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

Farid, Muhammad Shahid; Lucenteforte, Maurizio; Grangetto, Marco

doi:10.1016/j.inffus.2017.11.007

Cited by 23 publications

(9 citation statements)

References 64 publications

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“…Speckesser et al [ 24 ] proposed a smart classroom learning media library based on mobile technology. Farid et al [ 25 ] analyzed relevant theoretical and practical questions from three aspects: the concept and application of information technology education. Specifically, they mainly answered basic questions such as what is a smart classroom, how to organize and construct it, and how to apply it to the classroom.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges

Shang¹

2022

Computational Intelligence and Neuroscience

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College English classroom teaching evaluation is one of the key issues discussed by all schools at present. First of all, teachers and students are highly concerned about English learning in the compulsory course of English examination, but there are also many problems. The distance network teaching with computer technology as the main body develops rapidly, a large number of video and audio teaching resources network through the network transmission to present in front of learners, through the network transmission of video and audio aided, expand the audience of network teaching, is conducive to the realization of digitalization, information, lifelong, new education goals. Firstly, this article summarizes the problems of poor effect, uneven performance, and mismatch between evaluation and teaching in college English at present. Then, based on the previous works, the author designed a machine learning-based Internet of things technology and verifies its feasibility. The data of teaching experiments show that the writing performance of the students in the lower section of the experimental class is better than that of the students in the middle and high sections. Among them, significant progresses have been made in grammar, length, expression, and structure, which has optimized students’ preclass preview efficiency, autonomous learning motivation, quality of homework completion, and afterclass reflection behavior. Finally, the author summarized the shortcomings of this research and puts forward the prospect of relevant research, in order to provide reference value for the national college English blended teaching and promotes the efficient implementation and vigorous development of college English teaching. In the future, the evaluation index system under the pressure of performance can be further studied.

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Section: Related Workmentioning

confidence: 99%

Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges

Shang¹

2022

Computational Intelligence and Neuroscience

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“…It takes an original image and generates a virtual image which is used to assess the distortion that is created during the process of DIBR. A similar research [27] proposed an algorithm which was a combination of two metrics; one is used to assess the quality of synthesized images, and the second metric is responsible to assess the quality of depth maps. The proposed SIQM metric utilizes the phenomenon of cyclopean eye theory.…”

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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“…Yue et al [5] designed an NR synthesised IQA method in which the specific distortions have been taken into consideration. Farid et al [32] designed an NR synthesised IQA method by considering the influence of side‐views information and the distortion of the depth map. Zhou et al [33] proposed a synthesised IQA method for the flicker distortion by calculating the gradient distance between two adjacent frames.…”

Section: Related Workmentioning

confidence: 99%

Blind quality assessment for 3D synthesised video with binocular asymmetric distortion

et al. 2020

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During the process of watching 3D synthesised video (3D‐SV) and switching viewpoints, there is a case of asymmetric distortion, the left(right) viewpoint is a synthesised video generated by rendering technique, and the right(left) viewpoint is a real video taken by the camera. How to accurately estimate the quality of 3D‐SV with binocular asymmetric distortions is a new and challenging problem. Aiming at this problem, a blind quality assessment method for 3D‐SV with binocular asymmetric distortions is proposed. Firstly, the local edge deformations of synthesised videos at different scales are measured by calculating their standard deviations. Secondly, the global naturalness of synthesised videos is computed by analysing their natural statistical characteristics. Thirdly, a strategy for fusing left and right quality scores is proposed, which considers their texture information in different directions. Finally, the random forest is used to obtain an objective quality score. The experimental results show the superiority of the proposed method on asymmetry 3D‐SV database.

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

Cited by 23 publications

References 64 publications

Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges

Application of Machine Learning and Internet of Things Techniques in Evaluation of English Teaching Effect in Colleges

A Layered Approach for Quality Assessment of DIBR‐Synthesized Images

Blind quality assessment for 3D synthesised video with binocular asymmetric distortion

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