No reference objective quality metric for stereoscopic 3D video

Han, Yi; Yuan, Zhenhui; Muntean, Gabriel‐Miro

doi:10.1109/bmsb.2014.6873539

Cited by 18 publications

(2 citation statements)

References 17 publications

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“…In [56], an NR stereoscopic VQA metric that considers the correlation between the packet loss and perceptual video quality in the network is proposed. The metric yields better results than existing objective metrics so that it can be used in real time when monitoring network statistics.…”

Section: Nr Metricsmentioning

confidence: 99%

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

Coskun,

Nur Yilmaz,

Battisti

et al. 2023

J. Imaging

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A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications.

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Section: Nr Metricsmentioning

confidence: 99%

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

Coskun,

Nur Yilmaz,

Battisti

et al. 2023

J. Imaging

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show abstract

“…Han et al 72 proposed an NR VQ Metric (NVQM), which is a modified version of the 2D Video Quality Metric ITU-T G.1070, 73 for the perceptual quality assessment of real-time S3D videos, while taking into consideration the left and the right image views, the network packet loss and the perceptual quality of videos with different bit rates. The left and the right views are processed separately and then combined at the display side to give the perception of depth, which is vital for 3D experience.…”

Section: Nr Objective Metricsmentioning

confidence: 99%

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Biswas

Appina

Tamboli³

et al. 2021

Applications of Machine Learning 2021

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Objective quality metrics provide cost-efficient methods for quality evaluation, as they are practically algorithms, models, that avoid the necessity of subjective assessment, which is a precise but resource-consuming approach. Their ultimate measure of prediction accuracy fundamentally relies on the correlation between the estimated levels of quality and the actual subjective scores of perceived quality, rated by human individuals. Such metrics have already been developed for every single emerging technology where quality, in general, is relevant. This applies to stereoscopic 3D imaging as well, which is utilized in both industry, healthcare, education and entertainment. In this paper, we introduce an exhaustive analysis regarding the practical applications of objective quality metrics for stereoscopic 3D imaging. Our contribution addresses each and every state-of-the-art objective metric in the scientific literature, separately for image and video quality. The study differentiates the metrics by input requirements and supervision, and examines performance via statistical measures. Machine learning algorithms are particularly emphasized within the paper, such as the Deep Edge and COlor Signal INtegrity Evaluator (DECOSINE) using Segmented Stacked Auto-Encoder (S-SAE), different Convolutional Neural Network (CNN) frameworks, and transfer-learning-based methods like the Xception model, AlexNet, ResNet-18, ImageNet, Caffe, GoogLeNet, and also our very own transfer-learning-based methods. The paper focuses on the actual practical applications of the predictive models, and highlights relevant criteria, along with general feasibility, suitability and usability. The analysis of the investigated use cases also addresses potential future research questions and specifies the appropriate directives for quality-focused, user-centric development.

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Network reputation-based stereoscopic 3D video delivery in heterogeneous networks

Yuan

Muntean

2014

2014 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting

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No reference objective quality metric for stereoscopic 3D video

Cited by 18 publications

References 17 publications

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues

On the practical applications of objective quality metrics for stereoscopic 3D imaging

Network reputation-based stereoscopic 3D video delivery in heterogeneous networks

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