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Using visual feature extraction neural network model to improve performance of quadtree based image coding

Using visual feature extraction neural network model to improve performance of quadtree based image coding
Using visual feature extraction neural network model to improve performance of quadtree based image coding
In this paper, we propose a new technique to improve the performance of quadtree (QT) based image coding through the utilization of a neural network based visual feature extraction model(VFEM). After QT reconstruction is completed, a trained VFEM uses the information contained in the QT reconstructed image to recover the QT reconstruction error. This results in a better quality reconstructed image than the one simply reconstructed from QT representation. Since no extra information other than QT structure itself needs to be transmitted, the VFEM improvement does not increase the coding bit rate. Therefore, a better rate-distortion performance is achieved.
30-35
He, Z.
c7048a1b-3632-409a-96f5-e82b998d4754
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
He, Z.
c7048a1b-3632-409a-96f5-e82b998d4754
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80

He, Z. and Chen, S. (1997) Using visual feature extraction neural network model to improve performance of quadtree based image coding. Proceedings of 5th IEE International Conference on Artificial Neural Networks. pp. 30-35 .

Record type: Conference or Workshop Item (Other)

Abstract

In this paper, we propose a new technique to improve the performance of quadtree (QT) based image coding through the utilization of a neural network based visual feature extraction model(VFEM). After QT reconstruction is completed, a trained VFEM uses the information contained in the QT reconstructed image to recover the QT reconstruction error. This results in a better quality reconstructed image than the one simply reconstructed from QT representation. Since no extra information other than QT structure itself needs to be transmitted, the VFEM improvement does not increase the coding bit rate. Therefore, a better rate-distortion performance is achieved.

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More information

Published date: 7 July 1997
Additional Information: IEE 5th International Conference on Artificial Neural Networks (Cambridge, UK), July 7-9, 1997. Organisation: IEE
Venue - Dates: Proceedings of 5th IEE International Conference on Artificial Neural Networks, 1997-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251012
URI: http://eprints.soton.ac.uk/id/eprint/251012
PURE UUID: d5f34390-b2d0-4b7b-80db-36594753e61d

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Date deposited: 31 Mar 2000
Last modified: 01 Feb 2022 17:47

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Contributors

Author: Z. He
Author: S. Chen

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