The University of Southampton
University of Southampton Institutional Repository

Artificial neural network visual model for image quality enhancement

Artificial neural network visual model for image quality enhancement
Artificial neural network visual model for image quality enhancement
An artificial neural network visual model is developed, which extracts multi-scale edge features from the decompressed image and uses these visual features as input to estimate and compensate for the coding distortions. This provides a generic postprocessing technique that can be applied to all the main coding methods. Experimental results involving post-processing the JPEG and quadtree coding systems show that the proposed artificial neural network visual model significantly enhances the quality of reconstructed images, both in terms of the objective peak signal to noise ratio and subjective visual assessment.
0925-2312
339-346
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
He, Z.
c7048a1b-3632-409a-96f5-e82b998d4754
Grant, P. M.
e527fff4-da0f-4bc4-91cf-eed522070300
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
He, Z.
c7048a1b-3632-409a-96f5-e82b998d4754
Grant, P. M.
e527fff4-da0f-4bc4-91cf-eed522070300

Chen, S., He, Z. and Grant, P. M. (2000) Artificial neural network visual model for image quality enhancement. Neurocomputing, 30 (1-4), 339-346.

Record type: Article

Abstract

An artificial neural network visual model is developed, which extracts multi-scale edge features from the decompressed image and uses these visual features as input to estimate and compensate for the coding distortions. This provides a generic postprocessing technique that can be applied to all the main coding methods. Experimental results involving post-processing the JPEG and quadtree coding systems show that the proposed artificial neural network visual model significantly enhances the quality of reconstructed images, both in terms of the objective peak signal to noise ratio and subjective visual assessment.

Text
NEUC2000-30 - Accepted Manuscript
Download (136kB)
Other
neulet.ps - Other
Download (987kB)

More information

Published date: January 2000
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251050
URI: http://eprints.soton.ac.uk/id/eprint/251050
ISSN: 0925-2312
PURE UUID: e7b784f3-8e6a-4fbe-9e0f-39d260c9c4dc

Catalogue record

Date deposited: 13 Sep 2000
Last modified: 14 Mar 2024 05:08

Export record

Contributors

Author: S. Chen
Author: Z. He
Author: P. M. Grant

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×