Subjective and Objective Video Quality Assessment of 3D Synthesized Views With Texture/Depth Compression Distortion

Liu, Xiangkai; Zhang, Yun; Hu, Sudeng; Kwong, Sam; Kuo, C.-C. Jay; Peng, Qiang

doi:10.1109/tip.2015.2469140

Cited by 78 publications

(47 citation statements)

References 47 publications

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“…The first feature focuses on capturing the temporal flicker distortion and the second feature is used to measure the change of the spatiotemporal activity in the synthesized sequence due to blurring and blockiness distortion caused by texture compression. The performances of the proposed metric evaluated on the synthesized video quality database SIAT [11] are better than the performances of the commonly used image/video quality assessment methods.…”

Section: Quality Assessment Of Dibr-synthesized Viewmentioning

confidence: 83%

“…The view synthesis distortion flickering locates on the edge of the foreground object which has a movement. Flickering can be observed as significant and high-frequency alternated variation between different luminance levels [11]. The temporal flicker distortion is the most significant difference between the traditional 2D video and the synthesized video.…”

Section: Distortion In the Dibr-synthesized Viewmentioning

confidence: 99%

“…A full-reference video quality assessment of synthesized view with texture/depth compression presented in [11] focuses on the temporal flicker distortion due to depth compression distortion and the view synthesis process. It is based on two quality features which are extracted from both spatial and temporal domains of the synthesized sequence.…”

Section: Quality Assessment Of Dibr-synthesized Viewmentioning

confidence: 99%

“…When the morphological wavelet decomposition is used in the first stage of morphological multi-scale IQA framework, multi-scale wavelet mean squared error (MW-MSE) is calculated as weighted sum of MSE ji values for all subbands at all scales of the two wavelet representations as final pooling (11).…”

Section: The Calculation Of Mw-psnrmentioning

confidence: 99%

“…For the MCL-3D database the best MW-PSNR performances using nonseparable wavelet transformation have been achieved with M = 4 levels producing nine subbands. Equal value weights are used in the calculation of MW-MSE (11). Matlab implementation of MW-PSNR is available online [55].…”

Section: Analysis Of Mw-psnr Performancesmentioning

confidence: 99%

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DIBR-synthesized image quality assessment based on morphological multi-scale approach

Sandic-Stankovic

Kukolj

Callet

2016

J Image Video Proc.

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The depth-image-based rendering (DIBR) algorithms used for 3D video applications introduce new types of artifacts mostly located around the disoccluded regions. As the DIBR algorithms involve geometric transformations, most of them introduce non-uniform geometric distortions affecting the edge coherency in the synthesized images. Such distortions are not handled efficiently by the common image quality assessment metrics which are primarily designed for other types of distortions. In order to better deal with specific geometric distortions in the DIBR-synthesized images, we propose a full-reference metric based on multi-scale image decomposition applying morphological filters. Using non-linear morphological filters in multi-scale image decomposition, important geometric information such as edges is maintained across different resolution levels. Edge distortion between the multi-scale representation subbands of the reference image and the DIBR-synthesized image is measured precisely using mean squared error. In this way, areas around edges that are prone to synthesis artifacts are emphasized in the metric score. Two versions of morphological multiscale metric have been explored: (a) Morphological Pyramid Peak Signal-to-Noise Ratio metric (MP-PSNR) based on morphological pyramid decomposition, and (b) Morphological Wavelet Peak Signal-to-Noise Ratio metric (MW-PSNR) based on morphological wavelet decomposition. The performances of the proposed metrics have been tested using two databases which contain DIBR-synthesized images: the IRCCyN/IVC DIBR image database and MCL-3D stereoscopic image database. Proposed metrics achieve significantly higher correlation with human judgment compared to the state-of-the-art image quality metrics and compared to the tested metric dedicated to synthesisrelated artifacts. The proposed metrics are computationally efficient given that the morphological operators involve only integer numbers and simple computations like min, max, and sum as well as simple calculation of MSE. MP-PSNR has slightly better performances than MW-PSNR. It has very good agreement with human judgment, Pearson's 0.894, Spearman 0.77 when it is tested on the MCL-3D stereoscopic image database. We have demonstrated that PSNR has particularly good agreement with human judgment when it is calculated between images at higher scales of morphological multi-scale representations. Consequently, simplified and in essence reduced versions of multi-scale metrics are proposed, taking into account only detailed images at higher decomposition scales. The reduced version of MP-PSNR has very good agreement with human judgment, Pearson's 0.904, Spearman 0.863 using IRCCyN/IVC DIBR image database.

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Section: Quality Assessment Of Dibr-synthesized Viewmentioning

confidence: 83%

Section: Distortion In the Dibr-synthesized Viewmentioning

confidence: 99%

Section: Quality Assessment Of Dibr-synthesized Viewmentioning

confidence: 99%

Section: The Calculation Of Mw-psnrmentioning

confidence: 99%

Section: Analysis Of Mw-psnr Performancesmentioning

confidence: 99%

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