Repository landing page

We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.

Satisfied user ratio prediction with support vector regression for compressed stereo images

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

Similar works

Loading suggested articles...

Full text

thumbnail-image

De Montfort University Open Research Archive

redirect
Last time updated on 21/05/2020
Leaflet Β© OpenStreetMap contributors

This paper was published in De Montfort University Open Research Archive.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.

We use cookies to improve our website.

Learn more