| Paper: | IMDSP-L3.5 | ||
| Session: | Distributed Source Coding | ||
| Time: | Tuesday, May 16, 17:50 - 18:10 | ||
| Presentation: | Lecture | ||
| Topic: | Image and Multidimensional Signal Processing: Distributed Source Coding | ||
| Title: | EFFICIENT, LOW COMPLEXITY ENCODING OF MULTIPLE, BLURRED NOISY DOWNSAMPLED IMAGES VIA DISTRIBUTED SOURCE CODING PRINCIPLES | ||
| Authors: | Matthew Gaubatz, Azadeh Vosoughi, Anna Scaglione, Sheila Hemami, Cornell University, United States | ||
| Abstract: | 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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