review lossless compression
DESCRIPTION
Journal Review: Lossless Compression using Huffman Code, LZW, and enhancement by using Retinex Algorithm.TRANSCRIPT
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.
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