Intra Prediction-Based Measurement Coding Algorithm for Block-Based Compressive Sensing Images

Block-based compressive image sensor (BCIS) captures light and represents them as compressed data called measurement. It has potential to revolutionize conventional image and video acquisition system that builds upon high complexity and redundant process. However, by comparing the compression performance between these two systems, BCIS cannot reduce bitrate to similar factor as the compressed media by pixel-based compression algorithms. It still requires enormous amounts of bit to store and transmit data. In this work, we introduce intra prediction based measurement coding algorithm for giving an extra compression performance to measurement. Moreover, importantly, there is a requirement that sensing matrix for BCIS must not be derived from non-uniform distribution in order to control prediction accuracy and quality. Therefore, we use structural sensing matrix made of sequency-ordered Walsh-Hadamard. Furthermore, it allows boundary pixels of adjacent blocks to be accessible through measurement, which helps intra prediction to generate its candidates accurately. The algorithm encodes prediction error between target measurement and multiple prediction candidates, resulting in smaller data size. This work can significantly reduce bpp by 10.90% and simultaneously increase 3.95 dB in PSNR compared to the state-of-the-art works. Moreover, we implemented the proposal on FPGA. It gave 10 times higher throughput than software. The core power consumption is at 50 mW and working at 88 MHz when processing 3840×2160 pixels with the sampling rate of 1/4.