An adaptive compressive sensing with side information

Guicquero, William; Dupret, Antoine

doi:10.1109/acssc.2013.6810246

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Blocked CS is based on the multiplexing of the sensor array, i. e., the partitioning in smaller sub-arrays or blocks, each of them characterized by a single sensing matrix and solved in parallel. Guicquero and Vandergheynst [37] proposed an adaptive CS scheme to adapt the number of measurements taken on each block by using the estimate of the measurements variance. The main drawback of blocked CS schemes is the introduction of artifacts at the interfaces.…”

Section: A Compressive Sensing and Sparsity-awarenessmentioning

confidence: 99%

Sparsity-Aware 3-D ToF Sensing

2023

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Section: A Compressive Sensing and Sparsity-awarenessmentioning

confidence: 99%

Sparsity-Aware 3-D ToF Sensing

2023

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“…Image acquisition with adaptivity has been introduced in several ways. For example, local features e.g., standard deviation [18], [19], edge counting [20] or estimation of the reconstruction error [21] in local domain is used to guide the adaptive acquisition. An adaptive scheme proposed in [22] by estimating the compression based on local redundancy measured statistically utilizing previously sensed measurements.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Video Acquisition Scheme for Object Tracking and its Performance Optimization

Banerjee¹,

Chopp²,

Serra³

et al. 2021

Preprint

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We present a novel adaptive host-chip modular architecture for video acquisition to optimize an overall objective task constrained under a given bit rate. The chip is a high resolution imaging sensor such as gigapixel focal plane array (FPA) with low computational power deployed on the field remotely, while the host is a server with high computational power. The communication channel data bandwidth between the chip and host is constrained to accommodate transfer of all captured data from the chip. The host performs objective task specific computations and also intelligently guides the chip to optimize (compress) the data sent to host. This proposed system is modular and highly versatile in terms of flexibility in re-orienting the objective task. In this work, object tracking is the objective task. While our architecture supports any form of compression/distortion, in this paper we use quadtree (QT)-segmented video frames. We use Viterbi (Dynamic Programming) algorithm to minimize the area normalized weighted rate-distortion allocation of resources. The host receives only these degraded frames for analysis. An object detector is used to detect objects, and a Kalman Filter based tracker is used to track those objects. Evaluation of system performance is done in terms of Multiple Object Tracking Accuracy (MOTA) metric. In this proposed novel architecture, performance gains in MOTA is obtained by twice training the object detector with different system generated distortions as a novel 2-step process. Additionally, object detector is assisted by tracker to upscore the region proposals in the detector to further improve the performance.

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

Zammit¹,

Wassell²

2020

IEEE Access

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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.

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An adaptive compressive sensing with side information

Cited by 8 publications

References 5 publications

Sparsity-Aware 3-D ToF Sensing

Sparsity-Aware 3-D ToF Sensing

An Adaptive Video Acquisition Scheme for Object Tracking and its Performance Optimization

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

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