This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
A description about image Compression. What are types of redundancies, which are there in images. Two classes compression techniques. Four different lossless image compression techiques with proper diagrams(Huffman, Lempel Ziv, Run Length coding, Arithmetic coding).
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
A description about image Compression. What are types of redundancies, which are there in images. Two classes compression techniques. Four different lossless image compression techiques with proper diagrams(Huffman, Lempel Ziv, Run Length coding, Arithmetic coding).
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
Conference Proceedings of the National Level Technical Symposium on Emerging Trends in Technology, TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010
Conference Proceedings of the National Level Technical Symposium on Emerging Trends in Technology, TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010
Design and Implementation of EZW & SPIHT Image Coder for Virtual ImagesCSCJournals
The main objective of this paper is to designed and implemented a EZW & SPIHT Encoding Coder for Lossy virtual Images. .Embedded Zero Tree Wavelet algorithm (EZW) used here is simple, specially designed for wavelet transform and effective image compression algorithm. This algorithm is devised by Shapiro and it has property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. SPIHT stands for Set Partitioning in Hierarchical Trees. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images. The SPIHT algorithm was powerful, efficient and simple image compression algorithm. By using these algorithms, the highest PSNR values for given compression ratios for a variety of images can be obtained. SPIHT was designed for optimal progressive transmission, as well as for compression. The important SPIHT feature is its use of embedded coding. The pixels of the original image can be transformed to wavelet coefficients by using wavelet filters. We have anaysized our results using MATLAB software and wavelet toolbox and calculated various parameters such as CR (Compression Ratio), PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and BPP (Bits per Pixel). We have used here different Wavelet Filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal Filters .In this paper we have used one virtual Human Spine image (256X256).
REGION OF INTEREST BASED COMPRESSION OF MEDICAL IMAGE USING DISCRETE WAVELET ...ijcsa
Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. There are different ways of abbreviating image files. For the use of Internet, the two most common abbreviated graphic image formats are the JPEG formulation and the GIF formulation. The JPEG procedure is more often utilized or
photographs, while the GIF method is commonly used for logos, symbols and icons but at the same time
they are not preferred as they use only 256 colors. Other procedures for image compression include the
utilization of fractals and wavelets. These procedures have not profited widespread acceptance for the
utilization on the Internet. Abbreviating an image is remarkably not similar than the compressing raw
binary data. General-purpose abbreviation techniques can be utilized to compress images, the obtained
result is less than the optimal. This is because of the images have certain analytical properties, which can
be exploited by encoders specifically designed only for them. Also, some of the finer details of the image
can be renounced for the sake of storing a little more bandwidth or deposition space. In the paper,
compression is done on medical image and the compression technique that is used to perform compression
is discrete wavelet transform and discrete cosine transform which compresses the data efficiently without
reducing the quality of an image
Abstract
In today’s world of electronic revolution, digital images play an important role in consumer electronics. The need for storing images grows day to day. Compression of digital images plays an important role in storing the images. Due to compression the memory occupied by the images gets reduced and so that we can store more images in the available memory space. Also compression results in encoding of images which itself offer a security on transaction. This paper deals with a clear note on various aspects regarding compression and various compression techniques used and their implementation.
Keywords: digital image, compression, data redundancy, compression techniques.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Low Complex-Hierarchical Coding Compression Approach for Arial ImagesIDES Editor
Image compression is an extended research area
from a long time. Various compression schemes were
developed for the compression of image in gray scaling and
color image compression. Basically the compression is
focused for lossy or lossless color image compression based
on the need of applications. Where lossy compression are
higher in compression ratio but are observed to be lower in
retrieval accuracy. Various compression techniques such as
JPEG and JPEG-2K architectures are developed to realize
such and method. But with the need in high accuracy
retreivation these techniques[8] are not suitable. To achieve
nearby lossless compression[1] various other coding methods
were suggested like lifting scheme coding. This coding result
in very high retrieval accuracy but gives low compression
ratio. This limitation is a bottleneck in current image
compression architectures. So, there is a need in the
development of a compressing approach where both higher
compression as well as higher retrieval accuracy is obtained.
