpapers or 2) Art/Demo papers. All the registered papers as late breaking papers will be published only in theUSB proceedings of IEVC2024.
Important Dates‐ Pre‐Entry Submission (title, authors):
Nov. 17, Friday, 2023
‐ Abstract Submission (1‐2 pages):
Nov. 24, Friday, 2023
‐ Notification of Acceptance:
Dec. 8, Friday, 2023
‐ Camera‐Ready Paper (1‐2 pages):
Dec. 15, Friday, 2023

Further information:
After the conference, the Trans. on IEVC of IIEEJ is planning a forthcoming special issue on “Extended Papers
Presented in IEVC2024”, which will be published in December 2024. More detailed information will be notified on
the IEVC2024 web‐site and the Journal of IIEEJ.

23

Call for Papers
Special Issue on
Design and Implementation Technologies to Support
Immersive Video Communication and Distribution
IIEEJ Editorial Committee
In recent years, there has been rapid progress in advanced image processing and high-definition
video services, driven by advancements in semiconductor technology and information
communication technology. Specifically, in addition to improving spatial resolution, frame rates,
dynamic range, and color gamut, and other aspects of image quality, there has been an increase in
the diversity of immersive media, such as 360-degree video, VR/AR/MR, and so on. Research onvideo compression, communication, and distribution technologies related to these developments is
also thriving. Furthermore, device technologies are essential for the communication anddistribution of these videos. In order to make the most of the benefits of further progress inhigher integration and lower power consumption in the device technology, there is a strong

demand for further progress in hardware/software design technology and implementation
technology, such as how to design and implement the required functions.
In this special issue, we invite various categories (Ordinary paper, Short paper, Systemdevelopment paper, Practice Oriented Paper) of papers on hardware/software design technology
and implementation technology that support communication and distribution of immersive,high-definition video. We look forward to receiving your contributions.
1. Topics covered include but not limited toVideo Communication, Video Distribution, Hardware/Software Design Technology, LSI and
System, Image Compression, High-definition Video, Immersive Media, 360-degree Video,
VR/AR/MR, 3D Related Design Technologies
2. Treatment of papers
Submission paper style format and double-blind peer review process are the same as theregular paper. If the number of accepted papers is less than the minimum number for the
special issue, the acceptance paper will be published as the regular contributed paper. We ask
for your understanding and cooperation.
3. Publication of Special Issue:
IIEEJ Transactions on Image Electronics and Visual Computing Vol.12, No.1 (June 2024)
4. Submission Deadline:
Tuesday, October 31, 2023
5. Contact details for Inquiries:
IIEEJ Office E-mail: hensyu@iieej.org
6. Online Submission URL:

http://www.editorialmanager.com/iieej/

24

Revised: January 6, 2017
Revised: July 6, 2018

Guidance for Paper Submission
1.

Submission of Papers(1) Preparation before submission
・ The authors should download “Guidance for Paper Submission” and “Style Format” from the“Academic Journals”, “English Journals” section of the Society website and prepare the paperfor submission.
・ Two versions of “Style Format” are available, TeX and MS Word. To reduce publishing costs
and effort, use of TeX version is recommended.
・ There are four categories of manuscripts as follows: Ordinary paper: It should be a scholarly thesis on a unique study, development orinvestigation concerning image electronics engineering. This is an ordinary paper topropose new ideas and will be evaluated for novelty, utility, reliability andcomprehensibility. As a general rule, the authors are requested to summarize a paper
within eight pages.
 Short paper: It is not yet a completed full paper, but instead a quick report of the partial
result obtained at the preliminary stage as well as the knowledge obtained from the said
result. As a general rule, the authors are requested to summarize a paper within four
pages.
 System development paper: It is a paper that is a combination of existing technology or it
has its own novelty in addition to the novelty and utility of an ordinary paper, and thedevelopment results are superior to conventional methods or can be applied to othersystems and demonstrates new knowledge. As a general rule, the authors are requested
to summarize a paper within eight pages.
 Data Paper: A summary of data obtained in the process of a survey, product development,
test, application, and so on, which are the beneficial information for readers even though
its novelty is not high. As a general rule, the authors are requested to summarize a paper
within eight pages.
・ To submit the manuscript for ordinsry paper, short paper, system development paper, or data
paper, at least one of the authors must be a member or a student member of the society.
・ We prohibit the duplicate submission of a paper. If a full paper, short paper, systemdevelopment paper, or data paper with the same content has been published or submitted toother open publishing forums by the same author, or at least one of the co-authors, it shallnot be accepted as a rule. Open publishing forum implies internal or external books,
magazines, bulletins and newsletters from government offices, schools, company
organizations, etc. This regulation does not apply to a preliminary draft to be used at anannual meeting, seminar, symposium, conference, and lecture meeting of our society or othersocieties (including overseas societies). A paper that was once approved as a short paper andbeing submitted again as the full paper after completion is not regarded as a duplicate
submission.
(2) Submission stage of a paper・ Delete all author information at the time of submission. However, deletion of reference
information is the author’s discretion.
・ At first, please register your name on the paper submission page of the following URL, andthen log in again and fill in the necessary information. Use the “Style Format” to upload yourmanuscript. An applicant should use PDF format (converted from dvi of TeX or MS Word25

format) for the manuscript. As a rule, charts (figures and tables) shall be inserted into themanuscript to use the “Style Format”. (a different type of data file, such as audio and video,
can be uploaded at the same time for reference.)
http://www.editorialmanager.com/iieej/
・ If you have any questions regarding the submission, please consult the editor at our office.
Contact:
Person in charge of editing
The Institute of Image Electronics Engineers of Japan
3-35-4-101, Arakawa, Arakawa-Ku, Tokyo 116-0002, Japan
E-mail: hensyu@iieej.org
Tel: +81-3-5615-2893, Fax: +81-3-5615-2894

2.

