![ ISSN: 1693-6930
TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082
1072
any input parameter (point a). So, in this study, parameter input, algorithm for VQA and visual
perception approaches will be discussed.
First discussion is about input parameter. Tracking antenna systems require information
about the video clarity level on the user's TV screen. This information is obtained from the
AV-Out (Audio Video Output) connection on the rear panel of the TV. This connection produces
CVBS signals continuously. The CVBS (Composite Video Baseband Signal) on AV-Out can
describe the video clarity level [1]. Tracking antenna system based on CVBS signal processing
has been proposed in previous studies [2]. However, both of previous study has not been
developed in terms of VQA and visual perception.
Second discussion is about algorithm for VQA, Assessment of the video clarity level is
one form of the decision-making process. To achieve optimal results, the decision-making
process should be simple, easy to control, and easy to interact with users. One of the most
common algorithms used in the decision-making process is FIS (Fuzzy Inference System). In
the previous study, FIS has been used for decision-making in an effort to improve the quality of
information [3]. The implementation of FIS (Fuzzy Inference System) on VQA has been widely
used, fuzzy rule evaluation has been proposed to measure image quality due to distortion based
on HVS (Human Visual System) modeling [4]. FIS also used to predict the quality of video from
several fidelity distortions, blurring distortion and contrast distortion [5]. FIS has been proposed
to improve QoE (Quality of Experience) by building quality predictions on mobile device-based
video [6-7]. In addition, FIS has also been used to predict the quality of wireless video
streaming [8-9]. While in current study, FIS used as VQA for the tracking antenna systems that
work in real-time and make faster and precise decision-making related to assessing video clarity
level.
Third discussion is about visual perception approaches. On video quality measurement,
it is necessary to understand how users generally assess a video and its quality. So, visual
perception is complex [10]. The popular method used to calculate video quality is PSNR (Peak
Signal-to-Noise Ratio) or by using MSE (Mean Square Error) [11-14]. Another method that also
often used is MOS (Mean Opinion Score) approaching. Previous study related to video quality
measurement in analog TV technology is using MOS approach to video quality with random
noise. The parameters used are the comparison of chrominance and luminance signal versus
noise [15]. While in this study, the MOS approach uses another parameter that is the result of
CVBS signal processing. MOS variant that used is MOS-VQS (Mean Opinion Score-Video
Quality Subjective) because video clarity level assessment is subjective and it’s limited to noise
and ghosting influence on analog video TV quality.
Considering to previous discussion, the real-time VQA in this study was built from FIS
algorithm for decision making process in assessing video clarity, MOS-VQS for visually
perceptual approach and CVBS signal processing result for VQA's input parameters. Besides
that, the membership function on FIS can be changed according to user’s perception. This is
conducted so that the real time VQA built can adaptive to many diverse users visual perception.
So, the FIS algorithm that used in this study is adaptive FIS with membership function tuning.
2. Methodology
In an effort to realize reliable real-time VQA, the research methodology steps begin with
the system design that includes hardware design and algorithm design. Then the second step is
FIS algorithm simulation that includes membership function tuning. The third stage is real-time
VQA performance test that includes VQA performance trial on default mode and VQA
performance trial on user mode. User mode and default mode will be discussed on
sub-chapter 3.4. The final step is overall results analysis. The research methodology is as
shown in Figure 1.
3. System Design
Figure 2 shows the system design of real-time VQA that proposed. FIS algorithm that
serve as assessment machine are designed with visual perception approaches using
MOS-VQS. The input of real-time VQA is the result of CVBS signal processing. The real-time
VQA output is accurate information with regard to the video clarity level on the user's TV screen.](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-2-320.jpg)
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Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni)
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Due to the focus of this study is limited to real-time VQA, video clarity level information that
produce by real-time VQA will be displayed on the LCD display and then recorded as trial result.
Each user certainly has a different perception in assessing the video clarity level.
Therefore, this study proposed an access control for users. This access control is connected to
the fuzzification process on the FIS. It is expected that the real-time VQA performance can
correspond with the user's desires.
Figure 1. Research Methodology
Figure 2. System Design of Real-time VQA
3.1. Visual perception approaches using MOS-VQS
MOS-VQS (Mean Opinion Score-Video Quality Subjective) is a subjectively visual
perception approaches and this is a recommendation from ITU-T (International
Telecommunication Union-Telecommunication standard section). Most of the MOS-VQS use 5
points quality scaling, among others are excelent, good, fair, poor, and bad [16]. But in this
study, 5 scale were initialized as (good clarity, fair clarity, bad clarity, worst clarity, noisy). Then,
each scale is visualized to the TV reception characteristics in the urban area as in the Table 1.
Picture in the Table 1 obtained from observation on latitude and longitude 7 ° 19'12.0_S 112 °
43'55.9_E. This coordinate is an area with many tall buildings that can disturb TV signal
reception.](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-3-320.jpg)
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Table 1. Subjectively Video Clarity Level Classification with MOS-VQS [2]
MOS Scale Standard
Scale of Video Clarity Level
in This Study
TV reception
characteristics
Description
1. Excellent Good Clarity
Perfect colors and
minimum ghosting.
2. Good Fairly Clarity
Perfect colors but
fairly ghosting.
3. Fair Bad Clarity
Blurred colors and
ghosting looks dominant.
4. Bad Worst Clarity
Ghosting looks
extremely dominant.
5. Worst Noisy
Noise looks extremely
dominant,
image and colors are
disappear.
