• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.250-253
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• 2009

This paper presents NR (No Reference) Quality assessment method for IPTV or mobile IPTV. Because No Reference quality assessment method does not access the original signal so it is suitable for the real-time streaming service. Our proposed method use decoding parameters, such as quantization parameter, motion vector, and packet loss as a major network parameter. To evaluate performance of the proposed algorithm, we carried out subjective test of video quality with the ITU-T P.910 ACR (Absolute Category Rating) method and obtained the mean opinion score (MOS) value for QVGA 180 video sequence coded by H.264/AVC encoder. Experimental results show the proposed quality metric has a high correlation (84%) to subjective quality.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.127-133
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• 2009

With advances in video technology, many researchers are interested in video quality assessment to prove better performance of proposed algorithms. Since human visual system is too complex to be formulated exactly, many researches about video quality assessment are in progressing. No-reference video-quality assessment is suitable for various video streaming services, because of no requested additional data and network capacity to perform quality assessment. In this paper, we propose a novel no-reference video-quality assessment method with the estimation of dynamic range distortion. To measure the performance, we obtain mean opinion score (MOS) data by subject video quality test with the ITU-T P.910 Absolute Category Rating (ACR) method. And, we compare it with proposed algorithm using 363 video sequences. Experimental results show that the proposed algorithm has a higher correlation with obtained MOS.

• 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.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.255-265
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• 2013

3DTV is expected to be a promising next-generation broadcasting service. On the other hand, the visual discomfort/fatigue problems caused by viewing 3D videos have become an important issue. This paper proposes a perceptual quality assessment metric for a stereoscopic video (SV-PQAM). To model the SV-PQAM, this paper presents the following features: temporal variance, disparity variation in intra-frames, disparity variation in inter-frames and disparity distribution of frame boundary areas, which affect the human perception of depth and visual discomfort for stereoscopic views. The four features were combined into the SV-PQAM, which then becomes a no-reference stereoscopic video quality perception model, as an objective quality assessment metric. The proposed SV-PQAM does not require a depth map but instead uses the disparity information by a simple estimation. The model parameters were estimated based on linear regression from the mean score opinion values obtained from the subjective perception quality assessments. The experimental results showed that the proposed SV-PQAM exhibits high consistency with subjective perception quality assessment results in terms of the Pearson correlation coefficient value of 0.808, and the prediction performance exhibited good consistency with a zero outlier ratio value.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.316-320
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• 2009

In this paper, a reference-free perceptual quality metric is proposed for image assessment. It measures the amount of overall blockiness and blurring in the image. And edge-oriented artifacts, such as ringing, mosaic and staircase noise are also considered. In order to give a single quality score, the individual artifact scores are adaptively combined according to the difference between the edge-oriented artifacts and other artifacts. The quality score obtained by the proposed algorithm shows strong correlation with the MOS values by VQEG.

• 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.13 no.6
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• pp.849-858
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• 2008

In this paper, a no-reference perceptual metric is proposed for image quality assessment. It measures the amount of overall blockiness and blurring of the image and evaluates the amount of ringing, staircase, and mosaic noises around the strong edges. Finally, the individual scores are combined by a fuzzy integral to generate the final quality score of the image. The quality scores obtained by the proposed algorithm show strong relationship with the MOS(Mean Opinion Score) values by experts.