No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis
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 diffe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/6/770 |
| _version_ | 1797529712696754176 |
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| author | Chongchong Jin Zongju Peng Wenhui Zou Fen Chen Gangyi Jiang Mei Yu |
| author_facet | Chongchong Jin Zongju Peng Wenhui Zou Fen Chen Gangyi Jiang Mei Yu |
| author_sort | Chongchong Jin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:17:43Z |
| format | Article |
| id | doaj.art-8f63cd7da9ac43d28cf59814360bdda1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T10:17:43Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-8f63cd7da9ac43d28cf59814360bdda12023-11-22T00:42:19ZengMDPI AGEntropy1099-43002021-06-0123677010.3390/e23060770No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features AnalysisChongchong Jin0Zongju Peng1Wenhui Zou2Fen Chen3Gangyi Jiang4Mei Yu5Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaFaculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaFaculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaFaculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaFaculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaFaculty of Information Science and Engineering, Ningbo University, Ningbo 315211, ChinaMultiview 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.https://www.mdpi.com/1099-4300/23/6/7703D synthesized imagesimage quality assessment (IQA)no-referencevisual-entropy-guidedmulti-layer features analysis |
| spellingShingle | Chongchong Jin Zongju Peng Wenhui Zou Fen Chen Gangyi Jiang Mei Yu No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis Entropy 3D synthesized images image quality assessment (IQA) no-reference visual-entropy-guided multi-layer features analysis |
| title | No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis |
| title_full | No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis |
| title_fullStr | No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis |
| title_full_unstemmed | No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis |
| title_short | No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis |
| title_sort | no reference quality assessment for 3d synthesized images based on visual entropy guided multi layer features analysis |
| topic | 3D synthesized images image quality assessment (IQA) no-reference visual-entropy-guided multi-layer features analysis |
| url | https://www.mdpi.com/1099-4300/23/6/770 |
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