Li Wern Chew

Survey of image compression algorithms in wireless sensor networks

The implementation of image processing engines in visual sensor nodes has been a major concern in the development of wireless multimedia sensor networks in a hardware constrained environment. In this paper, a review on eight popular image compression algorithms is presented. After conducting a comprehensive evaluation, it is found that Set-Partitioning in Hierarchical Trees (SPIHT) wavelet-based image compression is the most suitable hardware implemented image compression algorithm in wireless sensor networks due to its high compression efficiency and its simplicity in coding procedures.

Low memory image stitching and compression for WMSN using strip-based processing

Due to the limited Field-Of-View (FOV) of a single camera, it is sometimes desired to extend the FOV using multiple cameras. Image stitching is one of the methods that can be used to exploit and remove the redundancy created by the overlapping FOV. However, the memory requirement and the amount of computation for conventional implementation of image stitching are very high. In this paper, this problem is resolved by performing the image stitching and compression in a strip-by-strip manner. First, the stitching parameters are determined by transmitting two reference images to an intermediate node to perform the processing. Then, these parameters are transmitted back to the visual node and stored in there. These parameters will be used to determine the way of stitching the incoming images in a strip-by-strip manner. After the stitching of a strip is done, it can be further compressed using a strip-based compression technique.

New Virtual SPIHT Tree Structures for Very Low Memory Strip-Based Image Compression

Images obtained with wavelet-based compression techniques such as set-partitioning in hierarchical trees (SPIHT) yield very good results. However, a lot of memory space is required as the wavelet coefficients for the whole image need to be stored for the process of set-partitioning coding. In this letter, we propose new virtual SPIHT tree structures for very low memory strip-based image compression. The advantage of the proposed work is that it reduces the memory requirements for practical software and hardware implementations significantly without sacrificing performance.

Very low-memory wavelet compression architecture using strip-based processing for implementation in wireless sensor networks

This paper presents a very low-memory wavelet compression architecture for implementation in severely constrained hardware environments such as wireless sensor networks (WSNs). The approach employs a strip-based processing technique where an image is partitioned into strips and each strip is encoded separately. To further reduce the memory requirements, the wavelet compression uses a modified set-partitioning in hierarchical trees (SPIHT) algorithm based on a degree-0 zerotree coding scheme to give high compression performance without the need for adaptive arithmetic coding which would require additional storage for multiple coding tables. A new one-dimension (1D) addressing method is proposed to store the wavelet coefficients into the strip buffer for ease of coding. A softcore microprocessor-based hardware implementation on a field programmable gate array (FPGA) is presented for verifying the strip-based wavelet compression architecture and software simulations are presented to verify the performance of the degree-0 zerotree coding scheme.

Low-memory video compression architecture using strip-based processing for implementation in wireless multimedia sensor networks

This paper presents a very low-memory video compression architecture for implementation in a wireless multimedia sensor network. The approach employs a strip-based processing technique where a group of image sequences is partitioned into strips, and each strip is encoded separately. A new one-dimensional, memory-addressing method is proposed to store the wavelet coefficients at predetermined locations in the strip buffer for ease of coding. To further reduce the memory requirements, the video-coding scheme uses a modified set-partitioning in hierarchical trees algorithm to give a high compression performance. The proposed work is implemented using a soft-core microprocessor-based approach. Simulation tests conducted have verified that even though the proposed video compression architecture using strip-based processing requires a much less complex hardware implementation and its efficient memory organisation uses a lesser amount of embedded memory for processing and buffering, it can still achieve a very good compression performance.

Wireless multimedia sensor networks onrReconfigurable hardware: information reduction techniques

Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs). This introduces practical and research challenges, because multimedia sensors, particularly image sensors, generate huge amounts of data to be processed and distributed within the network, while sensor nodes have restricted battery power and hardware resources. This book describes how reconfigurable hardware technologies such as field-programmable gate arrays (FPGAs) offer cost-effective, flexible platforms for implementing WMSNs, with a main focus on developing efficient algorithms and architectures for information reduction, including event detection, event compression, and multicamera processing for hardware implementations. The authors include a comprehensive review of wireless multimedia sensor networks, a complete specification of a very low-complexity, low-memory FPGA WMSN node processor, and several case studies that illustrate information reduction algorithms for visual event compression, detection, and fusion. The book will be of interest to academic researchers, R&D engineers, and computer science and engineering graduate students engaged with signal and video processing, computer vision, embedded systems, and sensor networks.

