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Implementation of Fractal Image Compression with GUI” SUBMITTED in PARTIAL FULFILLMENT of the REQUIREMENT for THE VISVESVARAYA TECHNOLOGICAL UNIVERSITY Jnana Sangama, Belgaum, Karnataka-590 014 A FINAL YEAR PROJECT REPORT ON “Implementation of Fractal Image Compression with GUI” SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF DEGREE OF BACHELOR OF ENGINEERING IN ELECTRONICS AND COMMUNICATION ENGINEERING 2018-2019 Submitted by SNEHA S-1NH15EC108 THANUJA A-1NH15EC119 SREE RENUKAKSHITHA JM-1NH15EC112 Internal Guide Prof. Mr. Karthik CV Assistant Professor. Department of Electronics and Communication Engineering IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING CERTIFICATE It is certified that the Project work entitled “Implementation of Fractal Image Compression with GUI” carried out by SNEHA S (1NH15EC108),THANUJA A (1NH15EC119) and SREE RENUKAKSHITHA JM (1NH15EC112), we the bonafide students of New Horizon College of Engineering, Bengaluru, in partial fulfillment for the award of Bachelor of Engineering in Electronics and Communication Engineering of the Visvesvaraya Technological University, Belgaum during the year 2018-2019.It is further certified that all corrections/suggestions indicated for internal assessment has been incorporated in the report deposited in the department library. The Project Report has been approved as it satisfies the academic requirements in respect of Project Work prescribed for the Bachelor of Engineering degree. Signature of the Guide Signature of the HOD Signature of the Principal Mr.Karthik CV Dr. SANJEEV SHARMA Dr. MANJUNATHA Asst.Prof. Prof. and HOD-ECE Principal Dept. of Electronics and Dept. of Electronics and NHCE Communication Engineering.. Communication Engineering. External Viva Examiner Signature with date: 1. 2. 2 | P a g e DECLARATION We, SNEHA S (1NH15EC108) , THANUJA A (1NH15EC119) , SREE RENUKAKSHITHA JM (1NH15EC112) students of 8th semester in Electronics and Communication Engineering , New Horizon College of Engineering, Bengaluru hereby declare that the project work entitled “IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI “ submitted to VISVESVARAYA TECHNOLOGICAL UNIVERSITY during the academic year 2018-2019 , is a record of an original work done by us under the guidance of Mr.KARTHIK CV , Assistant Professor, Department of Electronics and Communication , New Horizon College of Engineering, Bengaluru . This project work is submitted in partial fulfillment of the requirement for the award of the degree of Bachelor of Engineering in Electronics and Communication. The result embodied in the thesis has not been submitted to any other University or Institute for the award of any degree. PLACE: BENGALURU DATE: SNEHA S THANUJA A SREE RENUKAKSHITHA JM 1NH15EC108 1NH15EC119 1NH15EC112 IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI ACKNOWLEDGEMENTS The satisfaction and exhilaration that accompany the successful completion of any task would be incomplete without the mention of people who made it possible, whose consistent guidance and encouragement crowned our efforts with success. We consider it as our privilege to express gratitude to all those who guided us in the completion of our project work. First and foremost,we wish to express our profound gratitude to our respected Chairman Dr. Mohan Manghnani,New Horizon College of Engineering,Bengaluru for giving us this opportunity. We wish to express our genuine gratitude to our Principal Dr. Manjunatha,New Horizon College of Engineering,Bengaluru,for providing us with the congenial environment to work in. We would like to express our sincere thanks to Dr.Sanjeev Sharma,the HOD of Electronics and Communication, New Horizon College of Engineering,for his continuous support and encouragement. We are greatly indebted to our guide Mr.Karthik CV,Assistant Professor, Department of Electronics and Communication, New Horizon College of Engineering,who took great interest in our project work.He motivated and guided us throughout the accomplishment of this goal.We express our profound thanks for his meticulous guidance. Last but not the least,heartfelt thanks to our parents and friends for direct and indirect support. Above all,we thank the Lord Almighty for his grace on us to succeed in this endeavor. 4 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI ABSTRACT Fractal image compression is a lossy compression method for digital images based on fractals rather than pixels,which are best suited for textures and natural images,it works on self-similarity property in various fractions of images,relying on the fact that parts of an image often ressemble other parts of the same image.It takes long encoding time and affects the image quality. This code introduces an improved model integrating quantized quad trees and Huffman coding used for fractal image compression.Quantized quad tree method divides the quantized original gray level and color image into various blocks depending on a threshold value besides the properties of the features presented in image.Huffman coding is applied for improving compression quality.Simulation results shows that the quantized quad trees and Huffman coding improved compression ratio and quality derived from the fractal image compression with range block.Different quantitative measures can be found by passing images of different format and dimensions. 