Efficient, Low Complexity Encoding of Multiple, Blurred Noisy Downsampled Images Via Distributed Source Coding Principles
Gaubatz, M.; Vosoughi, A.; Scaglione, A.; Hemami, S.S.
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Volume 2, Issue , 14-19 May 2006 Page(s):II - II
Digital Object Identifier 10.1109/ICASSP.2006.1660280
Summary:In a portable device, such as a digital camera, limitations on storage are an important consideration. In addition, due to constraints on the complexity of available hardware, image coding algorithms must be fairly simple in implementation. This work presents one such efficient method for coding multiple images of a scene, in a manner that complements a post-processing-based enhancement system. Super-resolution, image restoration and de-noising algorithms have demonstrated the ability to improve the quality of an image using multiple blurry, noisy copies of the same scene. This additional quality does not come without cost, however, since an image capture system must store each image. The proposed encoding scheme is derived from a general linear system model, and encodes multiple images of the same scene, with different amounts of blurring. It is also compared with a variety of methods based on current camera compression technology. For the tested images, this approach requires one-half the rate required by other methods at lower rates. In addition, for a small performance loss, it is essentially implementable without using any compression hardware
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