Stereoscopic video quality measurement with fine-tuning 3D ResNets

Imani, Hassan; Islam, Md Baharul; Junayed, Masum Shah; Aydın, Tarkan; Arıca, Nafiz

doi:10.1007/s11042-022-13485-9

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…It is used for stereoscopic video quality assessment [53][54][55] and contains 12 pure and 288 distorted videos. These videos were recorded with a Panasonic camera, and their resolution is 1920 × 1080 .…”

Section: Lfovias3dph2 50 Datasetmentioning

confidence: 99%

Stereoscopic video deblurring transformer

Imani,

Islam,

Junayed

et al. 2024

Sci Rep

Self Cite

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Stereoscopic cameras, such as those in mobile phones and various recent intelligent systems, are becoming increasingly common. Multiple variables can impact the stereo video quality, e.g., blur distortion due to camera/object movement. Monocular image/video deblurring is a mature research field, while there is limited research on stereoscopic content deblurring. This paper introduces a new Transformer-based stereo video deblurring framework with two crucial new parts: a self-attention layer and a feed-forward layer that realizes and aligns the correlation among various video frames. The traditional fully connected (FC) self-attention layer fails to utilize data locality effectively, as it depends on linear layers for calculating attention maps The Vision Transformer, on the other hand, also has this limitation, as it takes image patches as inputs to model global spatial information. 3D convolutional neural networks (3D CNNs) process successive frames to correct motion blur in the stereo video. Besides, our method uses other stereo-viewpoint information to assist deblurring. The parallax attention module (PAM) is significantly improved to combine the stereo and cross-view information for more deblurring. An extensive ablation study validates that our method efficiently deblurs the stereo videos based on the experiments on two publicly available stereo video datasets. Experimental results of our approach demonstrate state-of-the-art performance compared to the image and video deblurring techniques by a large margin.

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Section: Lfovias3dph2 50 Datasetmentioning

confidence: 99%

Stereoscopic video deblurring transformer

Imani,

Islam,

Junayed

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

Imani,

Islam

2024

SN COMPUT. SCI.

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IV-SSIM—The Structural Similarity Metric for Immersive Video

Dziembowski,

Nowak,

Stankowski

2024

Applied Sciences

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In this paper, we present a new objective quality metric designed for immersive video applications—IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work—IV-PSNR (immersive video peak signal-to-noise ratio)—which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels between multiple views. The effectiveness of IV-SSIM was compared with 16 state-of-the-art quality metrics (including other metrics designed for immersive video). Tested metrics were evaluated in an immersive video coding scenario and against a commonly used image quality database—TID2013—showing their performance in both immersive and typical, non-immersive use cases. As presented, the proposed IV-SSIM metric clearly outperforms other metrics in immersive video applications, while also being highly competitive for 2D image quality assessment. The authors of this paper have provided a publicly accessible, efficient implementation of the proposed IV-SSIM metric, which is used by ISO/IEC MPEG video coding experts in the development of the forthcoming second edition of the MPEG immersive video (MIV) coding standard.

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Stereoscopic video quality measurement with fine-tuning 3D ResNets

Cited by 4 publications

References 56 publications

Stereoscopic video deblurring transformer

Stereoscopic video deblurring transformer

Objective Quality Assessment of Stereoscopic Video Using Inflated 3D Features

IV-SSIM—The Structural Similarity Metric for Immersive Video

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