3.2. CVBS signal processing
Tracking antenna system requires the input parameter as a reference. The AV-Out
connection on the rear panel of the TV is the most potential item to used. The signal format on
AV-Out is a CVBS analog signal. As in previous studies [1]-[2], counting the synchronization
pulse on a CVBS signal is one way of obtaining information related to the video clarity level. The
reason for using this way is because the synchronization pulse of CVBS signal on AV-Out
depends on the video clarity level on the TV screen. If video clarity level on TV screen is
becomes better, then shape and number of synchronization pulse on AV-Out becomes close to
the CVBS standard. Figure 3 shows the design of CVBS signal processing.
Figure 3. Design of CVBS Signal Processing [2]
To calculate the synchronization pulse, a sync separator IC LM 1881 N used to
separate the synchronization pulse from other signals (luminance signals, chrominance signal
and burst signal). Analog comparator and inverting Schmitt trigger is used to ensure that voltage
level of synchronization pulse is sufficient and can be processed by microcontroller. Then,
synchronization pulse calculations are performed by timer counter on the AT-Mega](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-4-320.jpg)
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32 microcontroller. In this case, the microcontroller calculates the rising edge of the transition
between sync level and blanking level on the CVBS signal. In this study, the results of
calculations called with rising transition and it will be processed using FIS algorithm. In this
case, FIS used as real-time VQA. Real-time VQA output will produce values that describe the
video clarity level accurately. Then the information will be displayed on the LCD display. In this
study, FIS algorithm implemented into listing program on AT-Mega microcontroller 32 A.
PAL BG (Phase Alternating by Line, B and G variant) is color encoding standard that
used for analog television in Indonesia. In PAL BG, each frame contains 625 lines which divided
into 2 fields (odd and even) [17]. The number of rising transition on the overall synchronization
pulse are 640 per frame. This value is derived from the summing transition of each line. If every
second there are 25 frames, then the rising transition number are 16,000 per second. The rising
transition on CVBS signal sourced from AV-Out has special characteristics. The number of
rising transition will be closer to 16,000 if the video clarity is better, and vice versa. In the
previous study, rising transition used as the main parameter [1]. As a continuity of previous
study, then in this study conducted development. In this study used two parameters which in
this case we call as rising transition difference (Rd) and rising transition changes (Rc). Details of
both parameters are as follows :
a. Rising transition difference (Rd) is the difference between rising transition counting results
with the standard value of 16,000. The smaller the value of this parameter, the video clarity
level condition can be defined as becomes better and vice versa. Rising transition
difference (Rd) is a single integer variable which is updated in real-time every second.
Rising transition difference (Rd) can be expressed in equation (1).
Rd = Abs [16,000 – rising transition] (1)
b. Rising transition changes (Rc) is the difference between the current rising transition
difference (Rd(Now)) with the previous rising transition difference (Rd(Previous)). The smaller the
value of this parameter, the video clarity level condition can be defined as becomes better
and vice versa. Rising transition changes (Rc) is an integer and is not a negative value.
Rising transition changes (Rc) can be expressed in equation (2).
Rc = Abs [ Rd(now) – Rd(Previous) ] (2)
3.3. FIS for assessment machine
3.3.1 Zero order fuzzy sugeno
In this study, zero order fuzzy sugeno used as real-time VQA. The reason for using zero
order is a lightweight computational load with the assumption that the microcontroller as the
main processor has limited computing capability. Input of FIS obtained from two parameters,
namely, rising transition difference (Rd) and rising transition changes (Rc). While the output
parameters of the FIS are information related to the video clarity level. In the fuzzification
process, the design of membership function on the input parameters in this study is arranged as
in Figure 4. While the designing membership function on the output parameters shown in
Figure 5.
Figure 4. Membership Function for Rd and Rc Parameter](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-5-320.jpg)
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Figure 5. Membership Function for Output Parameter
After the fuzzification process, then the next process is to determine the fuzzy rule. As
many as 25 rules used in the fuzzy rule which shown as detail in Table 2. In the defuzzification
process, the Weighted Average method used because it has relatively lightweight computing
load [18]. Furthermore, the defuzzification process is expressed in the equation (3).
z =
∑ 𝜇𝐶(𝑍𝑛) . 𝑍𝑛
∑ 𝜇𝐶(𝑍𝑛)
(3)
Where:
a. z is the output value sought. This value is real (float) number that represents the video
clarity level. Furthermore, this value will be replaced with information by MOS-VQS scale,
among others are: GOOD (for 4.6≥z≤5), FAIRLY (for 3.6≥z≤4.5), BAD (for 2.6≥z≤3.5),
WORST (for 1.6≥z≤2.5) and NOISY (for 1≥z≤1.5).
b. C is minimum value of membership grades on Rd and Rc parameters. For in this case,
C >0≤1.
c. Zn[Z1....Z25] value of fuzzy rule number to-n (Zn) which is not zero. For in this case, 1≥Zn≤ 5.
Table 2. Fuzzy Rule
Fuzzy Rule
Number
Rising Transition
Difference
Rising Transition
Changes
Video Clarity Level Information Displayed
on LCD Display
1 (Z1) very low very low GOOD (5.0)
2 (Z2) very low low GOOD (5.0)
3 (Z3) very low medium FAIRLY (4.0)
4 (Z4) very low high BAD (3.0)
5 (Z5) very low very high WORST (2.0)
6 (Z6) low very low WORST (5.0)
7 (Z7) low low FAIRLY (4.0)
8 (Z8) low medium FAIRLY (4.0)
9 (Z9) low high BAD (3.0)
10 (Z10) low very high WORST (2.0)
11 (Z11) medium very low FAIRLY (4.0)
12 (Z12) medium low FAIRLY (4.0)
13 (Z13) medium medium BAD (3.0)
14 (Z14) medium high BAD (3.0)
15 (Z15) medium very high WORST (2.0)
16 (Z16) high very low BAD (3.0)
17 (Z17) high low BAD (3.0)
18 (Z18) high medium BAD (3.0)
19 (Z19) high high WORST (2.0)
20 (Z20) high very high NOISY (1.0)
21 (Z21) very high very low NOISY (1.0)
22 (Z22) very high low NOISY (1.0)
23 (Z23) very high medium NOISY (1.0)
24 (Z24) very high high NOISY (1.0)
25 (Z25) very high very high NOISY (1.0)](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-6-320.jpg)
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The results show that the real-time VQA have good performance. This shown from the
average accuracy percentage of lowest that reaches 77.2% and highest that reaches 88.2%.
