• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2053-2067
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• 2023

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.3
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• pp.161-166
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• 2011

In this paper, for analysing the major factors giving the effect to the quality of HD level video on the multicasting service, we tried this test experiment on the College school network similar to the real service network environment. We measure the video quality distortion on the multicasting HD level video according to generating and increasing the broadcasting traffic on the situation that apply the QoS technique to the test network and not apply the QoS technique and then find out the threshold value of network factors giving the effect to the video quality distortion on the multicasting HD level video service. This paper can be used for guaranteeing the constant video quality and reducing the video quality distortion on the multicasting HD level video service.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.355-358
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• 2012

Although quality metrics for 2D video quality assessment have been proposed, the quality models on stereoscopic video have not been widely studied. In this paper, a new objective video quality metric for s tereoscopic video is proposed. The proposed algorithm consider three factors to evaluate stereoscopic video quality: blocking artifact, blurring artifact, and the difference between left and right view of stereoscopic vide o. The results show that the proposed algorithm has a higher correlation with DMOS than the others.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.592-606
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• 2011

Currently, the development of multimedia communication has progressed so rapidly that the video program service has become a requirement for ordinary customers. The quality of experience (QoE) for the visual signal is of the fundamental importance for numerous image and video processing applications, where the goal of video quality assessment (VQA) is to automatically measure the quality of the visual signal in agreement with the human judgment of the video quality. Considering the codec effect to the video quality, in this paper an efficient non-reference (NR) VQA algorithm is proposed which estimates the video quality (VQ) only by utilizing the distorted video signal at the destination. The VQA feature vectors (FVs) which have high relationships with the subjective quality of the distorted video are investigated, and a hybrid NR VQA (HNRVQA) function is established by considering the multiple FVs. The simulation results, testing on the SDTV programming provided by VCEG Phase I, show that the proposed algorithm can represent the VQ accurately, and it can be used to replace the subjective VQA to measure the quality of the video signal automatically at the destinations.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.155-158
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• 2007

In this paper, we solved the underestimation problem of PSNR, which is caused by repeated frames, by easily synchronizing original and decoded frames using the proposed marks. Also we propose full-reference system which can be applied for measuring the quality of various kinds of video communication systems, e.g. wireless handsets, mobile phones and applications for PC. In addition, we propose a new video quality assessment metric using video communication parameters, i.e. frame rate and delay. According to the experiments, the proposed metric is not only appropriate for real-time video communication systems but also shows better correlation with the subjective video quality assessment than PSNR. The proposed measuring system and metric can be effectively used for measuring and standardizing the video quality of future communications.

• ETRI Journal
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• v.43 no.3
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• pp.538-548
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• 2021

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2377-2389
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• 2022

At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.

• International Journal of Contents
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• v.10 no.4
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• pp.23-29
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• 2014

Online video advertising is now an increasingly important tool for marketers to reach and connect with their consumers. The purpose of this study was to empirically investigate the impact of video format on online video advertising. More specifically, this study aimed to explore whether online video quality and image size influences viewer responses toward online video advertising. By conducting an experimental study on YouTube, the results suggested that enhanced video quality of online advertising may have an important impact on effectiveness of the advertising, and the concept of presence is a key to understanding the effects of enhanced video quality in online advertising.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.975-979
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• 2010

Existing quality adaptation schemes are useful for improving network stability in a congested network, but these schemes do not guarantee the quality of experience on video streaming due to the frequent variations in playback quality for a streaming service. In this paper, we propose a network-adaptive quality adaptation scheme to guarantee the quality of experience on SVC(Scalable Video Coding) video streaming. The proposed scheme estimates the available bandwidth by bandwidth measurement scheme using media characteristic, and then smoothly adjusts the quality level of SVC video stream according to the estimated bandwidth. Through the simulation, we prove that our scheme guarantees the quality of experience for video streaming by minimizing quality variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.324-340
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• 2010

Video streaming service over wireless networks is a challenging task because of the changes in the wireless channel conditions that can occur due to interference, fading, and station mobility. To provide an efficient wireless video streaming service, the rate adaptation scheme should improve wireless node performance and channel utilization. Moreover, the quality adaptation scheme should be considered at the streaming application. To meet these requirements, we propose a new cross-layer design for video streaming over wireless networks. This design includes the rate and quality adaptation schemes. The rate adaptation scheme selects the optimal transmission mode and resolves the performance anomaly problem. Based on performance improvement by the proposed rate adaptation scheme, our quality adaptation scheme improves the quality of video streaming. Through performance evaluations, we prove that our cross-layer design improves the wireless channel utilization and the quality of video streaming.