Image Quality Assessment for DIBR Synthesized Views using Elastic Metric

Ling, Suiyi; Callet, Patrick Le

doi:10.1145/3123266.3123329

Cited by 17 publications

(20 citation statements)

References 16 publications

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“…Targeting the problem that global shifting artifacts are generally over-penalized by point-wise metrics, CT-IQM [11] was proposed using an encoding scheme based on the context tree. To quantify the deformation of curves in synthesized views, an elastic metric based EM-IQM is proposed in [12]. Li et al [13] proposed LOGs by considering both the geometric distortions as well as the sharpness of the images.…”

Section: Related Workmentioning

confidence: 99%

“…The overall performance results are shown in [7] 0.6864 0.4842 0.6125 MP-PSNRr [10] 0.6954 0.4784 0.6606 MW-PSNRr [10] 0.6637 0.4921 0.6293 CT-IQM [11] 0.6809 0.6626 0.4877 BF-M [1] 0.6980 0.5885 0.4768 EM-IQM [12] 0.7430 0.6726 0.4455 ST-IQM [22] 0.8217 0.7710 0.3929 LoGs [13] 0.8256 0.7812 0.3601 Proposed 0.9023 0.8448 0.2870 NO Reference Metric (NR) NIQSV [14] 0.6346 0.5146 0.6167 NIQSV+ [15] 0.7114 0.4679 0.6668 APT [16] 0.7307 0.7140 0.4622 CSC-NRM [25] 0.8302 0.7827 0.3233…”

Section: Overall Performancementioning

confidence: 99%

See 1 more Smart Citation

Perceptual Representations of Structural Information in Images: Application to Quality Assessment of Synthesized View in FTV Scenario

Ling¹,

Li²,

Callet³

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

Self Cite

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As the immersive multimedia techniques like Free-viewpoint TV (FTV) develop at an astonishing rate, user's demand for high-quality immersive contents increases dramatically. Unlike traditional uniform artifacts, the distortions within immersive contents could be non-uniform structure-related and thus are challenging for commonly used quality metrics. Recent studies have demonstrated that the representation of visual features can be extracted from multiple levels of the hierarchy. Inspired by the hierarchical representation mechanism in the human visual system (HVS), in this paper, we explore to adopt structural representations to quantitatively measure the impact of such structure-related distortion on perceived quality in FTV scenario. More specifically, a bio-inspired full reference image quality metric is proposed based on 1) lowlevel contour descriptor; 2) mid-level contour category descriptor; and 3) task-oriented non-natural structure descriptor. The experimental results show that the proposed model outperforms significantly the state-of-the-art metrics.

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Section: Related Workmentioning

confidence: 99%

Section: Overall Performancementioning

confidence: 99%

Perceptual Representations of Structural Information in Images: Application to Quality Assessment of Synthesized View in FTV Scenario

Ling¹,

Li²,

Callet³

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

Self Cite

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“…4) CT-IQA: a variable-length context tree based image quality assessment [7] proposed by Ling et al aims to quantify the overall structure dissimilarity and dissimilarities in various contour characteristics. 5) EM-IQA: In [8], Ling et al also proposed an elastic metric based image quality assessment metric by quantifying the deformation of curves in the local distortion regions.…”

Section: ) Mw-psnr Mp-psnr: Morphological Wavelet Peakmentioning

confidence: 99%

SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views

Tian

Zhang

Morin

et al. 2018

2018 IEEE Visual Communications and Image Processing (VCIP)

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Depth-image-based-rendering (DIBR) has been used to generate the virtual views for Multi-view videos and Freeviewpoint videos. However, the quality assessment of DIBRsynthesized views is very challenging owing to the new types of distortions induced by inaccurate depth maps, dis-occlusions and image inpainting methods. There exist a large number of object shifts and geometric distortions in the synthesized view which the traditional 2D quality metrics may fail to assess. In this paper, we propose a shift compensation based image quality assessment metric (SC-IQA) for DIBR-synthesized views. Firstly, the global geometric shift is compensated roughly by an SURF + RANSAC homography approach. Then, a multi-resolution block matching method, which performs a more accurate matching, is used to precisely compensate the shift and penalize the local geometric distortion as well. In addition, a visual saliency map is also used as a weighting function. To calculate the final overall quality scores, only the worst blocks are utilized since the biggest distortions have the most effects on the overall perceptual quality. The results show that the proposed metric significantly outperforms the state-of-the-art synthesized view dedicated metrics and the conventional 2D IQA metrics.

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“…With this concern, some researchers have proposed IQA metrics targeting 3D synthesized images. These methods are mainly divided into two categories, full-reference (FR) [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] and no-reference (NR) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…Battisti et al statistically analyzed the shift artifacts in the Haar wavelet sub-bands, and proposed a 3D synthesized view image quality metric (3DSwIM) [ 17 ]. Ling and Le Callet proposed a sketch-token-based synthesized IQA (ST-SIQA) metric [ 18 ] and elastic metric based IQA (EM-IQA) metric [ 19 ]. Both ST-SIQA and EM-IQA analyzed shift artifacts by calculating contour similarity between the reference and synthesized images.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Jin

Peng

Zou

et al. 2021

Entropy

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Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.

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Image Quality Assessment for DIBR Synthesized Views using Elastic Metric

Cited by 17 publications

References 16 publications

Perceptual Representations of Structural Information in Images: Application to Quality Assessment of Synthesized View in FTV Scenario

Perceptual Representations of Structural Information in Images: Application to Quality Assessment of Synthesized View in FTV Scenario

SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

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