Even so, real-time VQA performance needs to be improved, especially when the video clarity
condition is on good clarity and bad clarity. This improvement problem will be future study.
Acknowledgment
Acknowledgment of the authors to Direktorat Riset dan Pengabdian Masyarakat
(DRPM), Direktorat Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset,
Teknologi, dan Pendidikan Tinggi Republik Indonesia (KEMENRISTEK DIKTI) for the support of
Research Funding Year 2017 on research scheme Penelitian Kerjasama Antar Perguruan
Tinggi (PEKERTI), that conducted by Universitas Bhayangkara Surabaya (UBHARA) and
Institut Teknologi Sepuluh Nopember (ITS) Surabaya, with the contract number
010/SP2H/K2/KM/2017, on May 4, 2017. Acknowledgment also to ITS Robotics, Information
and Communication Technology Center, ITS Telecommunication Network Laboratory and
UBHARA Surabaya Electrical Engineering Laboratory.
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[12] Rekha KB, Kumar R. High Quality Video Assessment Using Salient Features. Indonesian Journal of
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[13] Shakir M, Rehman O, Abbas Z, Masood A, Shahid W. Evaluation of Video Quality in Wireless
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[14] Yanyu W, Shirin M. Video Compression Encoding and Decoding Mechanism of H.264 Based on
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[15] White T, Reid G. Quality of PAL Colour TV Pictures Impaired by Random Noise: Stability of
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[16] International Telecommunication Union. Recommendation ITU-T P.800.1. Mean Opinion Score
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[17] Fischer W. Digital Video and Audio Broadcasting Technology: A Practical Engineering Guide. Berlin:
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[18] Ross TJ. Fuzzy Logic with Engineering Applications. West Sussex. Wiley. 2010.](https://image.slidesharecdn.com/177161-200824022618/85/Real-time-Video-Quality-Assessment-for-Analog-Television-Based-on-Adaptive-Fuzzy-Membership-Function-Tuning-12-320.jpg)
Real-time Video Quality Assessment for Analog Television Based on Adaptive Fuzzy Membership Function Tuning
- 1. TELKOMNIKA, Vol.16, No.3, June 2018, pp. 1071~1082 ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013 DOI:10.12928/TELKOMNIKA.v16i3.7161 1071 Received September 2, 2017; Revised April 4, 2018; Accepted April 29, 2018 Real-time Video Quality Assessment for Analog Television Based on Adaptive Fuzzy Membership Function Tuning Herti Miawarni* 1 , M. Mahaputra Hidayat 2 , Surya Sumpeno 3 , Eko Setijadi 4 1,2 Engineering Faculty, Bhayangkara Surabaya University, Indonesia 3,4 Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia *Corresponding author, e-mail: herti_mia@ubhara.ac.id Abstract Real-time VQA (Video Quality Assessment) is an important part in the effort to build tracking antenna system especially for analog TV. In this case, VQA must work in real-time to assess the video clarity level. VQA assessment results are valuable information for the decision-making process. Thus, the antenna can rotate automatically looking for the ideal direction without user’s control. In addition, the video clarity level on the TV screen can reach optimum according to the user's wishes. The biggest challenge to VQA is, VQA must be able to assess the video clarity level according to the user’s visual perception. Therefore, in this study, the MOS-VQS (Mean Opinion Score-Video Quality Subjective) was used as a visual perception approach. In addition, Adaptive FIS (Fuzzy Inference System) with membership function tuning was implemented for decision making. This was conducted as an effort to build a reliable real-time VQA. The test results show that real-time VQA that has been built has a good performance. This is shown from the average accuracy percentage of the lowest assessment reached 77.2% and the highest reached 88.2%. Keywords: Real-time VQA, adaptive FIS, membership function tuning, tracking antenna system, analog TV Copyright © 2018 Universitas Ahmad Dahlan. All rights reserved. 1. Introduction On analog TV, video clarity level is the video parameter that determines QoE (Quality of Experience). The video clarity level on analog TV is not only influenced by RF strength, but also influenced by antenna direction. Tracking antenna system is technology that required so that TV antenna direction is in the ideal states automatically. We assume that the study on tracking antenna systems for analog TV is very important because of the fact that TV antennas with auto tracking capabilities are not popular on the market. Auto tracking capabilities on tracking antenna systems allow the antenna to rotate automatically and understand the video clarity level on the user's TV screen. So, the main problem is how to realize reliable tracking antenna system that has auto tracking capabilities for analog TV reception. We assume that there are 4 points that are needed to build the tracking antenna system, among others are: a. Input parameters that used as reference for the tracking antenna system. b. VQA (Video Quality Assessment) that works in real time to assess video quality, especially video clarity level. VQA should have reliable algorithm to video quality assessing, and also a visual perception approaches. This is because the visual perception on the built system must approximate the user's visual perception in assessing video quality. c. Final decision making that determining antenna direction, tracking direction, tracking time, idle time and, etc. d. Antenna rotator construction and antenna design. The general purpose of this study is to produce the applicable technology for analog TV reception. In particular, to build a reliable tracking antenna system. In this case, the reliability of the tracking antenna system depends on real-time VQA reliability. While the specific purpose of this study is about how to build reliable real-time VQA (point b) that include algorithm for VQA and visual perception approaches. But in this case, real time VQA cannot stand alone without
