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Updated: Jul 7, 2026
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Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)
Published on: May 27, 2012
Progressive transmission of images using MAP detection over channels with memory
B S Srinivas1, R E Ladner, M Azizoglu
1Sarnoff Corp, Princeton, NJ 08543, USA. bsrinivas@sarnoff.com
Summary
This study introduces a novel maximum a posteriori (MAP) detector to reduce communication channel errors in compressed images. The detector leverages spatial and temporal correlations for improved error correction without explicit channel coding.
Area of Science:
- Digital Image Processing
- Information Theory
- Communication Systems
Background:
- Communication channel errors significantly degrade compressed image quality.
- Existing methods often rely on explicit channel coding, adding complexity.
- Exploiting source statistics can potentially improve error resilience.
Purpose of the Study:
- To develop a maximum a posteriori (MAP) detector for mitigating channel errors in compressed image transmission.
- To evaluate the effectiveness of the proposed MAP detector against a memoryless approach.
- To analyze the impact of channel memory and error rates on detector performance.
Main Methods:
- A novel MAP detector is proposed, integrating spatial correlation of VQ-compressed images and channel temporal memory.
- A technique for computing residual redundancy in VQ-compressed grayscale images is presented.
- Performance comparison with a memoryless MAP detector under varying Gilbert-Elliott channel conditions.
Main Results:
- The proposed MAP detector demonstrates superior performance in correcting channel errors compared to the memoryless detector.
- Performance is shown to be dependent on the memory characteristics of the Gilbert-Elliott channel and the average channel error rate.
- The detector exhibits robustness to estimation errors in channel parameters.
Conclusions:
- The novel MAP detector effectively reduces the impact of channel errors on compressed images without explicit channel coding.
- Exploiting source and channel correlations offers a promising approach for robust image transmission.
- The findings provide valuable insights for designing error-resilient image compression and transmission systems.
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