Adaptive Block Compressive Sensing: Toward a Real-Time and Low-Complexity Implementation

Abstract

Adaptive block-based compressive sensing (ABCS) algorithms are studied in the context of the practical realisation of compressive sensing on resource-constrained image and video sensing platforms that use single-pixel cameras, multi-pixel cameras or focal plane processing sensors. In this paper, we introduce two novel ABCS algorithms that are suitable for compressively sensing images or intra-coded video frames. Both use deterministic 2D-DCT dictionaries when sensing the images instead of random dictionaries. The first uses a low number of compressive measurements to compute the block boundary variation (BBV) around each image block, from which it estimates the number of 2D-DCT transform coefficients to measure from each block. The second uses a low number of DCT domain (DD) measurements to estimate the total number of transform coefficients to capture from each block. The two algorithms permit reconstruction in real time, averaging 8 ms and 26 ms for 256 × 256 and 512 × 512 greyscale images, respectively, using a simple inverse 2D-DCT operation without requiring GPU acceleration. Furthermore, we show that an iterative compressive sensing reconstruction algorithm (IDA), inspired by the denoisingbased approximate message passing algorithm, can be used as a post-processing, quality enhancement technique. IDA trades off real-time operation to yield performance improvement over state-of-the-art GPUassisted algorithms of 1.31 dB and 0.0152 in terms of PSNR and SSIM, respectively. It also exceeds the PSNR performance of a state-of-the-art deep neural network by 0.4 dB and SSIM by 0.0126.

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

“…This section evaluates the performance of the proposed BCS method with quantized adaptive measurement ratio in terms of the peak signal-to-noise ratio (PSNR) of reconstructed images, by applying the proposed technique to the real SAR images in [51] and experimental data obtained by self-made drone SAR and vehicular SAR systems in Section 4. Moreover, through numerical simulations, the proposed method is compared to the existing BCS-based image compression methods in terms of the PSNR and the execution time.…”

“…To optimize the parameters and evaluate the performance of the proposed method, we use the real SAR images provided by Sandia National Lab., Radar ISR [51] and experimental data obtained by self-made drone SAR and vehicular SAR systems. Numerical simulations show that the proposed technique is more beneficial to SAR image compression than conventional schemes such as the BCS with fixed measurement rate and the variance-based adaptive BCS.…”

“…SAR images for performance evaluation (the images 1∼4 are available by courtesy of Sandia National Lab., Radar ISR[51] and the images 5 and 6 are obtained by field measurement in Section 4).…”

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

“…CS theory indicates that the precise recovery requires enough CS measurements. With insufficient CS measurements, the excellent CS recovery algorithm still cannot prevent the degradation of reconstruction quality, however, by adaptively allocating CS measurements based on local structures of the image, a simple recovery algorithm can also provide a good reconstruction quality (Yu et al, 2010 ; Taimori and Marvasti, 2018 ; Zammit and Wassell, 2020 ). Judging from the above facts, the adaptive allocation is a potential way to improve the rate-distortion performance of the CVS system with a light codec.…”

Distributed Video Adaptive Block Compressive Sensing

“…With block-based processing, each block has its own sparsity level, that is changing from frame to frame. In adaptive block-based image CS, the sparsity of each block is estimated prior to compressively sensing it [12]. The challenge is to estimate the sparsity with as low a complexity and overhead as possible.…”