Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

Abstract

: The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization

“…The second step is to calculate the sampling rate or the number of measurements of each block according to the allocation method. The most commonly used non-iterative allocation method [7,8,[10][11][12][13][14][15][16]19] uses the linear proportion of the allocation factor to estimate the sampling rates. The sampling rate m i of the ith block can be expressed as follows:…”

“…The same measurement matrix measures the raster scan vector of each block. Since different blocks contain different amounts of valuable information, adaptive block compressed sensing (ABCS) methods [7][8][9][10][11][12][13][14][15][16] have been proposed to make full use of limited measurement resources.…”

“…Moreover, some researchers combined multiple features to allocate the sampling rate of each block. Heng et al [16] used fuzzy logic systems to select allocation factors from saliency and standard deviation features. Xu et al [14] took the human visual system characteristics and signal sparsity as allocation factors.…”

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT

“…In addition, lossy image compression algorithms have higher compression rates than lossless image compression algorithms. In the prior art, common image compression algorithms include JPEG [ 8 , 9 , 10 , 11 , 12 ] and block truncation coding (BTC) [ 13 , 14 , 15 , 16 , 17 ]. For the development of JPEG technology, JPEG-CHE [ 10 ] decompresses precise data via compression history estimation (CHE), which is usually discarded after decompression.…”

“…Li et al [ 16 ] adopted ABCS for the Green Internet of Things (GIoT) with low power consumption. Sovannarith et al [ 17 ] proposed a fuzzy adaptive sampling block compressed sensing (FABCS) which combined ABCS and a fuzzy logic system (FLS). This algorithm can be applied in wireless multimedia sensor network (WMSN) architecture and detect features to sample the base and feature layer.…”