Reduced Memory SPIHT Coding Using Wavelet Transform with Post-Processing

Traditional wavelet-based image coding applies the discrete wavelet transform (DWT) on an image using filter banks over rings of characteristic zero. As the level of the DWT decomposition increases, the number of bits needed to represent the wavelet coefficients also increases. A significant amount of memory is needed to store these wavelet coefficients especially when the level of DWT decomposition is high. In this paper, a post-processing method is proposed to set the amplitude of the wavelet coefficients to pre-defined N-bits. The Set-Partitioning in Hierarchical Trees (SPIHT) coding is then performed to encode these coefficients to achieve compression. The main advantage of our proposed work is the significant reduction in memory requirements for wavelet coefficients storage during bit-plane coding. Simulation results show that our proposed SPIHT coding using wavelet transform with post-processing gives an equally good compression performance when M-3 ≤ N ≤ M-1 where M and N are the number of bits needed to represent the largest wavelet coefficient without and with post-processing respectively.

Wireless Multimedia Sensor Network Technology

This chapter presents background material for wireless multimedia sensor network (WMSN) technology. The chapter will describe the general structure for a WMSN and various architectures and platform classifications for WMSNs. The chapter will also discuss the various components in a WMSN node such as the sensing, processing, communication, power and localisation units. The efficient processing of information in a WMSN is of primary importance, and the chapter will discuss various multi-camera network models and information reduction techniques such as event detection and event compression. The chapter concludes with a discussion of applications of WMSNs.

Low Memory Strip-Based Image Compression for Color Images

In this paper, a low memory strip-based image compression for color images using the set-partitioning in hierarchical trees (SPIHT) coding is presented. The proposed coding scheme applies a lower scale of discrete wavelet decomposition and uses a new spatial orientation tree structure to achieve a low memory strip-based coding implementation. It also incorporates a modified SPIHT algorithm which uses a degree-0 to degree-2 zerotree coding methodology to increase the compression efficiency. The main advantage of the proposed work is the significant reduction in both the memory requirements as well as the complexity of the image coder for practical software and hardware implementation. Simulation results on color images also show that the proposed coding scheme gives a better compression performance compared to the traditional SPIHT coding at most bit-rates.

Motion Estimation Algorithm Using One-Bit-Transform with Smoothing and Preprocessing Technique

A high performance 2D one-bit-transform (1BT) motion estimation algorithm with smoothing and preprocessing (S + P) is introduced in this paper. The 1BT technique is used to transform an 8-bit image into a 1-bit representation image (1BT image). In the 1BT motion estimation algorithm, the 8-bit current frame (c frame) and reference frame (p frame) are first transformed into their 1BT image respectively, before calculating the Sum of Absolute Difference (SAD) and performing the search operations using the Full Search Block Matching Algorithm (FSBMA). In our proposed algorithm, a smoothing threshold (ThresholdS) is incorporated into the filtering kernel, which is used to perform the transformation from 8-bit image into the 1BT image. The smoothing technique can greatly reduce the scattering noise created in the 1BT image. This will help to improve the accuracy when performing the search operations. After the transformation, the 1BT image for the c frame and p frame is divided into number of macroblocks. The macroblock in the c frame will be first compared to the macroblock at the same position in the p frame. If the SAD is below the preprocessing threshold (ThresholdP), the macroblock is considered to have negligible movement and search operation is not required. This preprocessing technique can greatly reduce the total number of search operations. Simulation results show that an improvement up to 0.65 dB, with reduction in search operation up to 95.07% is achieved. Overall, the proposed S + P technique is very suitable to be used in applications such as video conferencing and monitoring.