Title :
A Novel No-Reference Stereoscopic Image Quality Assessment Method
Author :
Shao, Feng ; Gu, Shanbo ; Gangyi Jang ; Yu, Mei
Author_Institution :
Fac. of Inf. Sci. & Eng., Ningbo Univ., Ningbo, China
Abstract :
No-reference stereoscopic image quality assessment (NR-SIQA) is an important research issue in three- dimensional researches. In this paper, a novel NR- SIQA method is proposed by analyzing the detail characteristic of the specific distortion type, and distortion-specific features are extracted to describe the distorted stereoscopic image. The relationship between stereoscopic features and subjective score is established by using support vector regression (SVR). Experimental results show that compared with the full-reference structural similarity image quality (SSIM) assessment method, the proposed method is more effective in quantifying image quality.
Keywords :
feature extraction; regression analysis; stereo image processing; support vector machines; visual perception; NR-SIQA method; SSIM; SVR; feature extraction; image quality quantification; no-reference stereoscopic image quality assessment method; stereoscopic image distortion; structural similarity image quality; support vector regression; Databases; Image coding; Image quality; Stereo image processing; Training; Transform coding; White noise;
Conference_Titel :
Photonics and Optoelectronics (SOPO), 2012 Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4577-0909-8
DOI :
10.1109/SOPO.2012.6271061