A Comparative Study on Improvement of Image Compression Method using Hybrid DCT-DWT Techniques with Huffman Encoding for Wireless Sensor Network Application

Abstract

Nowadays, the demands on the usage of wireless network are increasing rapidly from year to year. Wireless network is a large scale of area where many nodes are connecting to each other to commun icate using a device. Primarily, wireless network also tend to be as a link to transmit and receive any multimedia such as image, sound, video, document and etc. In order to receive the transmitted media correctly, most type of media must be compressed before being transmitted and decompressed after being received by the device or else the device used must have the ability to read the media in a compressed way. In this paper, a hybrid compression of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with Huffman encoding technique are proposed for Wireless Sensor Network (WSN) application. Data compression is very useful to remove the redundant data and reduce the size of image. After conducting a comprehensive observation, it is found that hybrid compression is suitable due to the process consist of the combination of multiple compression techniques which suits for Wireless Sensor Network's application focusing on ZigBee platform.

Comparative Study on Performance of Discrete Wavelength Transform and Huffman Compression Technique on 2D Signal

“…Basically, DWT technique applied a filtering concept which divides the input image data into low frequency signal and high frequency signal. The data of high frequency is removed and only use the low frequency data [9]. This technique shows the loss of data during compression process which makes it classified in lossy compression.…”

A Comparative Study on Image Compression Method Using Stand-Alone DWT Techniques and Hybrid of DWT with Huffman Coding Technique for WSN Application

“…Huffman coding is an entropy-based algorithm which are capable in breaking down a long data strings into shorter ones [8]. Furthermore, image compression which uses the Huffman coding technique has been improved for the last three decades due to its advantage which categorized in lossless technique [12].…”

“…A 2-Dimensional (2D) DWT techniques applied the concept of filtering process and minimize the complexities in its architecture [8]. Besides, it transform the image between time and frequency domain which an appropriate adaptive band are selected based on signal characteristic and improve the time-frequency resolution whenever the frequency band match the signal spectrum [19].…”

“…There are many levels of DWT where it depends on the filtering stage of the transformation's occurrence can be made. Based on Figure 2, the selected image are divided into half which carry out the process of High Pass Filter (HPF) and Low Pass Filter (LPF) [8]. The process of separating image into half which based on row and column wise filter to produce smaller sub-bands are also known as level of decomposition [20].…”

“…A high quality of image usually contain a large storage capacity and required more time to transmit an image without any loss. Thus, other than denoising an image [7], image compression plays an important role to reduce the size of image data before transmitted and improve the computing time [8] to receive the image data with lowest amount of data loss. Hence, a suitable method of compression technique are proposed to investigate the effectiveness of compressing an image to reduce the data capacity in storing and transmitting the image.…”