  Adaptive block-based compressive sensing (ABCS) algorithms are studied in the
context of the practical realization 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 256x256 and 512x512
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 denoising-based
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 GPU-assisted 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.
