• Journal of Multimedia Information System
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• v.9 no.1
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• pp.33-42
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• 2022

In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.51-60
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• 2022

This paper raised the need to examine how the online education environment triggered by COVID-19 and the smart learning environment can be established in consideration of the improvement of education and learning through learning analysis. Many studies are being conducted in Korea, and the Ministry of Education is continuously striving to build a smart school by promoting strategies for promoting smart education on the way to a talent powerhouse. Nevertheless, there is no unified definition of smart learning, and it can be seen as customized (individualized) learning using smart devices. However, most of the discussions on the construction of smart schools so far have limitations in that they are limited to physical spaces. Accordingly, the opinions of teachers and learners were not sufficiently reflected in the establishment of the facility. This study intends to study smart learning in various departments. In addition, the subjects students in charge of the co-researcher of this study were analyzed. The total number of subjects was 951, and 434 responded to this study survey. In addition, students were well accepting the online environment, and in the future, regardless of COVID-19, research will be presented to improve mutual communication between professors and students in smart learning.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.75-86
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• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.127-136
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• 2022

In MMORPGs, there are many problems with the Grinding player experience. This research divides the Grinding player experience into four dimensions: Grinding in-Autonomy, Competence, Relatedness and Positive affect through theoretical investigation of game experience. Through the study of Litman (2008), Curiosity is divided into two dimensions, I-Type Curiosity and D-Type Curiosity, and the relationship between Curiosity and Grinding player experience is studied. By distributing questionnaires, collecting data, and using SPSS software to conduct reliability analysis, validity analysis, correlation analysis and multiple regression analysis on the data, it is verified that in MMORPG, I-Type Curiosity can positively affect Grinding in-Autonomy, Competence, Relatedness and Positive affect. D-Type Curiosity can positively affect Grinding in-Autonomy, Competence and Positive affect, but D-Type Curiosity has no statistical relationship with Grinding in-Relatedness. And through the standardized coefficient (Beta) value, between the Curiosity factors, I-Type Curiosity has a greater impact on Grinding in-Autonomy and Positive affect, and D-Type Curiosity has a greater impact on Grinding in-Competence. Finally, from the perspective of I-Type Curiosity and D-Type Curiosity, combined with the drawbacks of the MMORPG Gringding mechanism, some concrete and feasible suggestions and optimization schemes are put forward to improve the Grinding player experience. This research result can provide some feasible suggestions for MMORPG developers and designers, optimize the MMORPG Grinding mechanism from the perspective of I-Type Curiosity and D-Type Curiosity, and improve the Grinding player experience. It can provide appropriate assistance for the improved development of MMORPG games.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.137-144
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• 2022

This research aims to identify the relationship between visual perception and emotion by the types of fear responses elicited from warning images on cigarette packages as well as the effectiveness of the size of such images through questionnaires and eye-tracking experiments with twenty university students from the colleges based in Busan. The research distinguished and analyzed the warning images as rational appeals and emotional appeals by the degree of fear and disgust and the result concurred with the research conclusions of Maynard that people would naturally avoid eye contact when presented with a warning image on cigarette packages. Also, eye avoidance was highly identified with larger (75%) warning images. While the previous research mostly adopted the self-rated validation method, this research tried to make the methodology more objective by adopting both questionnaires and eye-tracking experiments. Through this research, authors contribute to finding effective warning images on cigarette packages in a way to increase public awareness of the dangers of smoking and discourage smoking. Further research is recommended to explore the effectiveness of using explicit images on cigarette packages by the types of smokers such as heavy smokers, normal smokers, and non-smokers.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.87-92
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• 2022

In this study, we aimed to illustrate that the thresholding method gives different results when tested on the original and the refined histograms. We use the global thresholding method, the well-known image segmentation method for separating objects and background from the image, and the refined histogram is created by the neighborhood distinction metric. If the original histogram of an image has some large bins which occupy the most density of whole intensity distribution, it is a problem for global methods such as segmentation and contrast enhancement. We refined the histogram to overcome the big bin problem in which sub-bins are created from big bins based on distinction metric. We suggest the refined histogram for preprocessing of thresholding in order to reduce the big bin problem. In the test, we use Otsu and median-based thresholding techniques and experimental results prove that their results on the refined histograms are more effective compared with the original ones.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.277-284
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• 2021

Despite skepticism about commercial potential, the stereoscopic 3D cinema is still a form that any filmmaker can choose to employ for its aesthetics and its immersive potential. Based on a haptic illusion, the stereoscopic media content requires a new perspective different from the principle of 2D media content in terms of creation and acceptance. This paper examines the technical and aesthetic issues of stereoscopic 3D film production from the perspective of today's emerging realistic and immersive media through a case study. One of the key factors for successful content creation and research and development in stereoscopic 3D cinema is the combination of artistic principles together with technical mastering of the new image technology. The purpose of this paper is to outline the principal challenges and research topics in stereoscopic 3D cinema through a case study of stereoscopic 3D pilot film production for the 'User-Selective UHD Stereoscopic Media Service Platform' of the ETRI (Electronics and Telecommunications Research Institute). This paper intends to examine stereoscopic 3D filmmaking workflow and production methodologies, as well as technical elements and aesthetic issues.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.