### Abstract

Artificial neural system (ANS) classification has been applied to the total set of states of a finite-state machine operating as part of an image-sequence coder. It has been found that the classification of states allows the use of a much smaller number of representation states, thereby drastically reducing the storage requirements of the finite-state machine. The coder implements a scheme for high compression of teleconferencing image-sequence data. It utilizes neural-net-based learning vector quantization (LVQ) operating in the spatial domain on 16-dimensional vectors. The method is structured as a combination of an intraframe algorithm followed by an interframe algorithm, operating on a bundle of frames. The intraframe algorithm operates on the head frame of the bundle; the interframe algorithm follows in order to encode the remaining frames. Then, the encoding process repeats with a new bundle of frames. The intraframe and the interframe algorithms are finite state-based. Simulation experiments have been carried out for a videoconferencing image sequence consisting of 20 frames of 112 × 96 pixels, with 25% average block motion. The representation vectors were of 2 × 2 × 4 resolution. The results obtained have shown that for peak signal-to-noise ratio (PSNR) = 32 dB, the required bit rate is 0.08 to 0.10 b/pixel.

Original language | English |
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Pages | 481-486 |

Number of pages | 6 |

State | Published - 1 Dec 1990 |

Event | 1990 International Joint Conference on Neural Networks - IJCNN 90 - San Diego, CA, USA Duration: 17 Jun 1990 → 21 Jun 1990 |

### Other

Other | 1990 International Joint Conference on Neural Networks - IJCNN 90 |
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City | San Diego, CA, USA |

Period | 17/06/90 → 21/06/90 |

### Fingerprint

### Cite this

*LVQ of image sequence source and ANS classification of finite state machine for high compression coding*. 481-486. Paper presented at 1990 International Joint Conference on Neural Networks - IJCNN 90, San Diego, CA, USA, .

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**LVQ of image sequence source and ANS classification of finite state machine for high compression coding.** / Manikopoulos, C.; Antoniou, George; Metzelopoulou, S.

Research output: Contribution to conference › Paper › Research › peer-review

TY - CONF

T1 - LVQ of image sequence source and ANS classification of finite state machine for high compression coding

AU - Manikopoulos, C.

AU - Antoniou, George

AU - Metzelopoulou, S.

PY - 1990/12/1

Y1 - 1990/12/1

N2 - Artificial neural system (ANS) classification has been applied to the total set of states of a finite-state machine operating as part of an image-sequence coder. It has been found that the classification of states allows the use of a much smaller number of representation states, thereby drastically reducing the storage requirements of the finite-state machine. The coder implements a scheme for high compression of teleconferencing image-sequence data. It utilizes neural-net-based learning vector quantization (LVQ) operating in the spatial domain on 16-dimensional vectors. The method is structured as a combination of an intraframe algorithm followed by an interframe algorithm, operating on a bundle of frames. The intraframe algorithm operates on the head frame of the bundle; the interframe algorithm follows in order to encode the remaining frames. Then, the encoding process repeats with a new bundle of frames. The intraframe and the interframe algorithms are finite state-based. Simulation experiments have been carried out for a videoconferencing image sequence consisting of 20 frames of 112 × 96 pixels, with 25% average block motion. The representation vectors were of 2 × 2 × 4 resolution. The results obtained have shown that for peak signal-to-noise ratio (PSNR) = 32 dB, the required bit rate is 0.08 to 0.10 b/pixel.

AB - Artificial neural system (ANS) classification has been applied to the total set of states of a finite-state machine operating as part of an image-sequence coder. It has been found that the classification of states allows the use of a much smaller number of representation states, thereby drastically reducing the storage requirements of the finite-state machine. The coder implements a scheme for high compression of teleconferencing image-sequence data. It utilizes neural-net-based learning vector quantization (LVQ) operating in the spatial domain on 16-dimensional vectors. The method is structured as a combination of an intraframe algorithm followed by an interframe algorithm, operating on a bundle of frames. The intraframe algorithm operates on the head frame of the bundle; the interframe algorithm follows in order to encode the remaining frames. Then, the encoding process repeats with a new bundle of frames. The intraframe and the interframe algorithms are finite state-based. Simulation experiments have been carried out for a videoconferencing image sequence consisting of 20 frames of 112 × 96 pixels, with 25% average block motion. The representation vectors were of 2 × 2 × 4 resolution. The results obtained have shown that for peak signal-to-noise ratio (PSNR) = 32 dB, the required bit rate is 0.08 to 0.10 b/pixel.

UR - http://www.scopus.com/inward/record.url?scp=0025545469&partnerID=8YFLogxK

M3 - Paper

SP - 481

EP - 486

ER -