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When dealing with signal compression, most compression algorithms optimize the reconstructed output with respect to the acquired input signal. A more general approach would be optimizing the compression algorithm with respect to the signal prior to the acquisition phase as the effective input, such that the final decompressed output is optimal. Ideally, this approach could utilize knowledge about specific acquisition devices to further optimize the compression, i.e. given a known degradation model, the presented approach could yield a highly optimized compressed result, which can be either used to transmit a signal of higher quality over the same infrastructure or alternatively, deliver the same quality using fewer resources.

Dar et al. [1] proposed an algorithm for joint restoration and compression of images, on which we rely in this work. This algorithm was implemented in MATLAB, which, due to MATLAB's overhead, opened up the possibility of significant efficiency improvements. For this reason, we chose to focus our efforts into optimizing the implementation runtime demands, while considering software engineering and object-oriented design as top priorities.


[1] Y. Dar, M. Elad, and A. M. Bruckstein, “Restoration by Compression,” IEEE Transactions on Signal Processing, vol. 66, no. 22, pp. 5833–5847, 2018.

Please, see project report.

Please, see final presentation.