Image quality assessment for free viewpoint video based on mid-level contours feature

Ling, Suiyi; Callet, Patrick Le

doi:10.1109/icme.2017.8019431

Cited by 24 publications

(23 citation statements)

References 16 publications

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“…Considering the fact that (1) content related trajectory is able to stress the system; (2) content is related to structure; (3) geometric distortions are the most annoying degradations that interrupt structure introduced by view synthesis, the main idea of the proposed method is to assess the quality of the navigation videos by quantifying to what extent the classes of contours change due to view synthesis, compression and transition among views. It is an extended version of our previous work [54] (a quality measure for image) to cope with the FVV scenario. In this version, the complex registration stage is replaced by local regions selection, and a ST-based temporal estimator is incorporated to quantify temporal artifacts.…”

Section: Video Quality Measure For Free Viewpoint Videosmentioning

confidence: 99%

Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Ling

Gutiérrez

et al. 2019

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Free-Viewpoint Video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artifacts influencing the perceived quality. To assess this influence, quality evaluation studies are often carried out using conventional displays and generating predefined navigation trajectories mimicking the possible movement of the viewers when exploring the content. Nevertheless, as different trajectories may lead to different conclusions in terms of visual quality when benchmarking the performance of the systems, methods to identify critical trajectories are needed. This paper aims at exploring the impact of exploration trajectories (defined as Hypothetical Rendering Trajectories: HRT) on perceived quality of FVV subjectively and objectively, providing two main contributions. Firstly, a subjective assessment test including different HRTs was carried out and analyzed. The results demonstrate and quantify the influence of HRT in the perceived quality. Secondly, we propose a new objective video quality assessment measure to objectively predict the impact of HRT. This measure, based on Sketch-Token representation, models how the categories of the contours change spatially and temporally from a higher semantic level. Performance in comparison with existing quality metrics for FVV, highlight promising results for automatic detection of most critical HRTs for the benchmark of immersive systems.

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Section: Video Quality Measure For Free Viewpoint Videosmentioning

confidence: 99%

Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Ling

Gutiérrez

et al. 2019

IEEE J. Emerg. Sel. Topics Circuits Syst.

Self Cite

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“…A higher semantic and efficient representation of low-level structural elements, i.e., mid-level representation is thus defined, which are the categories of the contours. Based on this assumption, a Sketch-Token based Image Quality Metric (ST-IQM) by checking how the categories of contours change due to structural distortions [22] is employed as the mid-level structure representation based estimator in our study (also employed in [23] as mid-level descriptor), where contours are first 'encoded' as a vector f ST of contour categories likelihood values. The mid-level estimator is calculated as the Minkowski summation of the errors computed based on the mid-level descriptor across the entire image:…”

Section: Mid-level Structure Representation Based Estimatormentioning

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%

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)

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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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“…De Silva et al [25] proposed a learning based Stereoscopic Structural Distortion (StSD) metric to evaluate the quality of 3D videos. The quality assessment algorithm proposed in [35] estimates the quality of the synthesized image by measuring how classes of image contours change due to synthesis process. The no-reference synthesized image quality metric proposed in [36] exploits simple morphological operators to predict the quality of the virtual image.…”

Section: Related Workmentioning

confidence: 99%

“…As described earlier, in evaluating the quality of synthesized images the proposed 3D-IQA metric does not require the corresponding reference images hence, it is not a full-reference IQA metric. For performance evaluation, we compared SIQM with the widely used 3D-IQA metrics: 3DSwIM [34], You l (local model) [32], You g (global model) [32], StSD [25], Benoit [37], Gorley [29], ST-SIAQ [35], and NIQSV [36]. In this set of compared methods, NIQSV is a no-reference technique while the rest are full-reference quality metrics.…”

Section: Performance Evaluation Parametersmentioning

confidence: 99%

Evaluating virtual image quality using the side-views information fusion and depth maps

2018

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Three Dimensional (3D) image quality assessment is a challenging problem as compared to 2D images due to their different nature of acquisition, representation, coding, and display. The additional dimension of depth in multiview video plus depth (MVD) format is exploited to obtain images at novel intermediate viewpoints using depth image based rendering (DIBR) techniques, enabling 3D television and free-viewpoint television (FTV) applications. Depth maps introduce various quality artifacts in the DIBR-synthesized (virtual) images. In this paper, we propose a novel methodology to evaluate the quality of synthesized views in absence of the corresponding original reference views. It computes the statistical characteristics of the side views from whom the virtual view is generated, and fuses this information to estimate the statistical characteristics of the cyclopean image which are compared to those of the synthesized image to evaluate its quality. In addition to texture images, the proposed algorithm also considers the depth maps in evaluating the quality of the synthesized images. The algorithm blends two quality metrics, one estimating the texture distortion in the synthesized texture image induced by compression, transmission, 3D warping, or other causes and the second one determining the distortion of the depth maps. The two metrics are combined to obtain an overall quality assessment of the synthesized image. The proposed Synthesized Image Quality Metric (SIQM) is tested on the challenging MCL-3D and SIAT-3D datasets. The evaluation results show that the proposed metric significantly improves over state-of-the-art 3D image quality assessment algorithms.

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Image quality assessment for free viewpoint video based on mid-level contours feature

Cited by 24 publications

References 16 publications

Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

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

Evaluating virtual image quality using the side-views information fusion and depth maps

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