Compressed Sensing-Based Distributed Image Compression
Source abstract
Abstract
In this paper, a new distributed block-based image compression method based on the principles of compressed sensing (CS) is introduced. The coding and decoding processes are performed entirely in the CS measurement domain. Image blocks are classified into key and non-key blocks and encoded at different rates. The encoder makes use of a new adaptive block classification scheme that is based on the mean square error of the CS measurements between blocks. At the decoder, a simple, but effective, side information generation method is used for the decoding of the non-key blocks. Experimental results show that our coding scheme achieves better results than existing CS-based image coding methods.
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Research context
OpenAlex topics: Sparse and Compressive Sensing Techniques · Image and Signal Denoising Methods · Advanced Data Compression Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand
School of Engineering and Advanced Technology, Massey University, Albany, Auckland, New Zealand
Asia-Pacific Broadcasting Union, Angkasapuri, Kuala Lumpur 50614, Malaysia
References (42)
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