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
7 July 1997
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.
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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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Published date: 7 July 1997
Additional Information:
IEE 5th International Conference on Artificial Neural Networks (Cambridge, UK), July 7-9, 1997. Organisation: IEE
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Proceedings of 5th IEE International Conference on Artificial Neural Networks, 1997-01-01
Organisations:
Southampton Wireless Group
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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: 14 Mar 2024 05:07
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Author:
Z. He
Author:
S. Chen
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