An Algorithm for Improving the Quality of Compacted JPEG Image by Minimizes t...ijcga
The Block Transform Coded, JPEG- a lossy image compression format has been used to keep storage and
bandwidth requirements of digital image at practical levels. However, JPEG compression schemes may
exhibit unwanted image artifacts to appear - such as the ‘blocky’ artifact found in smooth/monotone areas
of an image, caused by the coarse quantization of DCT coefficients. A number of image filtering
approaches have been analyzed in literature incorporating value-averaging filters in order to smooth out
the discontinuities that appear across DCT block boundaries. Although some of these approaches are able
to decrease the severity of these unwanted artifacts to some extent, other approaches have certain
limitations that cause excessive blurring to high-contrast edges in the image. The image deblocking
algorithm presented in this paper aims to filter the blocked boundaries. This is accomplished by employing
smoothening, detection of blocked edges and then filtering the difference between the pixels containing the
blocked edge. The deblocking algorithm presented has been successful in reducing blocky artifacts in an
image and therefore increases the subjective as well as objective quality of the reconstructed image.
IRJET-Lossless Image compression and decompression using Huffman codingIRJET Journal
S.Anitha"Lossless image compression and decompression using huffman coding", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
This paper propose a novel Image compression based on the Huffman encoding and decoding technique. Image files contain some redundant and inappropriate information. Image compression addresses the problem of reducing the amount of data required to represent an image. Huffman encoding and decoding is very easy to implement and it reduce the complexity of memory. Major goal of this paper is to provide practical ways of exploring Huffman coding technique using MATLAB .
This slide gives you the basic understanding of digital image compression.
Please Note: This is a class teaching PPT, more and detail topics were covered in the classroom.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
2. Disclaimer
Lecture by Kalyan Acharjya
2
All images/contents used in this presentation are
copyright of original owners.
The PPT has prepared for academic use only.
5. JPEG
Lecture by Kalyan Acharjya
Image Compression.
Size-270 KB Size-22 KB
“Without Compression a CD store only 200 Pictures or 8
Seconds Movie”
5
6. What is Image Compression?
Lecture by Kalyan Acharjya
Image compression is the process of reducing
the amount of data required to represent an
image.
Encoder 0101100111... Decoder
Original Image Decoded Image
Bitstream
6
8. Compression Fundamentals
Lecture by Kalyan Acharjya
Image compression involves reducing the size of image
data files, while retaining necessary information
Retaining necessary information depends upon the
application
Image segmentation methods, which are primarily a data
reduction process, can be used for compression
The ratio of the original, uncompressed image file and the
compressed file is referred to as the compression ratio
8
9. Why Compression?
Lecture by Kalyan Acharjya
Now, consider the transmission of video images, where we need
multiple frames per second, If we consider just one second of video
data that has been digitized at 640x480 pixels per frame, and requiring
15 frames per second for interlaced video, then:
Waiting 35 seconds for one second’s worth of video is not exactly real
time.
Even attempting to transmit uncompressed video over the highest
speed Internet connection is impractical
9
10. Image Compression General Models
Lecture by Kalyan Acharjya
Some image Compression Standard
JPEG-Based on DCT
JPEG 2000-Based on DWT
GIF-Graphics Interchange Format etc.
Source
Encoder
Channel
Encoder
Channel
Decoder
Source
Decoder
Channel/
Store
F(x, y)
F’(x, y)
10
11. Data ≠ Information
Lecture by Kalyan Acharjya
Data and information are not synonymous terms.
Data is the means by which information is conveyed.
Data compression aims to reduce the amount of data required
to represent a given quantity of information while preserving
as much information as possible.
Image compression is an irreversible process.
Some Transform used in Image Compression
DCT-Discrete Cosine Transform
DWT-Discrete wavelet Transform etc.
11
12. Lecture by Kalyan Acharjya
Compression Steps
Preparation: analog to digital conversion.
Processing: transform data into a domain easier to
compress.
Quantization: reduce precision at which the output is
stored.
Entropy Encoding: remove redundant information in the
resulting data stream.
Picture
Preparation
Picture
Processing
Quanti-
zation
Entropy
Encoding
Input Image Compressed Image
12
13. Image Compression-Lossy or Lossless
Lecture by Kalyan Acharjya
But its resolution or features should be unchanged for human
perception.
Relative Data Redundancy Rd of the first data set is Rd=1-1/CR
Where CR-Compression Ratio=n1/n2.
n1 and n2 denote the nos. of information carrying units in two data
sets that represent the same information.
• In Digital Image Compression, the basics data redundancies are-
Coding Redundancy
Inter pixel Redundancy
Psycho-visual Redundancy
13
14. Lecture by Kalyan Acharjya
14
Compression algorithms are developed by taking advantage of the
redundancy that is inherent in image data
Coding Redundancy
Occurs when the data used to represent the image is not utilized in
an optimal manner
Interpixel Redundancy
Occurs because adjacent pixels tend to be highly correlated, in
most images the brightness levels do not change rapidly, but
change gradually.
