A compressive sensing approach for progressive transmission of images

Sanei, Saeid; Phan, Anh Huy; Lo, Jen-Lung; Abolghasemi, Vahid; Cichocki, Andrzej

doi:10.1109/icdsp.2009.5201198

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Progressive quantization of compressive sensing [5][6][7] measurements is suggested in [8]. In [9], an adaptive joint source-channel coding method has been proposed for progressive transmission of medical images based on compressed sensing.…”

Section: Introductionmentioning

confidence: 99%

Progressive sparse image sensing using Iterative Methods

Azghani

Marvasti

2012

6th International Symposium on Telecommunications (IST)

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Progressive image transmission enables the receivers to reconstruct a transmitted image at various bit rates. Most of the works in this field are based on the conventional Shannon-Nyquist sampling theory. In the present work, progressive image transmission is investigated using sparse recovery of random samples. The sparse recovery methods such as Iterative Method with Adaptive Thresholding (IMAT) and Iterative IKMAX Thresholding (IKMAX) are exploited in this framework since they have the ability for successive reconstruction. The simulation results indicate that the proposed method performs well in progressive recovery. The IKMAX has better final reconstruction than IMAT at the cost of requiring sparse signal with extra information about the sparsity number. However, the IMAT has the ability to recover the compressible signals. Furthermore, two sampling strategies, random sampling and uniform sampling are exploited. The simulations show that IMAT is a better choice for random sampling and IKMAX behaves better than IMAT in the case of uniform sampling.

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