Bitrate-Based No-Reference Video Quality Assessment Combining the Visual Perception of Video Contents

Jun-cai, Yao; Liu, Guizhong

doi:10.1109/tbc.2018.2878360

Cited by 24 publications

(5 citation statements)

References 36 publications

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“…In the proposed algorithm, M is set to 3, T is set to 64, and H and W are 224 respectively. The input sizes of TDDR are (3,64,224,224) and the output sizes are (3,224,224), the data volume after dimensionality reduction is only 1.56% of the original data volume, and the number of parameters of the module itself is small. This process significantly reduces the data volume, saves the GPU memory and enables the network model to learn VQA tasks in an end-to-end manner.…”

Section: Trainable Data Dimensionality Reductionmentioning

confidence: 99%

“…The VQA algorithms can be divided into handcraft-based methods and deep learning-based methods. Handcraft-based methods usually use prior knowledge to extract features, and then map the features to quality scores [1][2][3][4][5][6] . For example, Saad et al 1 performed statistical analysis in the DCT domain, and obtained the quality score through support vector regression (SVR).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Saad et al 1 performed statistical analysis in the DCT domain, and obtained the quality score through support vector regression (SVR). Yao et al 3 combined bitrate and spatiotemporal features to obtain quality score. Korhonen 4 extracted low complexity features from all frames and extracted high complexity features from key frames.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

No-reference video quality assessment using data dimensionality reduction and attention-based pooling

Wang

Lai²

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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This paper proposes a new end-to-end no-reference (NR) video quality assessment (VQA) algorithm that makes use of dimensionality reduction and attention-based pooling. Firstly, the dataset is expanded through data enhancement based on frame sampling. Secondly, the cropped video blocks are input into the trainable data dimensionality reduction module which adopts 3D convolution to reduce the dimension of the data. Then, the dimensionality reduced data is input into the backbone of the algorithm to extract spatial features. The extracted features are pooled through attention-based pooling. Finally, the pooled features are regressed to the quality score through the full connection layer. Experimental results show that the proposed algorithm has achieved competitive performance on the LIVE, LIVE Mobile and CVD2014 datasets, and has low complexity.

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Section: Trainable Data Dimensionality Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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No-reference video quality assessment using data dimensionality reduction and attention-based pooling

Wang

Lai²

2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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“…The authors of [ 13 ] created a new no-reference metric for video quality assessment based on the bitrate. Firstly, it checks the influence of the bitstream, and the model was improved through features for visual perception such as TI, image contrast, and texture.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

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“…The hybrid layer not only uses the bit-stream information, but also uses the pixel information to assess the video quality. Juncai Yao comprehensively considered the characteristics of video and bit-stream information, and then added some weight coefficients to synthesize the hybrid method [22]. This method uses multiple influence parameters synthetically, so the process of extraction and calculation is relatively complex.…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Neural Network to Assess Network Video Quality

2022

KSII TIIS

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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.

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Bitrate-Based No-Reference Video Quality Assessment Combining the Visual Perception of Video Contents

Cited by 24 publications

References 36 publications

No-reference video quality assessment using data dimensionality reduction and attention-based pooling

No-reference video quality assessment using data dimensionality reduction and attention-based pooling

Adaptive Reservation of Network Resources According to Video Classification Scenes

Using Fuzzy Neural Network to Assess Network Video Quality

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