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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.
ac405529-3375-471a-8257-bda5c0d10e53
He, Z.
c7048a1b-3632-409a-96f5-e82b998d4754
Grant, P. M.
e527fff4-da0f-4bc4-91cf-eed522070300
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
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.

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More information

Published date: January 2000
Organisations: Southampton Wireless Group

Identifiers

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

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Date deposited: 13 Sep 2000
Last modified: 18 Jul 2017 10:12

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