Low-complexity Compressive Sensing-based Scalable Image Codec

In this paper, a low complexity compressive sensing (CS) based scalable image codec is proposed. At the encoder side, hierarchical wavelet decomposition of an input image is used that results in one low-frequency subband with no sparsity and a number of high-frequency subbands having sparsity. An adaptive measurement process is then applied on detail subbands to select the number of measurements. Set Partitioning in Hierarchical Tree (SPIHT) is used to encode the measurements to get a scalable bitstream. At the decoder, a non-iterative pseudoinverse algorithm with low complexity for CS reconstruction is used. Results show that the proposed method gives a PSNR improvement of 1 to 3.5 dB compared to other algorithms. Moreover, being non-iterative, the proposed codec is also having extremely low complexity.