International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 8, August 2015 Lossy and lossless compression using combinational methods Ms. C.S Sree Thayanandeswari,M.E, MISTE, J Jeya Christy Bindhu Sheeba, Dept of ECE, Assistant Professor, Department of ECE, PET IIndM.E(C.S) PET Engineering College, Engineering College, Vallioor. Vallioor . Abstract—Image compression is the process of reducing succession which minimize the memory space needed the amount of data required to represent an image. to store images and transmission can be done with little Image Compression is used in the field of Broadcast amount of time. There are two types of image TV, Remote sensing, Medical Images. Many common compression. They are lossy and lossless image file formats are surveyed and the experimental results compression. Depending on the application and the of various states of lossy and lossless compression degree of compression any one of the two types can be algorithms are given .In the proposed method, image is chosen. Lossless compression is used where the exact compressed by using lossy and lossless methods for replica of the original image is to be produced. Lossy different types of images. Here, the lossy compression compression can be affected by the loss of data is done by the fractal decomposition code and lossless compared to the original image. The improvement of compression is done by using the LZW algorithm. this type is that it provides a scope for high LZW is the dictionary based algorithm, which is simple compression ratios than the lossless compression and can be used for the hardware applications. Fractal compression represents the image in a contractive form. Inspite of its lossy nature it can be used for the case of lossless compression. A general comparison is done Original Encoder based on analyzing the parameters such as Peak Signal image to Noise Ratio (PSNR), Mean Square Error(MSE), Image fidelity (IF), Absolute Difference (AD) to the different types of images. Channel IndexTerms Image compression, LZW, Fractal decomposition, mean square error. Recreated 1. INTRODUCTION Decoder image In the digitized world of today, the role played by computer and its applications are mandatory in each and every field. There are many fields which has the Fig1.Block diagram of image compression system wide variety applications of the audio, image and digital video processing. In order to handle more The most common characteristics of the number of data (images, videos) there is a requirement images are the nearby pixels are compared and then of large amount of space and a huge bandwidth for the they have the unwanted information. The first quest is process of transmission. The good solution for this to find reduced number of similar depiction of the problem is the compression of the images which reduce image. The two major elements of compression are the redundant information and increase the space. redundancy and reduction in irrelevancy. In this paper, LZW algorithm is capable of Reduction in redundancies aims in getting rid producing compressed images without having an effect of the mimeo from the source signal. Reduction in the on the quality of the image. This can be successfully irrelevancy neglects the part of the signal that is not brought about by reducing the total number of bits seen by the receiver or the Human Visual Display needed to constitute each pixel of an image. Thus, in System. ISSN: 2278 – 1323 All Rights Reserved © 2015 IJARCET 3419 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 8, August 2015 2. BLOCK DIAGRAM Input Image Compress by Decode by LZW BCH Compressed image Original Decompress by Encode by image LZW BCH Fig.2Block diagram for the proposed system The block diagram consists of the input image. 3.1 LZW ALGORITHM At first the input image is to be compressed by the LZW algorithm. In order to be compressed by LZW it The LZW algorithm is named after the must be transformed to binary image. The grey scale scientists Lempel, Ziv and Welch. It is a simple image is to be converted from the decimal value to dictionary based algorithm used for the lossless binary value. The binary image which is compressed by compression of images. Dictionary based algorithms LZW is then divided into blocks which have 7 bits are nothing but they are arranged in the form of each; since it wants only 7 bits to depict a byte. This is dictionary. The algorithm first searches the file and then known by the term decoding by BCH. Thus the it arranges the dates in sequences of strings which occur compressed image is obtained. repeatedly. The LZW algorithm then replaces the repeated text omitting the incoming text. If any one of Then the reverse process of decoding is to be the data is found to be new then it will add to the done to delete the extra added 7 bits. Then the result so dictionary. These words are then saved in the dictionary obtained is to be decompressed to get the binary image. and the references are added where the data gets To obtain the original image, the binary image is to be repeated. Each word in the dictionary has a particular transformed to grey scale image. code. The repeated words are replaced with another code. The length of the code must be a constant one. 3. PROPOSED METHOD The LZW algorithm is used where the file have more repeated strings. It s a computationally fast algorithm The proposed method uses a compression and is very effective, since the decompression does not methodology using the two lossless techniques LZW need the strings to be passed to the table. LZW along with Huffman coding and then the Discrete encoding is based on the multiplication of the encoded Cosine Transform (DCT). Next, along with these pixels. The principle involves in building the dictionary lossless techniques the proposed method also has the by substituting the patterns for the image given as input. lossy algorithm as fractal compression. Fractal The LZW algorithm can be applied to different types of compression algorithm removes some information from image formats which are used to remove the repeated the input image and the output given by the fractal strings. The BCH algorithm used along with the LZW method is not so clear. DCT algorithm produces a algorithm is to correct the errors or to find the errors. blurred output. LZW algorithm produces the result The size of the image file which is compressed by LZW which is same as that of the original image. The LZW algorithm along with BCH increased because it has algorithm is superior to other compression techniques. monochrome images. ISSN: 2278 – 1323 All Rights Reserved © 2015 IJARCET 3420 International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 8, August 2015 3.2 DISCRETE COSINE TRANSFORM approaches needed to compress the image known as contacting transformation. Then by dividing and DFT has a good computational efficiency but contacting the image by a transformation it is named as the designing of DFT is difficult and has poor energy fractal transformation or fractal decomposition. It is compaction. Energy compaction is nothing but the advantageous since it depicts the image in a contractive capacity to collect the energy of the spatial coordinates form. Fractal compression is a recent method on lossy in the frequency domain. Energy compaction is very compression based on the use of fractals which much important for image compression. Since the DCT degrades the likeliness of different parts of an image. does not save any bits and also doesnot introduce any distortion hence it can be quantized and used in lossless 5 PERFORMANCE CRITERIA FOR IMAGE compression. COMPRESSION The DCT works well in separating the image SNR: into different pixels of differing frequencies. So that it can be compressed without losing the major The standardized quantity of measuring the information. The edges and borders in the images image quality is the signal-to-noise ratio. It is given by compressed by DCT are clearly visible without any ratio of the power of the signal to the power of noise in blurs and distortion. In the processing of the image by the signal. SNR is given in decibels by DCT, the image is first broken into 8*8 blocks of σ2 pixels. Then from the top to bottom or left to right DCT 푆푁푅 푑푏 = 10 log x ⁡ is applied to each and every block of pixels. The blocks 10 MSE of pixels are compressed by the process of quantization. The compressed block of array which has the image is PSNR: stored in less space than the original image. To obtain The most common case of representing the the original image is done by the process of picture of the input image is given by the Peak value of decompression which can be done by Inverse Discrete SNR. It is defined as the ratio of the maximum power Cosine Transform (IDCT). DCT and ICDT are of the signal to the power of the corrupted noise signal. symmetric in nature. 2 Before applying DCT to the image the pixels 255 푃푆푁푅 푑푏 = 10 log10 are to be divided based in the black and white pixels. 푀푆퐸 The black and white pixels range from 0 to 255. The Where the value 255 is the peak in image signal. pure black pixels are denoted by 0 and pure white pixels are given by 255. This is the reason why the MSE: image looks like black and white or grey in color. An image contains thousands of 8*8 blocks in which the Mean square error is defined as the measure of compression is done in each and every block.