• 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.

• 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.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1247-1252
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• 2004

A objective quality assessment for the evaluation performance of a video transmission system as a basic step for construction of digital video transmission system between the spots and headquarter. Due to a limit of an analog video transmission system and the developments of various digital media and digital video standards, the need of introduce of a digital system are increased gradual1y, At this points. previously performance evaluations are performed and the quality assessment is the most important thing. We can be divided quality assessment method by the subjective quality assessment and objective quality assessment. The subjective quality assessment method has some problems which are required high cost and much time to evaluate the quality, And because existing objective quality assessment method such that PSNR are based on an analog form, the correlation with subjective data is very low. Therefore we design a new objective quality assessment method using Gabor wavelet transform reflecting HVS(Human Visual System), Designed objective quality assessment method is superior to other objective method such that PSNR or EPSNR In this paper, we proposed objective quality assessment using Gabor wavelet can be used for performance evaluation and verification of video transmission system.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.197-209
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• 2009

Stereoscopic video delivers depth perception to users contrary to 2D video. Therefore, we need to develop a new video quality assessment model for stereoscopic video. In this paper, we propose a new method for objective assessment of stereoscopic video. The proposed method detects blocking artifacts and degradation in edge regions such as in conventional video quality assessment model. And it detects video quality difference between views using depth information for efficient quality prediction. We performed subjective assessment of stereoscopic video to check the performance of the proposed method, and we confirmed that the proposed algorithm is superior to the existing method in PSNR in respect to correlation with results of the subjective assessment.

• 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.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1447-1453
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• 2020

In this paper, we propose a method for no-reference quality assessment of sports videos using 3D shearlet transform and deep residual neural networks. In the proposed method, 3D shearlet transform-based spatiotemporal features are extracted from the overlapped video blocks and applied to logistic regression concatenated with a deep residual neural network based on a conditional video block-wise constraint to learn the spatiotemporal correlation and predict the quality score. Our evaluation reveals that the proposed method predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.50-55
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• 2013

This article presents a simple and reasonable in-service, real-time, and continuous single-ended objective video quality assessment model for DTV broadcasting using a multiburst signal at the bottom of the transient effect area, similar to Vertical Interval Test Signals. The issue of in-service video-quality monitoring in DTV broadcasting is addressed, and an effective method of quality monitoring is presented. The proposed method is also implemented and tested in a range of situations using a simulated HDTV broadcasting network.

• 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.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.387-396
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• 2009

No-Reference video-quality assessments are divided into two kinds of metrics based on decoding pixel domain or the bitstream one. Traditional full-/reduced- reference methods have difficulty to be deployed as realtime video transmission because it has problems of additional data, complexity, and assessment accuracy. This paper presents simple and highly accurate no-reference video-quality assessment in realtime video transmission. Our proposed method uses quantization parameter, motion vector, and information of transmission error. To evaluate performance of the proposed algorithm, we perform subjective test of video quality with the ITU-T P.910 Absolute Category Rating(ACR) method and compare our proposed algorithm with the subjective quality assessment method. Experimental results show the proposed quality metric has a high correlation (85%) in terms of subjective quality assessment.