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No-reference quality assessment for synthesized images based on local geometric distortions
Author(s): Xiaoyan Ma; Fen Chen; Wenhui Zou; Zongju Peng
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Paper Abstract

Depth-image-based-rendering (DIBR) techniques are significant for view synthesis. However, such technique may introduce challenging distortions. Unlike traditional uniform artifacts, the distortions of the synthesized could be local and non-uniform, thus are challenging for traditional image quality assessment metrics. To tackle this problem, aiming at the geometric distortions, a no reference quality assessment for DIBR-synthesized images is proposed in this paper. First, considering that the hue distribution of disoccluded regions is different from that of the natural image, the disoccluded regions are extracted from the hue difference map. The disoccluded regions with different sizes are calculated adaptively by overlapping the hue difference map according to the distortion intensity based on the progressive layer partitioning principle. Second, the artifacts of edges are measured as the distance between the patches at critical regions and their down-sampled versions based on the property of scale invariance. Finally, the perceptual quality is estimate by linearly pooling the scores of two geometric distortions together. The experimental results show that the PLCC, SRCC, RMSE of the proposed model are 0.7613, 0.6965, and 0.4244, respectively. In summary, the proposed metric achieves higher performance, but lower computational complexity than other models.

Paper Details

Date Published: 18 November 2019
PDF: 6 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111870Q (18 November 2019); doi: 10.1117/12.2538875
Show Author Affiliations
Xiaoyan Ma, Ningbo Univ. (China)
Fen Chen, Ningbo Univ. (China)
Chongqing Univ. of Technology (China)
Wenhui Zou, Ningbo Univ. (China)
Zongju Peng, Ningbo Univ. (China)
Chongqing Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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