scholarly journals A Layered Approach for Quality Assessment of DIBR-Synthesized Images

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Rafia Mansoor ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Asma Maqsood
Keyword(s):  
Quality Assessment ◽  
Color Image ◽  
Three Dimensional ◽  
Depth Map ◽  
Poor Quality ◽  
Depth Image ◽  
Multiview Video ◽  
3D Scene ◽  
2D And 3D

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.

scholarly journals Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation

2021 ◽  
Vol 11 (6) ◽  
pp. 2666
Author(s):  
Hafiz Muhammad Usama Hassan Alvi ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Quality Assessment ◽  
Mixed Reality ◽  
Depth Estimation ◽  
Poor Quality ◽  
Depth Image ◽  
Depth Maps ◽  
Structural Distortions ◽  
Image Quality Metric ◽  
Technological Constraints

Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.

scholarly journals View-Volume Network for Semantic Scene Completion from a Single Depth Image

2018 ◽  
Author(s):  
Yuxiao Guo ◽  
Xin Tong
Keyword(s):  
Computational Cost ◽  
Depth Map ◽  
Depth Image ◽  
Context Information ◽  
Efficiency And Effectiveness ◽  
Semantic Scene ◽  
3D Scene ◽  
3D Volume ◽  
2D And 3D ◽  
3D Representations

We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image. Our method extracts the detailed geometric features from the input depth image with a 2D view CNN and then projects the features into a 3D volume according to the input depth map via a projection layer. After that, we learn the 3D context information of the scene with a 3D volume CNN for computing the result volumetric occupancy and semantic labels. With combined 2D and 3D representations, the VVNet efficiently reduces the computational cost, enables feature extraction from multi-channel high resolution inputs, and thus significantly improve the result accuracy. We validate our method and demonstrate its efficiency and effectiveness on both synthetic SUNCG and real NYU dataset. 

scholarly journals Rendering Distortion Estimation Model for 3D High Efficiency Depth Coding

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Qiuwen Zhang ◽  
Liang Tian ◽  
Lixun Huang ◽  
Xiaobing Wang ◽  
Haodong Zhu
Keyword(s):  
High Efficiency ◽  
Three Dimensional ◽  
Rate Distortion ◽  
Depth Map ◽  
Depth Image ◽  
High Efficiency Video Coding ◽  
Estimation Model ◽  
Depth Image Based Rendering ◽  
Distortion Estimation ◽  
3D Scene

A depth map represents three-dimensional (3D) scene geometry information and is used for depth image based rendering (DIBR) to synthesize arbitrary virtual views. Since the depth map is only used to synthesize virtual views and is not displayed directly, the depth map needs to be compressed in a certain way that can minimize distortions in the rendered views. In this paper, a modified distortion estimation model is proposed based on view rendering distortion instead of depth map distortion itself and can be applied to the high efficiency video coding (HEVC) rate distortion cost function process for rendering view quality optimization. Experimental results on various 3D video sequences show that the proposed algorithm provides about 31% BD-rate savings in comparison with HEVC simulcast and 1.3 dB BD-PSNR coding gain for the rendered view.

scholarly journals Quality assessment of nutrition coverage in the media: a 6-week survey of five popular UK newspapers

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e014633 ◽  
Author(s):  
Alice R Kininmonth ◽  
Nafeesa Jamil ◽  
Nasser Almatrouk ◽  
Charlotte E L Evans
Keyword(s):  
Health Outcome ◽  
Quality Assessment ◽  
Assessment Tool ◽  
Poor Quality ◽  
Quality Score ◽  
Food Type ◽  
The Public ◽  
Day Of The Week ◽  
Article Quality