- 2. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1072 any input parameter (point a). So, in this study, parameter input, algorithm for VQA and visual perception approaches will be discussed. First discussion is about input parameter. Tracking antenna systems require information about the video clarity level on the user's TV screen. This information is obtained from the AV-Out (Audio Video Output) connection on the rear panel of the TV. This connection produces CVBS signals continuously. The CVBS (Composite Video Baseband Signal) on AV-Out can describe the video clarity level [1]. Tracking antenna system based on CVBS signal processing has been proposed in previous studies [2]. However, both of previous study has not been developed in terms of VQA and visual perception. Second discussion is about algorithm for VQA, Assessment of the video clarity level is one form of the decision-making process. To achieve optimal results, the decision-making process should be simple, easy to control, and easy to interact with users. One of the most common algorithms used in the decision-making process is FIS (Fuzzy Inference System). In the previous study, FIS has been used for decision-making in an effort to improve the quality of information [3]. The implementation of FIS (Fuzzy Inference System) on VQA has been widely used, fuzzy rule evaluation has been proposed to measure image quality due to distortion based on HVS (Human Visual System) modeling [4]. FIS also used to predict the quality of video from several fidelity distortions, blurring distortion and contrast distortion [5]. FIS has been proposed to improve QoE (Quality of Experience) by building quality predictions on mobile device-based video [6-7]. In addition, FIS has also been used to predict the quality of wireless video streaming [8-9]. While in current study, FIS used as VQA for the tracking antenna systems that work in real-time and make faster and precise decision-making related to assessing video clarity level. Third discussion is about visual perception approaches. On video quality measurement, it is necessary to understand how users generally assess a video and its quality. So, visual perception is complex [10]. The popular method used to calculate video quality is PSNR (Peak Signal-to-Noise Ratio) or by using MSE (Mean Square Error) [11-14]. Another method that also often used is MOS (Mean Opinion Score) approaching. Previous study related to video quality measurement in analog TV technology is using MOS approach to video quality with random noise. The parameters used are the comparison of chrominance and luminance signal versus noise [15]. While in this study, the MOS approach uses another parameter that is the result of CVBS signal processing. MOS variant that used is MOS-VQS (Mean Opinion Score-Video Quality Subjective) because video clarity level assessment is subjective and it’s limited to noise and ghosting influence on analog video TV quality. Considering to previous discussion, the real-time VQA in this study was built from FIS algorithm for decision making process in assessing video clarity, MOS-VQS for visually perceptual approach and CVBS signal processing result for VQA's input parameters. Besides that, the membership function on FIS can be changed according to user’s perception. This is conducted so that the real time VQA built can adaptive to many diverse users visual perception. So, the FIS algorithm that used in this study is adaptive FIS with membership function tuning. 2. Methodology In an effort to realize reliable real-time VQA, the research methodology steps begin with the system design that includes hardware design and algorithm design. Then the second step is FIS algorithm simulation that includes membership function tuning. The third stage is real-time VQA performance test that includes VQA performance trial on default mode and VQA performance trial on user mode. User mode and default mode will be discussed on sub-chapter 3.4. The final step is overall results analysis. The research methodology is as shown in Figure 1. 3. System Design Figure 2 shows the system design of real-time VQA that proposed. FIS algorithm that serve as assessment machine are designed with visual perception approaches using MOS-VQS. The input of real-time VQA is the result of CVBS signal processing. The real-time VQA output is accurate information with regard to the video clarity level on the user's TV screen.
- 3. TELKOMNIKA ISSN: 1693-6930 Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni) 1073 Due to the focus of this study is limited to real-time VQA, video clarity level information that produce by real-time VQA will be displayed on the LCD display and then recorded as trial result. Each user certainly has a different perception in assessing the video clarity level. Therefore, this study proposed an access control for users. This access control is connected to the fuzzification process on the FIS. It is expected that the real-time VQA performance can correspond with the user's desires. Figure 1. Research Methodology Figure 2. System Design of Real-time VQA 3.1. Visual perception approaches using MOS-VQS MOS-VQS (Mean Opinion Score-Video Quality Subjective) is a subjectively visual perception approaches and this is a recommendation from ITU-T (International Telecommunication Union-Telecommunication standard section). Most of the MOS-VQS use 5 points quality scaling, among others are excelent, good, fair, poor, and bad [16]. But in this study, 5 scale were initialized as (good clarity, fair clarity, bad clarity, worst clarity, noisy). Then, each scale is visualized to the TV reception characteristics in the urban area as in the Table 1. Picture in the Table 1 obtained from observation on latitude and longitude 7 ° 19'12.0_S 112 ° 43'55.9_E. This coordinate is an area with many tall buildings that can disturb TV signal reception.