Psychovisual Redundancy
Some information is more important to the human visual system
than other types of information
Data Redundancies
16. Trade Off: Quality vs. Compression
Lecture by Kalyan Acharjya
16
Lossless Compression (Information Preserving) -
Original can be recovered exactly. Higher quality,
bigger.
Lossy Compression- Only an approximation of
the original can be recovered. Lower quality,
smaller.
17. Lecture by Kalyan Acharjya
17
Compression algorithms are developed by taking advantage of the
redundancy that is inherent in image data
Coding Redundancy
Occurs when the data used to represent the image is not utilized in
an optimal manner
Interpixel Redundancy
Occurs because adjacent pixels tend to be highly correlated, in
most images the brightness levels do not change rapidly, but
change gradually.
Psychovisual Redundancy
Some information is more important to the human visual system
than other types of information
Data Redundancies
18. Coding Redundancy
Lecture by Kalyan Acharjya
18
Length of the code words (e.g., 8-bit codes for grey value
images) is larger than needed.
Coding redundancy is associated with the representation of
information.
The information is represented in the form of codes.
If the gray levels of an image are coded in a way that uses
more code symbols than absolutely necessary to represent
each gray level then the resulting image is said to contain
coding redundancy.
21. Measuring Information
Lecture by Kalyan Acharjya
21
These methods, from information theory, are not limited to
images, but apply to any digital information. Here uses
“symbols” instead of “pixel values” and “sources” instead of
“images”
22. Shanon’s First Theorem
Lecture by Kalyan Acharjya
22
Shanon looked at group of n consecutive source symbols with a
single code word (rather than one code word per source symbol)
and showed that-
Where Lavg is the average number of code symbols required to
represents all n symbols groups.
27. Inter-Pixel Redundancy
Lecture by Kalyan Acharjya
27
Inter-Pixel Spatial Redundancy:
Inter-pixel redundancy is due to the correlation between the neighboring
pixels in an image.
The value of any given pixel can be predicated from the value of its
neighbors (Highly Correlated).
The information carried by individual pixel is relatively small.
To reduce inter-pixel redundancy the difference between adjacent pixels can
be used to represent an image.
Inter-Pixel Temporal Redundancy
Inter-Pixel temporal redundancy is the statistical correlation between pixels
from successive frames in video sequence.
Temporal redundancy is also called inter-frame redundancy.
Removing a large amount of redundancy leads to efficient video
compression.
Algorithm: Run Length Coding
28. Spatial Redundancy
Lecture by Kalyan Acharjya
28
Its Histogram (Ignore White Background)
Just variable length coding is not
sufficient?
0 255
29. Run Length Algorithm
Lecture by Kalyan Acharjya
29
Lets Discuss (During Lecture!)
Consider one Binary Image
Its vector representation
Size without compression
Size after run length algorithm
1 1 1 1 1 1
1 1 1 1 0 0
1 1 1 1 1 1
0 0 1 1 1 1
1 1 1 1 1 1
0 0 0 0 0 0
30. Which have higher intensity (Centre Circle)?
Lecture by Kalyan Acharjya
30
32. Psychovisual Redundancy
Lecture by Kalyan Acharjya
32
The Psychovisual redundancies exist because human perception does not
involve quantitative analysis of every pixel or luminance value in the image.
It’s elimination is real visual information is possible only because the
information itself is not essential for normal visual processing.
33. Psychovisual Redundancy
Lecture by Kalyan Acharjya
33
We’re more sensitive to differences between dark intensities than
bright ones. Encode log(intensity) instead of intensity.
We’re more sensitive to differences of intensity in green than red
or blue.
Use variable quantization: devote most bits to green, fewest to
blue.
34. Some Basic Compression Methods
Lecture by Kalyan Acharjya
34
Huffman coding (Will Discuss in this Lecture- Coding Redundancy)
Golomb Coding
Arithmetic Coding
LZW Coding
Run Length Coding (Already Discussed)
Symbol Based Coding
Bit Plane Coding (You are familiar)
….many more for detail:
Image Processing Gonzalez Book (Chapter 8-Image Compression)
38. Remember
Lecture by Kalyan Acharjya
38
To study some standard image compression methods
Like JPG, JPEG2000 etc.
Suggested Further Reading
Gonzalez & Woods, Digital Image
Processing Book
Chapter 8: Image Compression
39. Thank You!
Any Question Please?
kalyan.acharjya@gmail.com
kalyan5.blogspot.in
Lecture by Kalyan Acharjya 39
Suggested Further Reading
Gonzalez & Woods, Digital Image Processing Book
Chapter 8: Image Compression