Review of Papers and Procedures(1) Review of a paper・ A manuscript is reviewed by professional reviewers of the relevant field. The reviewer willdeem the paper “acceptance”, “conditionally acceptance” or “returned”. The applicant is
notified of the result of the review by E-mail.
・ Evaluation method
Ordinary papers are usually evaluated on the following criteria:
 Novelty: The contents of the paper are novel.
 Utility: The contents are useful for academic and industrial development.
 Reliability: The contents are considered trustworthy by the reviewer.
 Comprehensibility: The contents of the paper are clearly described and understood by
the reviewer without misunderstanding.
Apart from the novelty and utility of an ordinary paper, a short paper can be evaluated byhaving a quickness on the research content and evaluated to have new knowledge withresults even if that is partial or for specific use.
System development papers are evaluated based on the following criteria, apart from thenovelty and utility of an ordinary paper.
 Novelty of system development: Even when integrated with existing technologies, thenovelty of the combination, novelty of the system, novelty of knowledge obtained fromthe developed system, etc. are recognized as the novelty of the system.
 Utility of system development: It is comprehensively or partially superior compared tosimilar systems. Demonstrates a pioneering new application concept as a system. The
combination has appropriate optimality for practical use. Demonstrates performance
limitations and examples of performance of the system when put to practical use.
Apart from the novelty and utility of an ordinary paper, a data paper is considered novel if
new deliverables of test, application and manufacturing, the introduction of new technology
and proposals in the worksite have any priority, even though they are not necessarily
original. Also, if the new deliverables are superior compared to the existing technology andare useful for academic and industrial development, they should be evaluated.
(2) Procedure after a review
・ In case of acceptance, the author prepares a final manuscript (as mentioned in 3.).
・ In the case of acceptance with comments by the reviewer, the author may revise the paper inconsideration of the reviewer’s opinion and proceed to prepare the final manuscript (as26mentioned in 3.).
・ In case of conditional acceptance, the author shall modify a paper based on the reviewer’s
requirements by a specified date (within 60 days), and submit the modified paper for
approval. The corrected parts must be colored or underlined. A reply letter must be attachedthat carefully explains the corrections, assertions and future issues, etc., for all of theacceptance conditions.
・ In case a paper is returned, the author cannot proceed to the next step. Please look at thereasons the reviewer lists for the return. We expect an applicant to try again after reviewing
the content of the paper.
(3) Review request for a revised manuscript
・ If you want to submit your paper after conditional acceptance, please submit the reply letterto the comments of the reviewers, and the revised manuscript with revision history to thesubmission site. Please note the designated date for submission. Revised manuscripts
delayed more than the designated date be treated as new applications.
・ In principle, a revised manuscript willl be reviewed by the same reviewer. It is judged either
accceptance or returned.
・ After the judgment, please follow the same procedure as (2).

3.

Submission of final manuscript for publication(1) Submission of a final manuscript
・ An author, who has received the notice of “Acceptance”, will receive an email regarding thecreation of the final manuscript. The author shall prepare a complete set of the final
manuscript (electronic data) following the instructions given and send it to the office by thedesignated date.
・ The final manuscript shall contain a source file (TeX edition or MS Word version) and a PDF
file, eps files for all drawings (including bmp, jpg, png), an eps file for author’s photograph(eps or jpg file of more than 300 dpi with length and breadth ratio 3:2, upper part of the body)for authors’ introduction. Please submit these in a compressed format, such as a zip file.
・ In the final manuscript, write the name of the authors, name of an organizations,introduction of authors, and if necessary, an appreciation acknowledgment. (cancel macros in
the Style file)・ An author whose paper is accepted shall pay a page charge before publishing. It is theauthor’s decision to purchase offprints. (ref. page charge and offprint price information)(2) Galley print proof
・ The author is requested to check the galley (hard copy) a couple of weeks before the paper ispublished in the journal. Please check the galley by the designated date (within one week).
After making any corrections, scan the data and prepare a PDF file, and send it to our officeby email. At that time, fill in the Offprint Purchase Slip and Copyright Form and return thescanned data to our office in PDF file form.
・ In principle, the copyrights of all articles published in our journal, including electronic form,
belong to our society.
・ You can download the Offprint Purchase Slip and the Copyright Form from the journal on our
homepage. (ref. Attachment 2: Offprint Purchase Slip, Attachment 3: Copyright Form)(3) Publication
・ After final proofreading, a paper is published in the Academic journal or English transaction(both in electronic format) and will also be posted on our homepage.

27

IIEEJ Trans. on Image Electronics and Visual Computing©2023 IIEEJ

Vol. 11, No. 1 2023

Editor in Chief: Osamu Uchida
The Institute of Image Electronics Engineers of Japan
3-35-4-101，Arakawa，Arakawa-ku，Tokyo 116-0002，Japan