- 4. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1074 Table 1. Subjectively Video Clarity Level Classification with MOS-VQS [2] MOS Scale Standard Scale of Video Clarity Level in This Study TV reception characteristics Description 1. Excellent Good Clarity Perfect colors and minimum ghosting. 2. Good Fairly Clarity Perfect colors but fairly ghosting. 3. Fair Bad Clarity Blurred colors and ghosting looks dominant. 4. Bad Worst Clarity Ghosting looks extremely dominant. 5. Worst Noisy Noise looks extremely dominant, image and colors are disappear. 3.2. CVBS signal processing Tracking antenna system requires the input parameter as a reference. The AV-Out connection on the rear panel of the TV is the most potential item to used. The signal format on AV-Out is a CVBS analog signal. As in previous studies [1]-[2], counting the synchronization pulse on a CVBS signal is one way of obtaining information related to the video clarity level. The reason for using this way is because the synchronization pulse of CVBS signal on AV-Out depends on the video clarity level on the TV screen. If video clarity level on TV screen is becomes better, then shape and number of synchronization pulse on AV-Out becomes close to the CVBS standard. Figure 3 shows the design of CVBS signal processing. Figure 3. Design of CVBS Signal Processing [2] To calculate the synchronization pulse, a sync separator IC LM 1881 N used to separate the synchronization pulse from other signals (luminance signals, chrominance signal and burst signal). Analog comparator and inverting Schmitt trigger is used to ensure that voltage level of synchronization pulse is sufficient and can be processed by microcontroller. Then, synchronization pulse calculations are performed by timer counter on the AT-Mega
- 5. TELKOMNIKA ISSN: 1693-6930 Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni) 1075 32 microcontroller. In this case, the microcontroller calculates the rising edge of the transition between sync level and blanking level on the CVBS signal. In this study, the results of calculations called with rising transition and it will be processed using FIS algorithm. In this case, FIS used as real-time VQA. Real-time VQA output will produce values that describe the video clarity level accurately. Then the information will be displayed on the LCD display. In this study, FIS algorithm implemented into listing program on AT-Mega microcontroller 32 A. PAL BG (Phase Alternating by Line, B and G variant) is color encoding standard that used for analog television in Indonesia. In PAL BG, each frame contains 625 lines which divided into 2 fields (odd and even) [17]. The number of rising transition on the overall synchronization pulse are 640 per frame. This value is derived from the summing transition of each line. If every second there are 25 frames, then the rising transition number are 16,000 per second. The rising transition on CVBS signal sourced from AV-Out has special characteristics. The number of rising transition will be closer to 16,000 if the video clarity is better, and vice versa. In the previous study, rising transition used as the main parameter [1]. As a continuity of previous study, then in this study conducted development. In this study used two parameters which in this case we call as rising transition difference (Rd) and rising transition changes (Rc). Details of both parameters are as follows : a. Rising transition difference (Rd) is the difference between rising transition counting results with the standard value of 16,000. The smaller the value of this parameter, the video clarity level condition can be defined as becomes better and vice versa. Rising transition difference (Rd) is a single integer variable which is updated in real-time every second. Rising transition difference (Rd) can be expressed in equation (1). Rd = Abs [16,000 – rising transition] (1) b. Rising transition changes (Rc) is the difference between the current rising transition difference (Rd(Now)) with the previous rising transition difference (Rd(Previous)). The smaller the value of this parameter, the video clarity level condition can be defined as becomes better and vice versa. Rising transition changes (Rc) is an integer and is not a negative value. Rising transition changes (Rc) can be expressed in equation (2). Rc = Abs [ Rd(now) – Rd(Previous) ] (2) 3.3. FIS for assessment machine 3.3.1 Zero order fuzzy sugeno In this study, zero order fuzzy sugeno used as real-time VQA. The reason for using zero order is a lightweight computational load with the assumption that the microcontroller as the main processor has limited computing capability. Input of FIS obtained from two parameters, namely, rising transition difference (Rd) and rising transition changes (Rc). While the output parameters of the FIS are information related to the video clarity level. In the fuzzification process, the design of membership function on the input parameters in this study is arranged as in Figure 4. While the designing membership function on the output parameters shown in Figure 5. Figure 4. Membership Function for Rd and Rc Parameter
- 6. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1076 Figure 5. Membership Function for Output Parameter After the fuzzification process, then the next process is to determine the fuzzy rule. As many as 25 rules used in the fuzzy rule which shown as detail in Table 2. In the defuzzification process, the Weighted Average method used because it has relatively lightweight computing load [18]. Furthermore, the defuzzification process is expressed in the equation (3). z = ∑ 𝜇𝐶(𝑍𝑛) . 𝑍𝑛 ∑ 𝜇𝐶(𝑍𝑛) (3) Where: a. z is the output value sought. This value is real (float) number that represents the video clarity level. Furthermore, this value will be replaced with information by MOS-VQS scale, among others are: GOOD (for 4.6≥z≤5), FAIRLY (for 3.6≥z≤4.5), BAD (for 2.6≥z≤3.5), WORST (for 1.6≥z≤2.5) and NOISY (for 1≥z≤1.5). b. C is minimum value of membership grades on Rd and Rc parameters. For in this case, C >0≤1. c. Zn[Z1....Z25] value of fuzzy rule number to-n (Zn) which is not zero. For in this case, 1≥Zn≤ 5. Table 2. Fuzzy Rule Fuzzy Rule Number Rising Transition Difference Rising Transition Changes Video Clarity Level Information Displayed on LCD Display 1 (Z1) very low very low GOOD (5.0) 2 (Z2) very low low GOOD (5.0) 3 (Z3) very low medium FAIRLY (4.0) 4 (Z4) very low high BAD (3.0) 5 (Z5) very low very high WORST (2.0) 6 (Z6) low very low WORST (5.0) 7 (Z7) low low FAIRLY (4.0) 8 (Z8) low medium FAIRLY (4.0) 9 (Z9) low high BAD (3.0) 10 (Z10) low very high WORST (2.0) 11 (Z11) medium very low FAIRLY (4.0) 12 (Z12) medium low FAIRLY (4.0) 13 (Z13) medium medium BAD (3.0) 14 (Z14) medium high BAD (3.0) 15 (Z15) medium very high WORST (2.0) 16 (Z16) high very low BAD (3.0) 17 (Z17) high low BAD (3.0) 18 (Z18) high medium BAD (3.0) 19 (Z19) high high WORST (2.0) 20 (Z20) high very high NOISY (1.0) 21 (Z21) very high very low NOISY (1.0) 22 (Z22) very high low NOISY (1.0) 23 (Z23) very high medium NOISY (1.0) 24 (Z24) very high high NOISY (1.0) 25 (Z25) very high very high NOISY (1.0)