5 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI TABLE OF CONTENTS CHAPTER 1 INTRODUCTION 1.1 OVERVIEW……………………………………………………………………….…………………………………9-10 1.1.1 COMPARISON OF LOSSY AND LOSSLESS METHODS………………………………10-11 1.1.2 COMPRESSION OF DIFFERENT IMAGE COMPRESSION FORMATS…………12-16 1.1.3 PURPOSE OF IMAGE COMPRESSION…………………………………………………….16 1.1.4 APPLICATIONS OF IMAGE COMPRESSION…………………………………………….17-18 CHAPTER 2 FRACTAL IMAGE COMPRESSION 2.1 ORIGIN OF FRACTAL IMAGE COMPRESSION……………………………………………………..19 2.2 INTRODUCTION………………………………………………………………………………………………..20 2.3 BASICS OF FRACTAL IMAGE COMPRESSION………………………………………………………20-21 2.3.1 FRACTALS-A BRIEF INTRODUCTION 2.3.1 SELF-SIMILARITY AND SELF-AFFINITY CHAPTER 3 LITERATURE SURVEY CHAPTER 4 SOFTWARE USED 6 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI 4.1 ABOUT THE SOFTWARE 4.2 FEATURES OF MATLAB 4.3 GUI IN MATLAB 4.4 USES OF MATLAB CHAPTER 5 METHODOLOGY 5.1 BLOCK DIAGRAM 5.2 QUADTREE PARTITIONING 5.3 HUFFMAN CODING 5.3.1 HUFFMAN ENCODING 5.3.2 HUFFMAN DECODING CHAPTER 6 RESULTS 6.1 PERFORMANCE CHARACTERISTICS 6.2 FRACTAL IMAGE COMPRESSION OUTPUT CHAPTER 7 CONCLUSION CHAPTER 8 FUTURE SCOPE REFERENCE APPENDIX 7 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI LIST OF FIGURES 1.1.2(a) Image formats-BMP,TIFF LWZ,PNG and JPEG 1.1.2(b) Difference of JPEG and JPEG 2000 for color images 4.1(a) Logo of Matlab 5.2(a) Shows an example of the quadtree partitioning of an image 5.2(b) Shows its corresponding quadtree 5.2(c) Shows the partitioning of the Lena image 5.2(d) Is the decode image of a dog using the quadtree algorithm. 6.2(a) Snapshot of the output screen 6.2(b) Snapshot of the results of a fractal image 6.2(c) Snapshot of the results of a fractal image of a lotus 6.2(d) Snapshot of the results of gray scale image of a baboon 6.2(e) Snapshot of the results of a color image of Lenna 6.2(f) Snapshot of the results of a satellite image of The Earth List of tables 6.2(a) Experimental Results 8 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI CHAPTER 1 INTRODUCTION 1.1 Overview Information that can be viewed is very important for us to identify, recognize and understand the surrounding world . An image consists of a two dimensional array of numbers. The shade of the image whether gray or color displayed for a given image(pixel) solely depends on the number that is stored in the array for the pixel. An image that takes large amount of data requires more memory to store, takes longer time to be transfered, and is difficult to process . Image compression becomes important due to the limit in the communication bandwidth, CPU speed, time taken for transmission and size required to store. The main aim of Image compression is to minimize the size of the image in bytes of a graphics file by maintaining a good quality of the image. Image compression is a challenging field in Image processing research. A common characteristic of most images is that the neighboring pixels are correlated and therefore contain redundant information. The foremost task then is to find less correlated representation of the image. Using the compression algorithms, redundant bits are removed from the image so that image size is reduced and the image is compressed. Two fundamental components of compression are redundancy and irrelevancy reduction . It needs to represent an image by removing the spatial and spectral redundancies as much as possible, while keeping the resolution and visual quality of the reconstructed image as close to the original image by taking advantage of these redundancies. An inverse process called decompression (decoding) is applied to the compressed data to get the reconstructed image . Image compression methods are classified into lossy and lossless compression. In lossless compression there is no information loss; the reconstructed image is exactly the same as the original, which is preferred for high value content, such as medical imagery or image scans made for archival purposes, artificial images such as technical drawings, icons or comics. Lossless compression methods include run length encoding, Huffman encoding, 9 | P a g e IMPLEMENTATION OF FRACTAL IMAGE COMPRESSION WITH GUI LZW (Lempel-Ziv-Welch) coding and area coding . In lossy compression, the reconstructed image contains degradation relative to the original because the compression scheme completely discards redundant information. However, lossy schemes are capable of achieving much higher compression. Under normal viewing conditions, no visible loss is perceived (visually lossless). Lossy compression is most commonly used to compress multimedia data like audio, video, and still images, especially in applications such as streaming media where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate, especially when used at low bit rates introduce compression artifacts.
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