Efficient and Robust Image Coding and Transmission based on Scrambled Block Compressive Sensing

Abstract

Image transmission in a wireless visual sensor network (WVSN) with limited resources over an unreliable and bandwidth-limited wireless channel is challenging. This paper presents a highly efficient and robust image coding and transmission scheme with a simple encoder based on compressive sensing (CS) for WVSNs. First, an image measurement based on scrambled block compressive sampling with a separable sensing operator is proposed to simplify the encoder. Second, a progressive nonuniform quantization, which exploits the measurement distribution at the encoder side and the measurement dependencies at the decoder side, is designed to improve the rate-distortion (R-D) performance while maintaining low complexity at the encoder. Third, to further improve the R-D performance, a progressive non-local low-rank reconstruction is designed at the decoder. The experimental results show that the proposed scheme can achieve higher R-D performance compared with the benchmark CS-based image coding and transmission schemes. Higher robustness can be achieved compared with the traditional source-channel coding, such as Consultative Committee for Space Data Systems $-$ Image Data Compression (CCSDS-IDC) with Raptor codes under a time-varying packet loss channel, and the encoding time can be significantly reduced compared with the traditional image coding schemes. The experimental results also show that the proposed scheme achieves state-of-the-art coding efficiency with lower computational complexity at the encoder while still supporting error resilience.