- 7. TELKOMNIKA ISSN: 1693-6930 Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni) 1077 3.3.2. Diversity of users satisfaction and adaptive fis with membership function tuning In this study, each user’s is assumed to have different levels of satisfaction against video clarity level on the TV screen. The diversity of user satisfaction levels will cause diversity of visual perceptions. On the other hand, the level of satisfaction also affects the tracking time needed for the tracking antenna system. In this case, not all users want to watch TV if video clarity level on the TV screen is only on good clarity condition and causes long tracking time. In order for real-time VQA to match with the user’s visual perceptions diversity, real-time VQA is designed to be able to receive manual input from the user. Thus, real-time VQA performance can be closer to the user satisfaction level. Therefore, in this research, adaptive FIS with membership function tuning is proposed and implemented. Membership function does not change automatically, but it can change flexibly to the user’s perception. In realization, users are given access to change membership function by simply pressing a button. It is intended to users can convey the visual perception to the system directly and easily without having to understand what is FIS algorithm. Users only need to press the GOOD button when the video clarity level is in good clarity condition, pressing the FAIRLY button when the video clarity level is in fairly clarity condition, and so on. Then the FIS algorithm will work in real-time to assess the video clarity level and display the information on the LCD. Then this information used for decision-making process on real-time VQA. Figure 6 shows the realization of real-time VQA. Referring to Figure 4 and Figure 6, the working principle of FIS with membership function tuning is as follows: a. If user presses the GOOD button as shown in Figure 6 then microcontroller will extract the Rd and Rc parameters values which obtained by CVBS signal processing. Then store the value into variable A as shown in Figure 4 on fuzzy membership function. b. If user presses the FAIRLY button as shown in Figure 6 then microcontroller will extract the Rd and Rc parameters values which obtained by CVBS signal processing. Then store the value into variable B as shown in Figure 4 on fuzzy membership function. c. If user presses the BAD button as shown in Figure 6 then microcontroller will extract the Rd and Rc parameters values which obtained by CVBS signal processing. Then store the value into variable C as shown in Figure 4 on fuzzy membership function. d. If user presses the WORST button (see Figure 6) then microcontroller will extract the Rd and Rc parameters values which obtained by CVBS signal processing. Then store the value into variable D as shown in Figure 4 on fuzzy membership function. e. If user presses the NOISY button (see Figure 6) then microcontroller will extract the Rd and Rc parameters values which obtained by CVBS signal processing. Then store the value into variable E as shown in Figure 4 on fuzzy membership function. f. After pressing the button, A, B, C, D and E, the value become differ for each video clarity level (good clarity, fair clarity, bad clarity, worst clarity, and noisy). Besides that, A, B, C, D and E value also become differ for each user’s subjectivity. It will produces the triangular legs in membership function has different sides and changeable according to user's perception. Figure 6. Realization of Real-time VQA
- 8. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1078 3.4. Trial scenario (user mode and default mode) On the implementation, the assessment on real-time VQA is obtained from user’s assessment (User Mode) and from default assessment (Default Mode). In User Mode, the membership function of FIS algorithm is determined by user. As shown in Figure 6, the PCB prototype provides a button for the user to enter input assessment which indirectly gives access to change the membership function settings on the FIS algorithm. While in Default Mode, membership functions in the FIS algorithm follow the listing program that has been compiled at early time. Membership function is determined based on CVBS signal processing measurement results and after MATLAB simulation process. 4. Results and Analysis 4.1. CVBS signal processing characteristic The trial was conducted to observe the values of rising transition difference (Rd) and rising transition changes (Rc) parameters in some video clarity conditions. The results of this trial, then will be used as a reference in preparing the membership function FIS algorithm, especially in Default Mode. The result of the trial in this session is as shown in Figure 7 and Figure 8. Figure 7. Rd Parameters at Various Video Clarity level Figure 8. Rc Parameters at Various Video Clarity Level The test result in Figure 7 shows that the value of the Rd parameter in a good clarity condition is smaller when compared to the noisy state. In good clarity condition, the minimum value is 0 and the maximum reaches 880. While in the noisy condition, the minimum value reaches 14,355 and the maximum reaches 16,000. The widest range of values is obtained from bad clarity conditions. In this condition, the value of the Rd parameter is in the range of 1,657 to 11,590. Overall, it can be concluded that, if video clarity level becomes better, and then value of the Rd parameter becomes smaller. Conversely, if video clarity level becomes worse, then value of the Rd parameter becomes greater. The test result in Figure 8 shows that the value of the Rc parameter in a good clarity condition is smaller when compared to the noisy condition. In good clarity condition, the minimum value is 0 and the maximum reaches 42. While in the noisy condition, the minimum value reaches 835 and the maximum reaches 2,278. Overall, it can be concluded that, the better the video clarity, the smaller the Rc value changes in every second. In this case, the value of Rc can be said to be more stable when compared with other conditions. Conversely, if the video clarity becomes worse, then value of the Rc parameter will become unstable. The test results in Figure 7 and Figure 8 show the difference in values of Rd and Rc parameters at some video clarity level. In other words, the Rd and Rc parameters are feasible to be used as fuzzy input parameters in an effort to build a reliable real-time VQA. Then, using the test results, the membership function in Default Mode is designed as in Figure 9. In this case, the membership function is triangular with different sides, because it adjusts the range of Rd 880 1934 11590 14488 16000 0 917 1657 9960 14355 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 good clarity fairly clarity bad clarity worst clarity noisy RdValue Video Clarity Level Based on User's Perception 42 101 332 1057 2278 0 38 98 318 835 0 500 1000 1500 2000 2500 good clarity fairly clarity bad clarity worst clarity noisy RcValue Video Clarity Level Based on User's Perception
