Quality assessment of DIBR-synthesized views: An overview

Tian, Shishun; Zhang, Lu; Zou, Wenbin; Li, Xia; Su, Ting; Morin, Luce; Déforges, Olivier

doi:10.1016/j.neucom.2020.09.062

Cited by 20 publications

(7 citation statements)

References 117 publications

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“…In this Section, we highlight some QA models designed for DIBR-synthesized images based on multi-resolution and/or multi-scale image representations in order to mimic the multiresolution and multi-scale character of HVS. More complete overview of quality models designed for QA of DIBRsynthesized view is presented at [65].…”

Section: Related Workmentioning

confidence: 99%

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Sandic-Stankovic¹,

Kukolj

Callet

2022

IEEE Trans. on Image Process.

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Images synthesized using depth-imagebasedrendering (DIBR) techniques may suffer from complex structural distortions. The goal of the primary visual cortex and other parts of brain is to reduce redundancies of input visual signal in order to discover the intrinsic image structure, and thus create sparse image representation. Human visual system (HVS) treats images on several scales and several levels of resolution when perceiving the visual scene. With an attempt to emulate the properties of HVS, we have designed the no-reference model for the quality assessment of DIBR-synthesized views. To extract a higher-order structure of high curvature which corresponds to distortion of shapes to which the HVS is highly sensitive, we define a morphological oriented Difference of Closings (DoC) operator and use it at multiple scales and resolutions. DoC operator nonlinearly removes redundancies and extracts fine grained details, texture of an image local structure and contrast to which HVS is highly sensitive. We introduce a new feature based on sparsity of DoC band. To extract perceptually important loworder structural information (edges), we use the non-oriented Difference of Gaussians (DoG) operator at different scales and resolutions. Measure of sparsity is calculated for DoG bands to get scalar features. To model the relationship between the extracted features and subjective scores, the general regression neural network (GRNN) is used. Quality predictions by the proposed DoC-DoG-GRNN model show higher compatibility with perceptual quality scores in comparison to the tested state-of-theart metrics when evaluated on four benchmark datasets with synthesized views, IRCCyN/IVC image/video dataset, MCL-3D stereoscopic image dataset and IST image dataset.

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Section: Related Workmentioning

confidence: 99%

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Sandic-Stankovic¹,

Kukolj

Callet

2022

IEEE Trans. on Image Process.

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“…There are various new IQAs based on deep features (Bosc et al 2011) first analyzed that the IQAs developed for natural images work poorly for 3D synthesized images and proposed a dataset named IRC-CyN/IVC dataset for this analysis of 84 images. After that, there are many different 3D-IQAs and datasets proposed for this task in the literature, such as IVY, IETR (Tian et al 2021). A summarized comparison of these three datasets is given in Table 1 with their drawbacks.…”

Section: Related Workmentioning

confidence: 99%

“…This subjective testing is also validated using Cohen's Kappa coefficient. The performance of five popular IQA metrics (LOGS (Tian et al 2021), Peak Signal to Noise Ratio(PSNR), SSIM (Wang et al 2004), LPIPS (Zhang et al 2018), APT (Tian et al 2021) ) oriented for natural as well as 3D images are given in Table 2. The comparison in Table 1, the value of correlation coefficients, and the error in Table 2 suggest that the literature has no proper algorithm for this purpose.…”

Section: Motivationmentioning

confidence: 99%

Do We Need a New Large-Scale Quality Assessment Database for Generative Inpainting Based 3D View Synthesis? (Student Abstract)

Sadbhawna

Jakhetiya

Subudhi

et al. 2022

AAAI

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The advancement in Image-to-Image translation techniques using generative Deep Learning-based approaches has shown promising results for the challenging task of inpainting-based 3D view synthesis. At the same time, even the current 3D view synthesis methods often create distorted structures or blurry textures inconsistent with surrounding areas. We analyzed the recently proposed algorithms for inpainting-based 3D view synthesis and observed that these algorithms no longer produce stretching and black holes. However, the existing databases such as IETR, IRCCyN, and IVY have 3D-generated views with these artifacts. This observation suggests that the existing 3D view synthesis quality assessment algorithms can not judge the quality of most recent 3D synthesized views. With this view, through this abstract, we analyze the need for a new large-scale database and a new perceptual quality metric oriented for 3D views using a test dataset.

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“…In literature, many computational algorithms based on stereoscopic and synthesized IQA have been documented [15]. Most of the early 3D-IQA algorithms are extended from 2D-IQA algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Layered Approach for Quality Assessment of DIBR‐Synthesized Images

Mansoor

Farid

Khan

et al. 2021

Wireless Communications and Mobile Computing

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Multiview video plus depth (MVD) is a popular video format that supports three-dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image-based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the synthesized image is crucial to provide an appreciable quality of experience (QoE) to the viewer. In a 3D scene, objects are at a different distance from the camera, characterized by their depth. In this paper, we investigate the effect that objects at a different distance make on the overall QoE. In particular, we find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera. The quality of each layer is individually computed, and their scores are pooled together to obtain a single quality score that represents the quality of the synthesized image. The performance of the proposed metric is evaluated on two benchmark DIBR image databases. The results show that the proposed metric is highly accurate and performs better than most existing 2D and 3D quality assessment algorithms.

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Quality assessment of DIBR-synthesized views: An overview

Cited by 20 publications

References 117 publications

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Quality Assessment of DIBR-Synthesized Views Based on Sparsity of Difference of Closings and Difference of Gaussians

Do We Need a New Large-Scale Quality Assessment Database for Generative Inpainting Based 3D View Synthesis? (Student Abstract)

A Layered Approach for Quality Assessment of DIBR‐Synthesized Images

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