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.
Highlights
Traditional image coding techniques are developed for applications where the source is encoded once and the coded data is played back many times
While each key block is reconstructed from its own measurements, the decoding of WZ blocks requires the help of side information, due to a reduced sampling rate
The visual reconstruction quality of the reconstructed images is evaluated by the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM)
Summary
Traditional image coding techniques are developed for applications where the source is encoded once and the coded data is played back (decoded) many times. The image is divided into non-overlapping blocks of pixels, and each image block is encoded independently These methods generally use the same measurement rate for all image blocks, even though the compressibility and sparsity of the individual blocks can be quite different. Most proposed coding schemes use a block-based coding approach, where the image is divided into non-overlapping blocks of pixels and each image block is coded independently. They generally use the same measurement rate for all image blocks, even though the compressibility and sparsity of the individual blocks can be quite different [7,8,9,10].
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