- 9. TELKOMNIKA ISSN: 1693-6930 Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni) 1079 and Rc parameter values according to the test results in Figure 7 and Figure 8. While Figure 10 shows the rule surface of the simulation result using MATLAB toolbox. Figure 9. Membership Function Design on Default Mode Figure 10. Rule Surface MATLAB Toolbox on Default Mode 4.2. Performance test on default mode the purpose of the trial is to evaluate the performance of fis algorithm especially in the default mode. the fis algorithm as real-time vqa works well if the fis output visual perception close to subjective perception of the tv user. the trial process is performed on 5 video clarity conditions according to user’s perception. each condition are 100 times tested. figure 11 shows the results of trials in this session. the average accuracy percentage on the real-time vqa is parameter that observed in this trial. accuracy percentage is the percentage of conformity between real-time VQA perception and user’s perception. Figure 11. Real-time VQA Performance Test on Default Mode Test results show some important points, among others are as follows: a. VQA has good performance when the video clarity level is in good clarity condition. From 100 trials, 98 times real-time VQA declare good clarity and the rest declare fairly clarity. In other words, the accuracy percentage reached 98%. b. VQA has good performance when the video clarity level is in a fairly clarity condition that reaches 81%, the worst clarity condition reaches 79%, and the noisy condition reaches 100%. 98 2 81 1419 64 22 79 100 0 20 40 60 80 100 120 good clarity fairly clarity bad clarity worst clarity noisy Trial Video Clarity Level Based On User's Perception Real Time VQA describe GOOD Real Time VQA describe FAIRLY Real Time VQA describe BAD Real Time VQA describe WORST Real Time VQA describe NOISY
- 10. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1080 c. VQA has poor performance when the video clarity level is in bad clarity condition. The accuracy percentage only reaches 64%. This percentage is the lowest performance of real-time VQA. 4.3. Performance Test on User Mode This trial conducted by involving the user. Randomly, a user is selected to assess the video clarity level on the TV screen. User’s assessment conducted by pressing the button on the prototype PCB. The result of assessment by user, automatically changes fuzzy membership functions on the microcontroller (discussion on sub-chapter 3.3.2). Figure 12 shows the results of the real-time VQA performance test on the 1st user. Figure 12. Real-time VQA Performance Test on 1 st User (User Mode) Test results on the Figure 12, show some important points, among others are as follows: a. VQA has good performance when the video clarity level is in fairly clarity condition. Accuracy percentage reached 96%. Accuracy percentage on the bad clarity condition reaches 82%, accuracy percentage on the worst clarity condition reaches 100%, and accuracy percentage on the noisy condition reaches 100%. b. VQA has poor performance when the video clarity level is in good clarity condition the accuracy percentage is only 63%. This percentage is the lowest performance of real-time VQA especially in this session. While Figure 13 shows the membership function setting obtained from user’s assessment via button. Figure 13 shows how the user’s assessment could change triangle form on fuzzy membership function. It can be compare with Figure 9. Figure 13. Fuzzy Membership Function on 1 st User’s assessment 63 37 96 24 82 16 100 100 0 20 40 60 80 100 120 good clarity fairly clarity bad clarity worst clarity noisy Trial Video Clarity Level Based On User Perception Real Time VQA describe GOOD Real Time VQA describe FAIRLY Real Time VQA describe BAD Real Time VQA describe WORST Real Time VQA describe NOISY
- 11. TELKOMNIKA ISSN: 1693-6930 Real-time Video Quality Assessment for Analog Television Based on … (Herti Miawarni) 1081 4.4. Overall performance test This trials process only repeats of trial that has been conducted on the sub-chapter 4.3. But in this sub-chapter, trial process conducted on different users. Table 3 shows the results of the implementation trials. The results of trials in sub-chapters 4.2 and 4.3 are also included in the Table 3. Table 3. Real-time VQA Performance Test on Different User Performance Trial Accuracy Percentage on Various Video Clarity Level Condition (%) Average Accuracy Percentage (%)Good Clarity Fairly Clarity Bad Clarity Worst Clarity Noisy Default Mode 98 81 64 79 100 80.6 1st User 63 96 82 100 100 88.2 2nd User 89 84 48 74 100 79 3Rd User 80 81 65 98 100 84.8 4th User 83 91 69 87 100 86 5th User 89 96 44 93 100 84.4 6th User 49 86 95 100 100 86 7th User 90 84 53 100 100 85.4 8th User 87 89 49 96 100 84.2 9th User 73 76 92 93 100 86.8 10th User 72 96 66 82 100 83.2 11th User 59 71 93 100 100 84.6 12th User 73 79 54 80 100 77.2 13th User 46 72 77 94 100 77.8 14th User 87 84 51 93 100 83 15th User 76 86 48 88 100 79.6 Lowest Accuracy Percentage Test results show some important points, among others are as follows: a. In general, the implementation of FIS algorithm on real-time VQA shows good performance. This shown from the average percentage of the lowest accuracy that reach 77.2% and the highest reach 88.2%. b. From trials that performed on 1st user to the 15th user, it can be concluded that they has same visual perception in assessing the video clarity level only in the noisy condition. This is evidenced from the accuracy percentage ranging from user to 1 to 15 users. The accuracy percentage under noisy conditions is 100%. While in other video clarity level conditions, they have different visual perceptions. 4.5. Overall Results Analysis The analysis of overall trials in this study is obtained from the results in Table 3. Important points, among others are as follows: a. Overall, real-time VQA performance still needs to be improved especially when video clarity is in good clarity condition. Good clarity condition is the ultimate goal sought for each analog TV user, so setting membership function on fuzzy algorithm especially when good clarity condition still need special attention. b. Besides that, it still needs to be improved when video clarity levels on bad clarity condition. When referring to trial result on chapter 4.1, the Rd parameter range when the video clarity level in the bad clarity conditions is the widest between 1,657 and 11,590. Thus, the degree of obscurity in the fuzzy membership function also needs special attention. Assessment result on this condition shows that 5 membership function triangles that established are not enough for real-time VQA to describe the video clarity level. 5. Conclusion This study is an effort to build tracking antenna system for analog TV especially concern on real-time VQA. It is not easy to build real-time VQA that corresponding to subjectivity and diversity of user’s visual perceptions. Adaptive FIS algorithm with membership function tuning was used as real-time VQA. In this case, the membership function of Rd and Rc parameters can be changed according to user’s perception as simply only by pressing button.
