'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
We propose the first method to predict the Satisfied User Ratio
(SUR) for compressed stereo images. The method consists
of two main steps. First, considering binocular vision
properties, we extract three types of features from stereo images:
image quality features, monocular visual features, and
binocular visual features. Then, we train a Support Vector Regression
(SVR) model to learn a mapping function from the
feature space to the SUR values. Experimental results on the
SIAT-JSSI dataset show excellent prediction accuracy, with a
mean absolute SUR error of only 0.08 for H.265 intra coding
and only 0.13 for JPEG2000 compression
