Multi-model prediction for image set compression | IEEE Conference Publication | IEEE Xplore

Multi-model prediction for image set compression


Abstract:

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model ...Show More

Abstract:

The key task in image set compression is how to efficiently remove set redundancy among images and within a single image. In this paper, we propose the first multi-model prediction (MoP) method for image set compression to significantly reduce inter image redundancy. Unlike the previous prediction methods, our MoP enhances the correlation between images using feature-based geometric multi-model fitting. Based on estimated geometric models, multiple deformed prediction images are generated to reduce geometric distortions in different image regions. The block-based adaptive motion compensation is then adopted to further eliminate local variances. Experimental results demonstrate the advantage of our approach, especially for images with complicated scenes and geometric relationships.
Date of Conference: 17-20 November 2013
Date Added to IEEE Xplore: 09 January 2014
ISBN Information:
Conference Location: Kuching, Malaysia

References

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