- 12. ISSN: 1693-6930 TELKOMNIKA Vol. 16, No. 3, June 2018: 1071 – 1082 1082 The results show that the real-time VQA have good performance. This shown from the average accuracy percentage of lowest that reaches 77.2% and highest that reaches 88.2%. Even so, real-time VQA performance needs to be improved, especially when the video clarity condition is on good clarity and bad clarity. This improvement problem will be future study. Acknowledgment Acknowledgment of the authors to Direktorat Riset dan Pengabdian Masyarakat (DRPM), Direktorat Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia (KEMENRISTEK DIKTI) for the support of Research Funding Year 2017 on research scheme Penelitian Kerjasama Antar Perguruan Tinggi (PEKERTI), that conducted by Universitas Bhayangkara Surabaya (UBHARA) and Institut Teknologi Sepuluh Nopember (ITS) Surabaya, with the contract number 010/SP2H/K2/KM/2017, on May 4, 2017. Acknowledgment also to ITS Robotics, Information and Communication Technology Center, ITS Telecommunication Network Laboratory and UBHARA Surabaya Electrical Engineering Laboratory. References [1] Miawarni H, Setijadi E. Antenna Tracking System Based on Pulse of Synchronization CVBS: Design System And Analyze. International Electronics Symposium (IES). Bali. 2016; 18: 228. [2] Miawarni H, Hidayat MM, Sumpeno S, Setijadi E. Tracking System for Indoor TV Antenna Based on CVBS Signal Processing. Jurnal Elektronika dan Telekomunikasi (JET). Surabaya. 2017; 17(2): 48-55. [3] Suardinata, Kamalrulnuzam. A Fuzzy Logic Classification of Incoming Packet for Voip. TELKOMNIKA (Telecommunication, Computing, Electronics and Control). 2010; 8(2): 165–174. [4] Bock F, Steudel A, Clausert H, Glesner M. A Fuzzy Rule-Based System for the Subjective Quality Evaluation ff Distorted Images. Sixth International Conference on Image Processing and its Applications. Dublin. 1997; 1: 448. [5] Yao S, Lin W, Lu Z, Ong E, Etoh M. Objective Quality Assessment for Compressed Video. IEEE Proceedings of the International Symposium on Circuits and Systems (ISCAS). Bangkok. 2003; 4: 897. [6] Alreshoodi M, Danish E, Woods J, Fernando A, Alwis C. Prediction of Perceptual Quality for Mobile Video Using Fuzzy Inference Systems. IEEE Transactions on Consumer Electronics. 2015; 61(4): 569–554. [7] Pitas C, Charilas D, Panagopoulos A, Constantinou P. Adaptive Neuro-Fuzzy Inference Models for Speech and Video Quality Prediction in Real-World Mobile Communication Networks. IEEE Wireless Communications. 2013; 20(3): 80–88. [8] Danish E, Fernando A, Alreshoodi M, Woods J. A Hybrid Prediction Model for Video Quality by QoS/QoE Mapping in Wireless Streaming. IEEE International Conference on Communication Workshop (ICCW). London. 2015; 7: 1723. [9] Alreshoodi M, Olufemi A, Woods J, Walker S. Fuzzy Logic Inference System-Based Hybrid Quality Prediction Model for Wireless 4K UHD H.265-Coded Video Streaming. IET Networks. 2015; 4(6): 296–303. [10] Winkler S. Video Quality and Beyond. Proceeding on European Signal Processing Conference. Poznan. 2007; 15: 150. [11] Winkler S, Mohandas P. The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics. IEEE Transactions on Broadcasting. 2008; 54(3): 660–668. [12] Rekha KB, Kumar R. High Quality Video Assessment Using Salient Features. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS). 2017; 7(3): 767–772. [13] Shakir M, Rehman O, Abbas Z, Masood A, Shahid W. Evaluation of Video Quality in Wireless Multimedia Sensor Networks. International Journal of Electrical and Computer Engineering (IJECE). 2016; 6(1): 223-234. [14] Yanyu W, Shirin M. Video Compression Encoding and Decoding Mechanism of H.264 Based on FPGA. TELKOMNIKA (Telecommunication, Computing, Electronics and Control). 2016; 14(2): 85–91. [15] White T, Reid G. Quality of PAL Colour TV Pictures Impaired by Random Noise: Stability of Subjective Assessment. IEE Proceedings on Communications, Radar and Signal Processing. 1981; 128: 231. [16] International Telecommunication Union. Recommendation ITU-T P.800.1. Mean Opinion Score (MOS) Terminology. Geneva: ITU; 2006. [17] Fischer W. Digital Video and Audio Broadcasting Technology: A Practical Engineering Guide. Berlin: Springer. 2010. [18] Ross TJ. Fuzzy Logic with Engineering Applications. West Sussex. Wiley. 2010.