Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD) (original) (raw)
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Applied Sciences, 2020
Several real-time visual monitoring applications such as surveillance, mental state monitoring, driver drowsiness and patient care, require equipping high-quality cameras with wireless sensors to form visual sensors and this creates an enormous amount of data that has to be managed and transmitted at the sensor node. Moreover, as the sensor nodes are battery-operated, power utilization is one of the key concerns that must be considered. One solution to this issue is to reduce the amount of data that has to be transmitted using specific compression techniques. The conventional compression standards are based on complex encoders (which require high processing power) and simple decoders and thus are not pertinent for battery-operated applications, i.e., VSN (primitive hardware). In contrast, compressive sensing (CS) a distributive source coding mechanism, has transformed the standard coding mechanism and is based on the idea of a simple encoder (i.e., transmitting fewer data-low proces...
COMPRESSIVE SENSING APPLICATIONS
Requirement for energy efficient systems has increased due to never stopping growth in electronic appliances and sensor networks constitute a big part of it. Energy consumption in a network can be reduced drastically using Compressive Sensing. Compressive sensing also reduces hardware requirement and thus has and economic aspect too which needs to be looked at. This project aims at scope of implementation of compressive sensing and save energy in few areas where a lot of energy is spent in collecting and processing data. Ashish Anand 2013A8PS396P
Compressive Sensing based Image Compression and Recovery
Compressive sensing is a new paradigm in image acquisition and compression. The CS theory promises recovery of images even if the sampling rate is far below the nyquist rate. This enables better acquisition and easy compression of images, which is more advantageous when the resources at the sender side are scarce. This paper shows the CS based compression and two recovery two methods i.e., l1 optimization and TSW CS recovery. Experimental results show that CS provides better compression, and TSWCS provides better recovery with less relative error recovery than l1 optimization. It is also observed that use of increased measurements leads to reduced error.
Compressive Sensing Image Sensors-Hardware Implementation
Sensors, 2013
The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.
A Novel Image Compressive Sensing Method Based on Complex Measurements
2011 International Conference on Digital Image Computing: Techniques and Applications, 2011
Compressive sensing (CS) has emerged as an efficient signal compression and recovery technique, that exploits the sparsity of a signal in a transform domain to perform sampling and stable recovery. The existing image compression methods have complex coding techniques involved and are also vulnerable to errors. In this paper, we propose a novel image compression and recovery scheme based on compressive sensing principles. This is an alternative paradigm to conventional image coding and is robust in nature. To obtain a sparse representation of the input, discrete wavelet transform is used and random complex Hadamard transform is used for obtaining CS measurements. At the decoder, sparse reconstruction is carried out using compressive sampling matching pursuit (CoSaMP) algorithm. We show that, the proposed CS method for image sampling and reconstruction is efficient in terms of complexity, quality and is comparable with some of the existing CS techniques. We also demonstrate that our method uses considerably less number of random measurements.
Line-based compressive sensing for low-power visual applications
arXiv: Image and Video Processing, 2019
In this paper, a Line based Compressive Sensing (LCS) scheme is discussed and proposed for low power visual applications, in which image acquisition is performed in a line-by-line manner at the encoder side using same measurement operator. Such approach reduces the computational burden and makes the implementation process easier (at encoder) plus provides better and more efficient initial reconstruction (at decoder) than other CS techniques. The reconstruction algorithm is based on the combination of the conventional augmented Lagrangian method with variable splitting and alternating direction method and is referred as TV-AL3. The simulation results show that the proposed line based CS scheme not only improves the quality of image by 1dB to ~3dB at various subrates, when compared to the conventional CS schemes but also reduces the computational complexity at the encoder side.
Compressive Sensing a New Approach For Data Compression: A Review
2017
As we know that for data compression generally Shannon – Nyquist theorem taken in to consideration. But a severe problem which is associated with the traditional theory is the storage problem. According to this theorem the sampling rate must be twice the largest frequency component of the signal which we want to reconstruct. Due to this the data which is required to transmit a signal or to store it is too large. So to overcome this problem a new method is proposed, which is known as Compressive sensing. The sampling rate which is required reconstruct the signal is comparatively low in the compressive sensing. The various aspects about the compressive sensing and literature review with some important properties is given below. KeywordsCompressive Sensing(CS),Restricted Isometry Property (RIP) __________________________________________________*****_________________________________________________
Dictionary learning-based distributed compressive video sensing
Picture Coding Symposium (PCS …, 2010
We address an important issue of fully low-cost and low-complex video compression for use in resource-extremely limited sensors/devices. Conventional motion estimation-based video compression or distributed video coding (DVC) techniques all rely on the high-cost mechanism, namely, sensing/sampling and compression are disjointedly performed, resulting in unnecessary consumption of resources. That is, most acquired raw video data will be discarded in the (possibly) complex compression stage. In this paper, we propose a dictionary learning-based distributed compressive video sensing (DCVS) framework to "directly" acquire compressed video data. Embedded in the compressive sensing (CS)-based single-pixel camera architecture, DCVS can compressively sense each video frame in a distributed manner. At DCVS decoder, video reconstruction can be formulated as an l 1minimization problem via solving the sparse coefficients with respect to some basis functions. We investigate adaptive dictionary/basis learning for each frame based on the training samples extracted from previous reconstructed neighboring frames and argue that much better basis can be obtained to represent the frame, compared to fixed basis-based representation and recent popular "CS-based DVC" approaches without relying on dictionary learning.
Quantized Compressive Sensing Video Compression Framework for Low Power Applications
Compressive sensing is an approach to reduce the acquired signal dimension with a sampling rate lower than the Nyquist rate. This is achieved by incorporating suitable quantization techniques, such as scalar quantization, to compensate for the low sampling rate. However, quantization of small compressive sensing measurements does not perform well in terms of rate-distortion. This paper presents an adaptive variant of differential pulse code modulation coupled with scalar quantization for quantizing and de-quantizing the compressive sensing measurements of videos. The proposed adaptive approach along with scalar quantization allows reduction of the necessary bandwidth and provides a higher prediction gain than the conventional differential pulse code modulation. The performance of the proposed approach is validated by integrating with various compressive sensing reconstruction schemes and compared with the conventional video codec approach. The reconstructed video frames' visual ...
International Journal of Electrical and Computer Engineering (IJECE), 2022
The modern digital world comprises of transmitting media files like image, audio, and video which leads to usage of large memory storage, high data transmission rate, and a lot of sensory devices. Compressive sensing (CS) is a sampling theory that compresses the signal at the time of acquiring it. Compressive sensing samples the signal efficiently below the Nyquist rate to minimize storage and recoveries back the signal significantly minimizing the data rate and few sensors. The proposed paper proceeds with three phases. The first phase describes various measurement matrices like Gaussian matrix, circulant matrix, and special random matrices which are the basic foundation of compressive sensing technique that finds its application in various fields like wireless sensors networks (WSN), internet of things (IoT), video processing, biomedical applications, and many. Finally, the paper analyses the performance of the various reconstruction algorithms of compressive sensing like basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), iteratively reweighted least square (IRLS), iterative hard thresholding (IHT), block processing-based basis pursuit (BP-BP) based on mean square error (MSE), and peak signal to noise ratio (PSNR) and then concludes with future works.