Discard Significant Bits of Compressed Sensing: A Robust Image Coding for Resource-Limited Contexts

• Published in: ACM transactions on multimedia computing communications and applications 2025-01, Vol.21 (1), p.1-25
• Main Authors: Chen, Zan, Wang, Tao, Li, Jun, Guo, Wenlong, Feng, Yuanjing, Qian, Xueming, Hou, Xingsong
• Format: Article
• Language: English
• Subjects: Computing methodologies; Image compression; Reconstruction
• ISSN: 1551-6857; 1551-6865
• DOI: 10.1145/3701732

Compressive sampling (CS) provides a robust and simple framework for compressing images in resource-constrained environments. However, CS-based image coding schemes often have poor rate-distortion (R-D) performance, particularly due to the quantization process. Our research indicates that leveraging the image prior enables the estimation of most significant bits (MSBs) from least significant bits (LSBs), which provides a quantization strategy to improve R-D performance without increasing coding complexity. That is discarding MSBs of measurements, and only transmitting LSBs to the decoder side. At the decoder side, we reconstruct images by solving an inverse-quantization set-constrained CS optimization problem. Our approach further employs a tailored designed deep denoiser as the proximal operator to enhance the reconstructed image quality.Extensive experimental results demonstrate that the proposed scheme achieves satisfactory performance, with promising R-D results (PSNR gains over 1.71 dB than JPEG at 0.50 bpp compression ratio), and robust bit error and loss resilience (reconstructed 29.98 dB even with 50% bit loss at 0.50 bpp compression ratio), meanwhile having lower encoding complexity (less than half encoding time of CCSDS-IDC).