Recent developments in low-cost CMOS cameras have created the opportunity of bringing imaging capabilities to sensor networks. Various visual sensor platforms have been developed with the aim of integrating visual data to wireless sensor applications. The objective of this article is to survey current visual sensor platforms according to in-network processing and compression/coding techniques together with their targeted applications. Characteristics of these platforms such as level of integration, data processing hardware, energy dissipation, radios and operating systems are also explored and discussed.

A survey of visual sensor network platforms

Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a spatial correlation function is derived to describe the correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed correlation function and entropy-based framework, a correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial correlation function can model the correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.

A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks

Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

Highly Reliable and Low-Complexity Image Compression Scheme Using Neighborhood Correlation Sequence Algorithm in WSN

Data redundancy caused by correlation has motivated the application of collaborative multimedia in-network processing for data filtering and compression in wireless multimedia sensor networks (WMSNs). This paper proposes an information theoretic image compression framework with an objective to maximize the overall compression of the visual information gathered in a WMSN. The novelty of this framework relies on its independence of specific image types and coding algorithms, thereby providing a generic mechanism for image compression under different coding solutions. The proposed framework consists of two components. First, an entropy-based divergence measure (EDM) scheme is proposed to predict the compression efficiency of performing joint coding on the images collected by spatially correlated cameras. The EDM only takes camera settings as inputs without requiring statistics of real images. Utilizing the predicted results from EDM, a distributed multi-cluster coding protocol (DMCP) is then proposed to construct a compression-oriented coding hierarchy. The DMCP aims to partition the entire network into a set of coding clusters such that the global coding gain is maximized. Moreover, in order to enhance decoding reliability at data sink, the DMCP also guarantees that each sensor camera is covered by at least two different coding clusters. Experiments on H.264 standards show that the proposed EDM can effectively predict the joint coding efficiency from multiple sources. Further simulations demonstrate that the proposed compression framework can reduce 10%-23% total coding rate compared with the individual coding scheme, i.e., each camera sensor compresses its own image independently.

A Spatial Correlation-Based Image Compression Framework for Wireless Multimedia Sensor Networks

Low power hardware-based image compression solution for wireless camera sensor networks

Low-complexity and energy efficient image compression scheme for wireless sensor networks

The invention relates to the field of message processing, in particular to an equipment and a method for real-time compression for large-capability images and data based on the multi-digital signal processor (DSP) parallel processing A multi-DSP parallel processing structure is adopted to complete the real-time compression for large-capability images and data; a field programmable gate array (FPGA) is chosen as a data-transformation element, and the DSP is used as a managing element according to the task allocation and data coupling mode; the image data output by a camera enter each DSP of a data compressor in parallel, and the FPGA is not processed basically Code stream after being compressed by each DSP is output to the FPGA in parallel and is output serially after the FPGA makes parallel-serial conversion, so no coupling basically exists between every two DSPs The equipment has the advantages of strong data processing ability, good parallel performance, high reliability and easy expansion Besides being applied in a general occasion for real-time image compression, the equipment of the invent can be mainly applied in the occasions which have high image input-code speed for satellite remote sensing, high-altitude photography and the like and have special requirements in the aspects of the quality of reconstructing image, the reliability of the equipment, and the like

Large capacity image data real-time compressing apparatus and method based on multi-DSP parallel processing

The utility model relates to the field of information processing, in particular to large-capability image data real-time compression equipment based on multi-DSP parallel processing. A multi-DSP parallel processing structure is adopted to complete the real-time compression of the large-capability image data; according to task distribution and data coupling mode, the FPGA is selected as a data conversion and DSP management device, the image data output by a camera enters each DSP of the compressor in parallel, and the FPGA does not process the image data basically. Code streams compressed by each DSP are output to the FPGA in parallel and then are output in series after the FPGA carries out parallel-serial conversion; therefore, no coupling exists between all DSP basically. The equipment has the advantages of strong data processing capability, good parallel performance, high reliability and easy extension and can be applied to general real-time image compression situations; more importantly, the equipment can be applied to situations (such as satellite remote sensing, high altitude photographing and the like) which have high image input code rate and have special requirements on the quality of rebuilt image and equipment reliability and the like.

Large-capability image data real-time compression equipment based on multi-DSP parallel processing

The invention belongs to the image compression field in the information technology, in particular to a DSP efficient concurrent control method in a JPEG2000 compression algorithm. The current data read from a first memory are processed under bit-plane scanning to produce results which are wrote into a second memory, meanwhile, the pervious group of data is shifted to an external cache from a third memory; and the data are read from the second memory and are processed under arithmetic coding to generate results which are wrote into the third memory, meanwhile, the next group of data is shifted into the first memory from the external cache. The invention makes the concurrent implementation of EBC algorithm operation and data shifting possible. The invention can execute the shifting process in the process of executing any cycle, and the shifting process is executed at the background without occupying the processing time of CPU, that is equivalent to the data shifting process without time spending, thereby realizing zero-time data shifting.

DSP high-efficient concurrency control method for JPEG2000 compression algorithm

Satellite remote sensing image compression system is expected high compressing rate, real time dealing and high
fidelity. Based on lapped biorthogonal transform (LBT) and its integer algorithm, this paper describes a fixed-point
lapped transform algorithm using approximation to trigonometric function, studies the structure of LBT coefficients and
presents a modified zerotree coding algorithm. Also, this paper studies the implementation in TMS320C6000 DSP of the
algorithm, the processing speed is improved through task combining, complier options, software pipelining, loop
unrolling and complex instruction. This algorithm has been used in a prototype of data compression equipment. Image
coding experiments show that the algorithm's memory requirement is low and its coding performance is very
competitive with SPIHT when its complexity is far lower than SHIHT.

Jidong Wang

Papers

Low-complexity and energy efficient image compression scheme for wireless sensor networks

Large capacity image data real-time compressing apparatus and method based on multi-DSP parallel processing

Large-capability image data real-time compression equipment based on multi-DSP parallel processing

DSP high-efficient concurrency control method for JPEG2000 compression algorithm

The programming realization of image compression algorithm in satellite data compression equipment