Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks

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

doi:10.1117/12.923716

Cited by 12 publications

(19 citation statements)

References 17 publications

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“…The design goal for JBIG2 was to provide lossy compression with almost no visible degradation of quality and to provide a higher compression ratio as compared to other lossless compression schemes [36]. It offers better compression ratios as compared to the existing lossless compression schemes [4][29] [36]. A typical JBIG2 encoder decomposes the input bilevel image into several regions and codes these regions separately using a different coding method.…”

Section: Jbig2 Compression Methodsmentioning

confidence: 99%

“…Khursheed et al [4] have investigated the bi-level compression schemes on a personal computer (PC). However, the FPGA based embedded platform has different constraints and requirements as compared to a PC.…”

Section: Compression Analysismentioning

confidence: 99%

“…In a binary image, each pixel can have a maximum of two colours, which makes the characterization of the objects an easy task as compared to grayscale and coloured objects. The binary objects, after segmentation, can be characterized by means of shapes such as circles, semi-circles, quarters of circles, ellipses, semi-ellipses, and quarters of ellipses, rectangles and curves [4]. These shapes have been used in generations of statistical models.…”

Section: Image Generation Modelmentioning

confidence: 99%

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Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression

Imran

O’Nils

Lawal

2014

Proceedings of the International Conference on Distributed Smart Cameras

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Wireless Vision Sensor Nodes are considered to have smaller resources and are expected to have a longer lifetime based on the available limited energy. A wireless Vision Sensor Node (VSN) is often characterized to consume more energy in communication as compared to processing. The communication energy can be reduced by reducing the amount of transmission data with the help of a suitable compression scheme. This work investigates bilevel compression schemes including G4, G3, JBIG2, Rectangular, GZIP, GZIP_Pack and JPEG-LS on a hardware platform. The investigation results show that GZIP_pack, G4 and JBIG2 schemes are suitable for a hardware implemented VSN. JBIG2 offers up to a 43 percent reduction in overall energy consumption as compared to G4 and GZIP_pack for complex images. However, JBIG2 has higher resource requirement and implementation complexity. The difference in overall energy consumption is smaller for smooth images. Depending on the application requirement, the exclusion of a header can reduce the energy consumption by approximately 1 to 33 percent.

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Section: Jbig2 Compression Methodsmentioning

confidence: 99%

Section: Compression Analysismentioning

confidence: 99%

Section: Image Generation Modelmentioning

confidence: 99%

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Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression

Imran

O’Nils

Lawal

2014

Proceedings of the International Conference on Distributed Smart Cameras

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“…The architecture from 3 is shown in Figure 2. Based on this general architecture, we investigated the compression efficiency of the various bi-level image compression standards and presented the results in 4 . We concluded in 4 that, JBIG2 5 , CCITT Group4 6 and Gzip_pack 7 provide better compression efficiency (JBIG2 is developed by Joint Bi-level Image Experts Group i.e.…”

Section: Introductionmentioning

confidence: 99%

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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“…The architecture from [6] is shown in Figure 2. Based on this general architecture, we investigated the compression performance of various bi-level image compression standards in [7,18] and concluded that JBIG2 [8], CCITT Group 4 [9] and Gzip_pack [10] provide good compression efficiency…”

Section: Introductionmentioning

confidence: 99%

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

Khursheed

Ahmad

Imran

et al. 2012

2012 IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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Abstract-Wireless Visual Sensor Network (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field. The VSNs acquire images of the area of interest in the field, perform some local processing on these images and transmit the results using an embedded wireless transceiver. The energy consumption on transmitting the results wirelessly is correlated with the information amount that is being transmitted. The images acquired by the VSNs contain huge amount of data due to many kinds of redundancies in the images. Suitable bi-level image compression standards can efficiently reduce the information amount in images and will thus be effective in reducing the communication energy consumption in the WVSN. But compression capability of the bi-level image compression standards is limited to the underline compression algorithm. Further data reduction can be achieved by detecting Region of Interest (ROI) in the bilevel images and then coding these ROIs using bi-level image compression method. We explored the compression performance of the lossless ROI detection and coding method for various kinds of changes such as different shapes, locations and number of objects in the continuous set of frames. The CCITT Group 4, JBIG2 and Gzip are used for coding the detected ROIs. We concluded that CCITT Group 4 is a better choice for coding the ROIs in the Bi-level images because of its comparatively good compression performance and less computational complexity. This paper is intended to be a resource for the researchers interested in reducing the amount of data in the bi-level images for energy constrained WVSNs.

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Selection of bi-level image compression method for reduction of communication energy in wireless visual sensor networks

Cited by 12 publications

References 17 publications

Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression

Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression

Binary video codec for data reduction in wireless visual sensor networks

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

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