Discard Significant Bits of Compressed Sensing: A Robust Image Coding for Resource-Limited Contexts
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| Title: | Discard Significant Bits of Compressed Sensing: A Robust Image Coding for Resource-Limited Contexts |
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| Authors: | Zan Chen, Tao Wang, Jun Li, Wenlong Guo, Yuanjing Feng, Xueming Qian, Xingsong Hou |
| Source: | ACM Transactions on Multimedia Computing, Communications, and Applications. 21:1-25 |
| Publisher Information: | Association for Computing Machinery (ACM), 2024. |
| Publication Year: | 2024 |
| Description: | Compressed sensing (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). |
| Document Type: | Article |
| Language: | English |
| ISSN: | 1551-6865 1551-6857 |
| DOI: | 10.1145/3701732 |
| Rights: | URL: https://www.acm.org/publications/policies/copyright_policy#Background |
| Accession Number: | edsair.doi...........4e3d7910a38ecc517d7866226584a511 |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Compressed sensing (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). |
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| ISSN: | 15516865 15516857 |
| DOI: | 10.1145/3701732 |