An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis

Rohil, Mukesh Kumar; Gupta, Neetika; Yadav, Prakash Chand

doi:10.1007/s11760-019-01543-z

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…In image processing, it is generally considered that wellfocused images have sharper edges and therefore have larger gradient function values. The Lapras algorithm is sensitive and can obtain fast results in images of different sizes [21][22][23][24].…”

Section: B Reduce Loss During Image Conversionmentioning

confidence: 99%

A Study on the Realization of Virtual Simulation Face Based on Artificial Intelligence

Hou

Kim

Zhang

et al. 2023

JICCE

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In recent years, as computer-generated imagery has been applied to more industries, realistic facial animation is one of the important research topics. The current solution for realistic facial animation is to create realistic rendered 3D characters, but the 3D characters created by traditional methods are always different from the actual characters and require high cost in terms of staff and time. Deepfake technology can achieve the effect of realistic faces and replicate facial animation. The facial details and animations are automatically done by the computer after the AI model is trained, and the AI model can be reused, thus reducing the human and time costs of realistic face animation. In addition, this study summarizes the way human face information is captured and proposes a new workflow for video to image conversion and demonstrates that the new work scheme can obtain higher quality images and exchange effects by evaluating the quality of No Reference Image Quality Assessment.

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Section: B Reduce Loss During Image Conversionmentioning

confidence: 99%

A Study on the Realization of Virtual Simulation Face Based on Artificial Intelligence

Hou

Kim

Zhang

et al. 2023

JICCE

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“…With high-contrast edges, the ringing artifact is the most obvious in areas with smoother textures during the reconstruction process. Ringing shows ripple or vibration structures near the strong edge [ 20 ]. A ringing artifact example is shown in Figure 4 b, in which the marked letters show the phenomenon of boundary ripples.…”

Section: Pea-based Video Quality Indexmentioning

confidence: 99%

“…In [ 19 ], blocking, packet-loss and freezing artifacts were obtained to predict video quality. Rohil et al [ 20 ] developed a holistic NR-VQA model based on quantifying certain distortions in video frames, such as ringing and contrast distortion. Next, the intensity values of various distortions were input to the neural network to evaluate the quality of videos.…”

Section: Introductionmentioning

confidence: 99%

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Lin

Yang

Wang

et al. 2021

Sensors

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Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.

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“…On this basis, most studies on video user perception relied on mobile edge computing (MEC) to localize the ability to deal with massive data and the advantages of high computing and storage data, thus analyzing and processing a large number of data streams to extract multiple network characteristic indicators (such as transmission delay, jitter, etc.). And they used a neural network, linear regression, and other methods to analyze the correlation between network characteristics and QoE [7][8][9]. Subsequently, the user QoE perception model was established to accurately perceive the video quality.…”

Section: Introductionmentioning

confidence: 99%

User Experience Perception Algorithm for Long and Short Videos Based on Multiple Nonlinear Regression

Dai

Zhou

Tian

et al. 2022

Mobile Information Systems

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The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user’s subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types.

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An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis

Cited by 10 publications

References 27 publications

A Study on the Realization of Virtual Simulation Face Based on Artificial Intelligence

A Study on the Realization of Virtual Simulation Face Based on Artificial Intelligence

Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

User Experience Perception Algorithm for Long and Short Videos Based on Multiple Nonlinear Regression

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