Objective quality assessment of 3D stereoscopic video based on motion vectors and depth map features

Mahmood, Sawsen Abdulhadi; Ghani, Rana Fareed

doi:10.1109/ceec.2015.7332721

Cited by 9 publications

(5 citation statements)

References 7 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%

“…They used the ITU-T G.1070 settings to model the packet loss artefacts and quality was measured by computing the correlation between perceptual scores and packet loss rates at different bit rates. Mahamood and Ghani [44] proposed an S3D NR VQA metric based on computing the motion vector lengths and depth map features. They concluded that the number of bad frames in a video is a good predictor of motion and depth quality of an S3D video.…”

Section: S3d Video Quality Assessmentmentioning

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%

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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show abstract

“…The proposed NR metric in [59], which examines the effect of the variable network conditions on the 3D-video quality, uses the frame rate, bit rate, and network-packet-loss rate. In [60], the proposed NR metric considers the motion vector lengths and depth information for the 3D-video-quality evaluation. In [61], an NR 3D objective VQA metric that estimates the 3D quality by taking into account the spatial distortions, excessive disparity, depth representation, and temporal information of the video is proposed.…”

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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“…Multiple NR S3D VQA models [33,34,35,36] are based on computing the motion vector strength, temporal complexities, successive frame difference statistics, packet loss measurements, structural strengths of spatial features, and edge features of intra and inter disparities. Mahamood et al [37] and Silva et al [38] proposed NR VQA models for S3D videos based on estimating the motion and depth qualities. These algorithms compute the correlation between histograms of motion vectors to measure the temporal quality, and depth quality is measured by computing the structural properties of depth frames.…”

Section: No-reference S3d Vqa Modelsmentioning

confidence: 99%

Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality

Biswas

Appina

Kara

et al. 2022

Signal Processing: Image Communication

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Objective quality assessment of 3D stereoscopic video based on motion vectors and depth map features

Cited by 9 publications

References 7 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

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

Jomodevi: A joint motion and depth visibility prediction algorithm for perceived stereoscopic 3D quality

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