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University of Toronto
1. Goukhshtein, Maxim. Distributed Coding of Compressively Sensed Sources.
Degree: 2017, University of Toronto
In this work we propose a new method for compressing multiple correlated sources with a very low-complexity encoder in the presence of side information. Our approach uses ideas from compressed sensing and distributed source coding. At the encoder, syndromes of the quantized compressively sensed sources are generated and transmitted. The decoder uses side information to predict the compressed sources. The predictions are then used to recover the quantized measurements via a two-stage decoding process consisting of bitplane prediction and syndrome decoding. Finally, guided by the structure of the sources and the side information, the sources are reconstructed from the recovered measurements. As a motivating example, we consider the compression of multispectral images acquired on board satellites, where resources, such as computational power and memory, are scarce. Our experimental results exhibit a significant improvement in the rate-distortion trade-off when compared against approaches with similar encoder complexity.
M.A.S.Advisors/Committee Members: Draper, Stark C, Electrical and Computer Engineering.
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APA (6th Edition):
Goukhshtein, M. (2017). Distributed Coding of Compressively Sensed Sources. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/79233
Chicago Manual of Style (16th Edition):
Goukhshtein, Maxim. “Distributed Coding of Compressively Sensed Sources.” 2017. Masters Thesis, University of Toronto. Accessed June 19, 2018. http://hdl.handle.net/1807/79233.
MLA Handbook (7th Edition):
Goukhshtein, Maxim. “Distributed Coding of Compressively Sensed Sources.” 2017. Web. 19 Jun 2018.
Goukhshtein M. Distributed Coding of Compressively Sensed Sources. [Internet] [Masters thesis]. University of Toronto; 2017. [cited 2018 Jun 19]. Available from: http://hdl.handle.net/1807/79233.
Council of Science Editors:
Goukhshtein M. Distributed Coding of Compressively Sensed Sources. [Masters Thesis]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/79233