Spatio-temporal segmentation based continuous no-reference stereoscopic video quality prediction

Sazzad, Z. M. Parvez; Yamanaka, S.; Horita, Yuukou

doi:10.1109/qomex.2010.5517772

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…This database is a combination of H.264 and JP2K compression artefact distortions. They utilized the JM reference software to add H.264 compression artefacts by varying the quantization parameter (QP = 32,38,44). JP2K artifacts (2,8,16,32 Mb/s) are added on a frame-by-frame basis for both views.…”

Section: Resultsmentioning

confidence: 99%

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No-Reference Stereoscopic Video Quality Assessment Algorithm Using Joint Motion and Depth Statistics

Appina

Jalli

Battula

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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We present a no-reference (NR) quality assessment algorithm for assessing the perceptual quality of natural stereoscopic 3D (S3D) videos. This work is inspired by our finding that the joint statistics of the subband coefficients of motion (optical flow or motion vector magnitude) and depth (disparity map) of natural S3D videos possess a unique signature. Specifically, we empirically show that the joint statistics of the motion and depth subband coefficients of S3D video frames can be modeled accurately using a Bivariate Generalized Gaussian Distribution (BGGD). We then demonstrate that the parameters of the BGGD model possess the ability to discern quality variations in S3D videos. Therefore, the BGGD model parameters are employed as motion and depth quality features. In addition to these features, we rely on a frame-level spatial quality feature that is computed using a robust off-the-shelf NR image quality assessment (IQA) algorithm. These frame-level motion, depth and spatial features are consolidated and used with the corresponding S3D video's difference mean opinion score (DMOS) labels for supervised learning using support vector regression (SVR). The overall quality of an S3D video is computed by averaging the frame-level quality predictions of the constituent video frames. The proposed algorithm, dubbed Video QUality Evaluation using MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state-of-the-art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality assessment databases.

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Section: Resultsmentioning

confidence: 99%

“…Sazzad et al [38] proposed an S3D NR VQA metric based on spatiotemporal segmentation. They measured structural loss by computing the edge strength degradation in each segment and motion vector length was measured to estimate the temporal cue loss.…”

Section: S3d Video Quality Assessmentmentioning

confidence: 99%

No-Reference Stereoscopic Video Quality Assessment Algorithm Using Joint Motion and Depth Statistics

Appina

Jalli

Battula

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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“…Sazzad et al 69 proposed an NR continuous VQA method based on spatio-temporal segmentation for predicting the perceptual quality of S3D videos coded with MPEG-2 MP@ML with different bit rates. The left and the right views of the given S3D video is individually converted into frames and the partition of videos in the subtemporal and temporal segment is performed using temporal segmentation with both the segments being partially overlapping.…”

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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“…The NR-SIQA algorithms are usually designed with the prior knowledge of the distortion process [15][16][17][18]. However, NR-SQIA are less efficient in providing a high correlation with the subjective quality evaluations because of the absence of the reference image information.…”

Section: Introductionmentioning

confidence: 99%

Structured entropy of primitive: big data‐based stereoscopic image quality assessment

Liu

Yang

Rho³

et al. 2017

IET Image Processing

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Spatio-temporal segmentation based continuous no-reference stereoscopic video quality prediction

Cited by 15 publications

References 16 publications

No-Reference Stereoscopic Video Quality Assessment Algorithm Using Joint Motion and Depth Statistics

No-Reference Stereoscopic Video Quality Assessment Algorithm Using Joint Motion and Depth Statistics

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

Structured entropy of primitive: big data‐based stereoscopic image quality assessment

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