Joint structure–texture sparse coding for quality prediction of stereoscopic images. Issue 24 (1st November 2015)

• Record Type: Journal Article
• Authors: Li, Kemeng; Shao, Feng; Jiang, Gangyi; Yu, Mei
• Is Part Of: Electronics letters. Volume 51:Issue 24(2015)
• Page Start: 1994
• Page End: 1995
• Publication Date: 2015-11-01
• Subjects: stereo image processing -- image coding -- image texture -- visual databases -- learning (artificial intelligence); joint structure‐texture sparse coding -- stereoscopic images -- perceptual quality prediction -- structure dictionary learning -- texture dictionary learning -- training database -- quality score -- left‐sparse feature similarity index -- right‐sparse feature similarity index -- 3D image‐quality assessment databases -- subjective assessment; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2015.2049
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : A quality prediction method for stereoscopic images is proposed based on joint structure–texture sparse coding. The goal is to predict the perceptual quality of a stereoscopic image by solving the joint structure–texture sparse coding problem. First, structure and texture dictionaries from a training database are learnt. Then, the quality score for a testing stereoscopic image is predicted by computing left and right sparse feature similarity indexes, respectively, and combining them together. Experimental results on two 3D image‐quality assessment databases demonstrate that the proposed method can achieve high consistent alignment with subjective assessment.