Three-dimensional visual comfort assessment via preference learning

Abstract

Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/13/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Abstract. Three-dimensional (3-D) visual comfort assessment (VCA) is a particularly important and challenging topic, which involves automatically predicting the degree of visual comfort in line with human subjective judgment. State-of-the-art VCA models typically focus on minimizing the distance between predicted visual comfort scores and subjective mean opinion scores (MOSs) by training a regression model. However, obtaining precise MOSs is often expensive and time-consuming, which greatly constrains the extension of existing MOS-aware VCA models. This study is inspired by the fact that humans tend to conduct a preference judgment between two stereoscopic images in terms of visual comfort. We propose to train a robust VCA model on a set of preference labels instead of MOSs. The preference label, representing the relative visual comfort of preference stereoscopic image pairs (PSIPs), is generally precise and can be obtained at much lower cost compared with MOS. More specifically, some representative stereoscopic images are first selected to generate the PSIP training set. Then, we use a support vector machine to learn a preference classification model by taking a differential feature vector and the corresponding preference label of each PSIP as input. Finally, given a testing sample, by considering a full-round paired comparison with all the selected representative stereoscopic images, the visual comfort score can be estimated via a simple linear mapping strategy. Experimental results on our newly built 3-D image database demonstrate that the proposed method can achieve a better performance compared with the models trained on MOSs.

Exemplar-based image completion using image depth information

“…Some primary methods [24], [25], [26] exist to measure the quality of the result. The first is visual inspection; the second is peak signal to noise ratio (PSNR).…”

Pairwise Learning to Rank for Image Quality Assessment

“…The subjective evaluation experiment on the TID2013 database involves 971 participants from five different countries, namely, Finland, France, Italy, Ukraine, and the United States. The subjective evaluation score MOS of each image is within the range of [0,9].…”

“…The MOS is obtained from 838 ratings conducted by observers. The subjective evaluation score MOS of each image is within the range of [0,9].…”

“…However, people are good at comparing two images and choosing the one with a better or worse quality. The pairwise comparison was used in [9], [10] to train visual comfort assessment (VCA) models. The VCA is proposed to predict the effects induced by stereo image content on visual health.…”

Extraction and investigation of biosignal features for visual discomfort evaluation

“…Jiang et al (2017a,b), in their work, introduced disparity features, such as magnitude, contrast, dispersion, skewness, and also combined with the oscillatory activity of the middle temporal area, for the assessment of visual discomfort. Performance of their method was evaluated on NBU S3D-VCA (Jiang et al 2015) and IVY LAB 3D (Jung et al 2013) stereoscopic image databases.…”