VLSI based orthogonal diagonal cross hair search (ODCHS) algorithm implementation for efficient image compression
Introduction
The usage of digital pictures is continuously increasing each day in various fields such as medical, media and technological applications and also the dimension of such pictures are becoming larger and bigger. Apart from the usage of digital images, the main issue is that digital images are moderately memory consuming. Generally digital image is signified by digits matrix, which denotes the light intensity of each picture element named pixel per image the number of pixel is based on the essential spatial resolution while the total quantity of bits per pixel is resolved by quantization precision required for the application. For instance, a typical picture has 256 × 256 pixels, every entailing eight binary bits for gray scale level quantization. To transmit or store such pictures, which contains half a million bits of data, needs bandwidth capability or wide-ranging memory. The emerging production of pictures and demands to their quality requires high performance compression methods for efficient transmission, storage and archival. The Image compression scheme is mainly utilized to reduce the bits requirements for the image transmission. By applying the image reconstruction process, the original image is carried out from the image compression method. Image compression has wide-ranging applications in different healthcare fields namely: transmission of image for telemedicine, satellite communication, teleconferencing and so on.
The most commonly applied image compression method which is the most effective algorithm used in JPEG image compression is discrete wavelet transform. The suggested traditional approach needs more knowledge, region and power; lifting methodology is an innovative approach that implements both forward and backward, lifting-based discrete wavelet transform. DWT architectures are designed using a lift-based approach and are a efficient image compression algorithm. This architecture contributes to lower memory benchmarking, low power, low latency, and high performance.
Section snippets
Literature survey
An inventive scheme of discrete-color images for lossless compression is explained in Alzahir 2014 [1]. It consists of two important mechanisms. One is a fixed-size codebook incorporating the blocks in 8 × 8 bit of two-tone information alongside their related Huffman codes and their comparative probabilities of event. The probabilities were acquired from an especially the big data set of two color images and are utilized for arithmetic coding. The next mechanism is the reductions coding of
Lossless compression
In the field of the image compression, the lossless compression is considered as the major forms. Lossless compression is applied in the medical applications. In medical applications, the image without the data can be discarded. It is where lossless compression comes as a major tool. The compression ratios of lossless compression algorithms are usually small. The compression is of the order of 2:1. The compression for lossy algorithms is around the order of 300:1. The image can be compressed to
Integer wavelet transform
Integer wavelet transform is a category of DWT. Wavelet Transform is used to find the frequency content as well as the time of occurrence of the frequency content in the signal. The wavelet transform in its discrete notation called DWT can be applied to discrete images. After taking Discrete Wavelet Transform the band with least number of significant wavelet coefficients can be neglected. Such a band practically contains no information. In any image there will be several bands with least number
ODCHS algorithm
ODCHS stands for Orthogonal Diagonal Cross Hair Search. The algorithm searches for valid pixel in a range of Maximum and Minimum values. The maximum and minimum values are in powers of two. The maximum and minimum values are used as threshold for the entire encoding process. The algorithm divides the image into groups of 4 × 4,8 × 8,16 × 16,32 × 32 pixels and so on from the top left corner. For each block it is checked, if at least any one of the pixel in the block exceeds the threshold level
Simulation results
In this work, the two-Dimensional Integer Wavelet Transform is designed using Very high speed integrated input Hardware Description Language (VHDL). The validation of proposed design has been simulated by using ModelSim 6.3C and Synthesis results are evaluated by using Xilinx12.4i design tool family Virtex 6, device XC6VCX240T, package FF1156, speed −2.
In Fig. 4 shows the simulation results of Integer wavelet transform, is used to find the frequency content as well as the time of occurrence of
Conclusion
In this work, a lossless image compression was achieved by LWT. In the proposed method ODCHS algorithm used for image compression scheme is implemented in terms of the VLSI Design environment. It is used for searches the valid pixel in a range of Maximum and Minimum values. The maximum and minimum values are used as threshold for the entire encoding process. The pixels that are less than the threshold value are encoded with a value zero. In terms of the usage of hardware resources, power, and
Declaration of Competing Interest
None
R. Krishnaswamy received the B.E (ECE) and M.E (Communication Systems) From Anna University. He is working as an Assistant Professor in Department of ECE in University College of Engineering, Ariyalur. He is currently pursuing pH. D Degree in Image processing in the Department of Electronic and Communication Engineering in Anna University, Chennai. His-areas of interests are Communication systems and Image Processing.
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R. Krishnaswamy received the B.E (ECE) and M.E (Communication Systems) From Anna University. He is working as an Assistant Professor in Department of ECE in University College of Engineering, Ariyalur. He is currently pursuing pH. D Degree in Image processing in the Department of Electronic and Communication Engineering in Anna University, Chennai. His-areas of interests are Communication systems and Image Processing.