Abstract
Recently, we developed a sequence-based minimum mean-squared error (MMSE) estimator for decoding quantized data transmitted over noisy channels. The method effectively views the encoder and noisy channel tandem as a discrete hidden Markov model (HMM), with transmitted indices the unknown states and received indices the observable symbols. Here, we extend this 1D approach to images, using a Markov mesh random field to model the encoded image. Our decoder is based on an approximate Forward/Backward algorithm for calculating pixel `label probabilities' in Markov meshes which may also have application to image labeling and segmentation. For a DPCM-based image coding system and a high error-rate channel, the new decoder obtains significant performance gains, both objective and visually discernable, over the standard decoder, as well as over several other competing techniques.
Original language | English (US) |
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Pages | 594-597 |
Number of pages | 4 |
State | Published - 1997 |
Event | Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, USA Duration: Oct 26 1997 → Oct 29 1997 |
Other
Other | Proceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) |
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City | Santa Barbara, CA, USA |
Period | 10/26/97 → 10/29/97 |
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering