Compressive-Sensed Image Coding via Stripe-based DPCM
These years have seen the advances of compressive sensing (CS), but efficient coding of sensed measurements is still an issue. In this paper, we propose an image coding system based on the compressive sensing paradigm via stripe-based differential pulse-code modulation (DPCM). In the system, we sample and encode an image in a unit of multiple rows, which we call a stripe. Through extensive experiments, we observe that the correlation between measurements of adjacent stripes are much higher than that of the neighboring blocks. Based on this, we combine the stripe-based CS acquisition with the DPCM framework and design a mechanism that predicts a stripe of measurements from its preceding stripe of measurements. The produced measurement residuals are then quantized and entropy-encoded into binary coding bits, which are tremendously reduced compared to the traditional block-based framework. Furthermore, we provide an image CS reconstruction algorithm corresponding to the stripe-based acquisition. Experiments verify that the reconstruction quality is no worse or even better than the block-based case when much lower bitrate is consumed. In a rate-distortion point of view, the proposed system also outperforms the methods using block-based sampling and achieves the state-of-the-art performance for compressive-sensed image coding.