Detecting and coding region of interests in bi-level images for data reduction in Wireless Visual Sensor Network

Khursheed, Khursheed; Ahmad, Naeem; Imran, Muhammad; O’Nils, Mattias

doi:10.1109/wimob.2012.6379153

Cited by 6 publications

(17 citation statements)

References 11 publications

(9 reference statements)

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“…In 9 , we explored the compression efficiency of Region of Interest (ROI) coding for various geometrical shaped objects in the black images. The results of ROI coding in 9 showed some improvement in the compression efficiency compared to that of change coding in 8 .…”

Section: Introductionmentioning

confidence: 92%

“…We investigated the possibility of further data reduction based on, change coding and ROI coding in 8,9 respectively. We concluded in 8 that change coding provides better compression efficiency than image coding for up to 95 % changes in terms of number of objects in the adjacent frames.…”

Section: Related Workmentioning

confidence: 99%

“…This new method must be applicable to all applications (applications attributing too few or too many objects in the change frames). By doing so, we will be able to apply this new method (Binary Video Coding or BVC) to all applications and its performance will always be better than or equal to that of change coding, ROI coding and image coding (It will never become worse than image coding which was the case for change coding and ROI coding in 8,9 ).…”

Section: Introductionmentioning

confidence: 99%

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Binary video codec for data reduction in wireless visual sensor networks

et al. 2013

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Wireless Visual Sensor Networks (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. Typical applications of WVSN include environmental monitoring, health care, industrial process monitoring, stadium/airports monitoring for security reasons and many more. The energy budget in the outdoor applications of WVSN is limited to the batteries and the frequent replacement of batteries is usually not desirable. So the processing as well as the communication energy consumption of the VSN needs to be optimized in such a way that the network remains functional for longer duration. The images captured by VSN contain huge amount of data and require efficient computational resources for processing the images and wide communication bandwidth for the transmission of the results. Image processing algorithms must be designed and developed in such a way that they are computationally less complex and must provide high compression rate. For some applications of WVSN, the captured images can be segmented into bi-level images and hence bi-level image coding methods will efficiently reduce the information amount in these segmented images. But the compression rate of the bi-level image coding methods is limited by the underlined compression algorithm. Hence there is a need for designing other intelligent and efficient algorithms which are computationally less complex and provide better compression rate than that of bi-level image coding methods. Change coding is one such algorithm which is computationally less complex (require only exclusive OR operations) and provide better compression efficiency compared to image coding but it is effective for applications having slight changes between adjacent frames of the video. The detection and coding of the Region of Interest (ROIs) in the change frame efficiently reduce the information amount in the change frame. But, if the number of objects in the change frames is higher than a certain level then the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Binary Video Codec (BVC) for the data reduction in WVSN. We proposed to implement all the three compression techniques i.e. image coding, change coding and ROI coding at the VSN and then select the smallest bit stream among the results of the three compression techniques. In this way the compression performance of the BVC will never become worse than that of image coding. We concluded that the compression efficiency of BVC is always better than that of change coding and is always better than or equal that of ROI coding and image coding.

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Section: Introductionmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Binary video codec for data reduction in wireless visual sensor networks

et al. 2013

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“…We explored the possibility of further data reduction in [11] based on change coding, where we concluded that change coding is better than image coding (the image coding is investigated in [7]) for up to 95% changes in terms of number of objects in the neighbouring frames. We explored the compression efficiency of Region of Interest (ROI) coding for various geometrical shaped objects in the black images and presented the results in [12]. The results of ROI coding in [12] showed some improvements in the compression performance compared to the results of change coding in [11].…”

mentioning

confidence: 99%

“…We explored the compression efficiency of Region of Interest (ROI) coding for various geometrical shaped objects in the black images and presented the results in [12]. The results of ROI coding in [12] showed some improvements in the compression performance compared to the results of change coding in [11].…”

mentioning

confidence: 99%

Bi-Level Video Codec for Machine Vision Embedded Applications

Khursheed

Imran

Ahmad

et al. 2013

ElAEE

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Wireless Visual Sensor Networks (WVSN) are feasible today due to the advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) cameras, low power electronics, distributed computing and radio transceivers. The energy budget in WVSN is limited due to the small form factor of the Visual Sensor Nodes (VSNs) and the wireless nature of the application. The images captured by VSN contain huge amount of data which leads to high communication energy consumptions. Hence there is a need for designing efficient algorithms which are computationally less complex and provide high compression ratio. The change coding and Region of Interest (ROIs) coding are the options for data reduction of the VSN. But, for higher number of objects in the images, the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Bi-Level Video Codec (BVC) for several representative machine vision applications. We proposed to implement image coding, change coding and ROI coding at the VSN and to select the smallest bit stream among the three. Results show that the compression performance of the BVC for such applications is always better than that of change coding and ROI coding. Index Terms-Wireless sensor networks, low power electronics, embedded computing, image communication. I. INTRODUCTION The Wireless Visual Sensor Network (WVSN) is composed of many Visual Sensor Nodes (VSNs). Each VSN consist of an image sensor for capturing images of the field of interest, microcontroller or Field Programmable gate Arrays (FPGA) for image processing, a radio transceiver for transmitting the results and energy resource for providing power to all the other components. The VSN is capable of performing complex image processing algorithms using limited energy resources due to the technological advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) image sensors, wireless transceivers, low power computing platforms/embedded systems and distributed computing. Many authors in the literature e.g. [1]-[3] have focused on different implementation strategies for performing vision Manuscript

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A new image size reduction model for an efficient visual sensor network

Kaljahi

Shivakumara

Idris

et al. 2019

Journal of Visual Communication and Image Representation

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Image size reduction for energy-efficient transmission without losing quality is critical in Visual Sensor Networks (VSNs). The proposed method finds overlapping regions using camera locations, which eliminate unfocussed regions from the input images. The sharpness for the overlapped regions is estimated to find the Dominant Overlapping Region (DOR). The proposed model partitions further the DOR into sub-DORs according to capacity of the cameras. To reduce noise effects from the sub-DOR, we propose to perform a Median operation, which results in a Compressed Significant Region (CSR). For non-DOR, we obtain Sobel edges, which reduces the size of the images down to ambinary form. The CSR and Sobel edges of the non-DORs are sent by a VSN. Experimental results and a comparative study with the state-of-the-art methods shows that the proposed model outperforms the existing methods in terms of quality, energy consumption and network lifetime.

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Detecting and coding region of interests in bi-level images for data reduction in Wireless Visual Sensor Network

Cited by 6 publications

References 11 publications

Binary video codec for data reduction in wireless visual sensor networks

Binary video codec for data reduction in wireless visual sensor networks

Bi-Level Video Codec for Machine Vision Embedded Applications

A new image size reduction model for an efficient visual sensor network

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