Huffman Based LZW Lossless Image

Compression Using Retinex

Algorithm

International Journal of Advanced Research in Computer and Communication Engineering

Vol. 2, Issue 8, August 2013

Review oleh: Mega Satya C / 136060300111018

Sistem Komunikasi dan Informasi Fakultas Teknik ElektroUniversitas Brawijaya Malang

2013

Dalvir Kaur.

Referensi

• Judul Jurnal:Huffman Based LZW Lossless Image Compression Using Retinex Algorithm.

• Penulis:

Dalvir Kaur(Master of Technology in Computer Science & Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, IndiaMaster of Technology in Computer Science & Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India)

Image Compression

• Encodes original image with few bits• Reduce irrelevance and redundancy

of the image data• Can reduce the transmit time over

the network and increase the speed of transmission

Abstraksi

Lossless Compression and Propose Technique

• No data loss• 1. Compress image with Huffman coding• 2. LZW Coding and Decoding• 3. Retinex Algorithm to enhance the contrast of

image and improve the quality of image• To increase the Compression Ratio (CR), Peak

Signal of Noise Ratio (PSNR), and Mean Square Error (MSE)

• Use Matlab Software

Abstraksi

Image Compression

• Means: the reduction of the size of image data, while retraining necessary information

• Mathematically: transforming a 2D pixel array into a statically uncorrelated data set

• So, image compression is used to minimize the amount of memory needed to represent an image

Introduction

Block Diagram of Image Compression System

Introduction

Fundamental Image Compression

• Neighbouring pixels of image are correlated and therefore contain redundant information

Fundamental components of Image Compression

Redundancies Reduction

Irrelevancy Reduction

Removing duplication from signal source (image/video)

Removing duplication from signal source (image/video)

Introduction

Three types of image redundancies

• Coding Redundancy : fewer bits to represent frequently occuring symbols

• Interpixel Redundancy: neigbouring pixels have almost same value

• Psycho visual redundancy: Human visual system cannot simultaneously distinguish all colors

Introduction

Need of Image Compression

• To create faster loading web pages (for Web Designers)• Save lof of unnecessary bandwidth• Attach photos to email , send the email more quickly and

save bandwidth cost• Store more images in hard disk saving memory space

Introduction

Suppose we need to download a digitized color photograph over a computer's 33.6 kbps modem. If the image is not compressed

(a TIFF file, for example), it will contain about 600 kilo bytes of data.

Image Compression Coding

• is to store the image into bit-stream as compact as possible and to display the decoded image in the monitor as exact as possible

Introduction

The basic flow of image compression coding

The basic flow of image

compression coding

• The encoder receives the original images• Image file will be converted into a series of

binary data (bit stream)• The decoder receives the encoded bit stream• The decoder decodes it to form decoded

image• The total data quantity of decoder image

fewer than the original image image compression

Introduction

Types of Compression• Lossy coding techniques the reconstructed image

contains degradation relative to the original image

• Lossless coding techniques the reconstructed image is numerically indentical to the original image

Introduction

Transformation coding, Vector Quantization, Fractal Coding, Block Truncation coding, Sub Band Coding

Run Length Enoding, Huffman Encoding, LZW Coding, Area Coding

Algorithm

Proposed Work

Step 1Read the image on to the workspace of Matlab

Step 2Call a function to find the symbols (pixal value)

Step 3Call a function to calculate the probability of each symbol

Step 4Arrange probability symbols in decreasing order. Lower probabilities are merged.Continue this step until only two probabilities are left. Code are assignedAccording to rule that the highest probable symbol will have a shorter length code

Step 5Mapping of the code words to the corresponding symbols. Result: Huffman codeword’s

Proposed Work

Algorithm (continued)

Step 6Concatenate all the Huffman code words and apply LZW encoding. Result:LZW dictionary and final encoded values (compressed data)

Step 7Apply LZW decoding process on final encoded values and output the Huffman code words

Step 8Apply Huffman encode value on LZW encoding process

Step 9In final apply the Multiscale Retinex Algorithm on compressed image to enhanceThe quality and color of the image

Step 10In last step the recovered image is generated

Flowchart of Huffman based LZW Lossless Image

Compression sing Retinex Algorithm

Proposed Work

Huffman Coding and Decoding Process

• Use the probability distribution of the alphabet of the source to develop the code words for symbols.

Proposed Work

Proposed Work

Flowchart:

Huffman Algorithm

LZW Coding and Decoding

• The algorithm : scan a file and search the sequence of data or string that occur more than once in a file

• LZW replacing strings of characters with single codes without doing any analysis of the incoming data

Proposed Work

LZW Encoding

Proposed Work

LZW Decoding

Proposed Work

Image Enhancement using Retinex

Algorithm• Retinex : “Retina” and “Cortex” abbreviate

• Retinex balance three aspects in compress the dynamic range of grayscale, edge enhancement, and color constancy

• Basic principles: brightness image and reflection image

Proposed Work

Retinex Provides:• Great dynamic compression

• Increased sharpness and color

• Fast algorithm that is used with different color bands

Proposed Work

Retinex:

• Single-scale Retinex (SSR)

• Multiscale Retinex (MSR)

• Multiscale Retinex for Color Restoration (MSRCR)

Multiscale Retinex Algorithm

• N = number of scales

• Rni = the ith component of the nth scale

• Wn = weight values

Proposed Work

The Graphical User Interface

Result

Result

Lena Image

House Image

Result

• Different algorithms have been evaluated in terms of the amount of compression they provide, algorithm efficiency, and susceptibility to error

• Reproduced image and the original image are equal in quality by using Retinex Algorithm, as it enhances the image contrast using MSR

Conclusion

• Improvement of compression ratio using the new techniques

• The proposed technique can be experimented on different kinds of data sets (audio, video, text) as till now it is restricted to image

• New methods can be combined and proposed that decreases the time complexity incurred in creating dictionary in LZW algorithm

• Larger dataset could be a subject for future research

Future Scope

Reviewer Suggestions

• Sebaiknya dijelaskan tipe data citra yang digunakan untuk percobaan, serta ukuran citraasli dan hasil kompresi.

• Dari hasil pengujian, sebaiknya dijelaskan artipencapaian nilai rasio kompresi, MSE, danPSNR yang dihasilkan dari penerapan metodekompresi.

End..