ObjectivesTo investigate the quality of nutrition articles in popular national daily newspapers in the UK and to identify important predictors of article quality.SettingNewspapers are a primary source of nutrition information for the public.DesignNewspaper articles were collected on 6 days of the week (excluding Sunday) for 6 weeks in summer 2014. Predictors included food type and health outcome, size of article, whether the journalist was named and day of the week.Outcome measuresA validated quality assessment tool was used to assess each article, with a minimum possible score of −12 and a maximum score of 17. Newspapers were checked in duplicate for relevant articles. The association of each predictor on article quality score was analysed adjusting for remaining predictors. A logistic regression model was implemented with quality score as the binary outcome, categorised as poor (score less than zero) or satisfactory (score of zero or more).ResultsOver 6 weeks, 141 nutrition articles were included across the five newspapers. The median quality score was 2 (IQR −2–6), and 44 (31%) articles were poor quality. There was no substantial variation in quality of reporting between newspapers once other factors such as anonymous publishing, health outcome, aspect of diet covered and day of the week were taken into account. Particularly low-quality scores were obtained for anonymously published articles with no named journalist, articles that focused on obesity and articles that reported on high fat and processed foods.ConclusionsThe general public are regularly exposed to poor quality information in newspapers about what to eat to promote health, particularly articles reporting on obesity. Journalists, researchers, university press officers and scientific journals need to work together more closely to ensure clear, consistent nutrition messages are communicated to the public in an engaging way.

scholarly journals No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

2019 ◽  
Vol 11 (10) ◽  
pp. 204 ◽  
Author(s):  
Dogan ◽  
Haddad ◽  
Ekmekcioglu ◽  
Kondoz
Keyword(s):  
Quality Evaluation ◽  
Evaluation Model ◽  
Depth Map ◽  
Depth Image ◽  
Depth Maps ◽  
Model Based ◽  
Objective Quality ◽  
Quality Evaluation Model ◽  
Confidence Measurement

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.

scholarly journals Free-viewpoint image synthesis using superpixel segmentation

2017 ◽  
Vol 6 ◽  
Author(s):  
Mehrdad Panahpour Tehrani ◽  
Tomoyuki Tezuka ◽  
Kazuyoshi Suzuki ◽  
Keita Takahashi ◽  
Toshiaki Fujii
Keyword(s):  
Three Dimensional ◽  
Image Synthesis ◽  
Physical Distance ◽  
Depth Image ◽  
Reference Image ◽  
Sampling Density ◽  
Superpixel Segmentation ◽  
Limited Sampling ◽  
Reference Images

A free-viewpoint image can be synthesized using color and depth maps of reference viewpoints, via depth-image-based rendering (DIBR). In this process, three-dimensional (3D) warping is generally used. A 3D warped image consists of disocclusion holes with missing pixels that correspond to occluded regions in the reference images, and non-disocclusion holes due to limited sampling density of the reference images. The non-disocclusion holes are those among scattered pixels of a same region or object. These holes are larger when the reference viewpoints and the free viewpoint images have a larger physical distance. Filling these holes has a crucial impact on the quality of free-viewpoint image. In this paper, we focus on free-viewpoint image synthesis that is precisely capable of filling the non-disocclusion holes caused by limited sampling density, using superpixel segmentation. In this approach, we proposed two criteria for segmenting depth and color data of each reference viewpoint. By these criteria, we can detect which neighboring pixels should be connected or kept isolated in each references image, before being warped. Polygons enclosed by the connected pixels, i.e. superpixel, are inpainted by k-means interpolation. Our superpixel approach has a high accuracy since we use both color and depth data to detect superpixels at the location of the reference viewpoint. Therefore, once a reference image that consists of superpixels is 3D warped to a virtual viewpoint, the non-disocclusion holes are significantly reduced. Experimental results verify the advantage of our approach and demonstrate high quality of synthesized image when the virtual viewpoint is physically far from the reference viewpoints.

scholarly journals Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain

2021 ◽  
Vol 15 ◽  
Author(s):  
Xinglong Wu ◽  
Yuhang Tao ◽  
Guangzhi He ◽  
Dun Liu ◽  
Meiling Fan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mouse Brain ◽  
Light And Electron Microscopy ◽  
Three Dimensional ◽  
Image Data ◽  
Semantic Segmentation ◽  
Poor Quality ◽  
Deep Convolutional Neural Networks ◽  
Segmentation Task

Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to achieve efficient and accurate three-dimensional reconstruction of the vasculature and neural networks in the brain. The success of these tasks heavily depends on the amount, and especially the quality, of the human-annotated labels fed into DCNNs. However, it is often difficult to acquire the gold standard of human-annotated labels for dense nerve tissues; human annotations inevitably contain discrepancies or even errors, which substantially impact the performance of DCNNs. Thus, a novel boosting framework consisting of a DCNN for multilabel semantic segmentation with a customized Dice-logarithmic loss function, a fusion module combining the annotated labels and the corresponding predictions from the DCNN, and a boosting algorithm to sequentially update the sample weights during network training iterations was proposed to systematically improve the quality of the annotated labels; this framework eventually resulted in improved segmentation task performance. The microoptical sectioning tomography (MOST) dataset was then employed to assess the effectiveness of the proposed framework. The result indicated that the framework, even trained with a dataset including some poor-quality human-annotated labels, achieved state-of-the-art performance in the segmentation of somata and vessels in the mouse brain. Thus, the proposed technique of artificial intelligence could advance neuroscience research.

scholarly journals Quality Assessment of Milling Pavement Surface Using 3D Line Laser Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Bing Hui ◽  
Mu Guo ◽  
Xiaofang Liu
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Poor Quality ◽  
Surface Model ◽  
Premature Failure ◽  
Quality Work ◽  
Milled Surface ◽  
Surface Assessment

To ensure that a regular milled surface texture provides good bonding without residual distress, a new specification of milling surface assessment has been established for quantitatively evaluating the milled surface quality. This research explores the possibility of using three-dimensional (3D) laser scanning technology to develop an algorithm to obtain a milled surface model that can measure evaluating indicators, milling depth and texture depth, and identify poorly milled areas. A case study was conducted by using a laser scanning vehicular system to collect 3D continuous pavement transverse profiles data in a 500 m long segment of Highway S107. The results show that the proposed method is very promising and can measure the milling depth and texture depth to effectively and quantitatively differentiate between good- (milling depth between 47 mm and 53 mm and texture depth exceeding 2 mm) and poor-quality work. Moreover, the poorly milled areas such as those with residual distress and unmilled areas that will lead to premature failure can also be identified using the proposed method. The proposed method can effectively support remilling work and ensure the quality of the overlay pavement.

scholarly journals ANALYSIS AND ENHANCEMENT OF THE DENOISING DEPTH DATA USING KINECT THROUGH ITERATIVE TECHNIQUE

2016 ◽  
Vol 78 (9) ◽  
Author(s):  
Mostafa Karbasi ◽  
Sara Bilal ◽  
Reza Aghababaeyan ◽  
Abdolvahab Ehsani Rad ◽  
Zeeshan Bhatti ◽  
...  
Keyword(s):  
Background Noise ◽  
Depth Map ◽  
Depth Image ◽  
Shadow Removal ◽  
3D Image ◽  
High Quality ◽  
Depth Data ◽  
Different Types ◽  
Accuracy And Stability

Since the release of Kinect by Microsoft, the, accuracy and stability of Kinect data-such as depth map, has been essential and important element of research and data analysis. In order to develop efficient means of analyzing and using the kinnect data, researchers require high quality of depth data during the preprocessing step, which is very crucial for accurate results. One of the most important concerns of researchers is to eliminate image noise and convert image and video to the best quality. In this paper, different types of the noise for Kinect are analyzed and a unique technique is used, to reduce the background noise based on distance between Kinect devise and the user. Whereas, for shadow removal, the iterative method is used to eliminate the shadow casted by the Kinect. A 3D depth image is obtained as a result with good quality and accuracy. Further, the results of this present study reveal that the image background is eliminated completely and the 3D image quality in depth map has been enhanced.

scholarly journals A single-model quality assessment method for poor quality protein structure

2020 ◽  
Author(s):  
Jianquan Ouyang ◽  
Ningqiao Huang ◽  
Yunqi Jiang
Keyword(s):  
Protein Structure ◽  
Quality Assessment ◽  
Structure Prediction ◽  
Assessment Method ◽  
Poor Quality ◽  
Single Model ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Quality Assessment Method

Abstract Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool. In this work, we introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

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