This document summarizes image compression techniques. It discusses:
1) The goal of image compression is to reduce the amount of data required to represent a digital image while preserving as much information as possible.
2) There are three main types of data redundancy in images - coding, interpixel, and psychovisual - and compression aims to reduce one or more of these.
3) Popular lossless compression techniques, like Run Length Encoding and Huffman coding, exploit coding and interpixel redundancies. Lossy techniques introduce controlled loss for further compression.
This presentation explains the Transform coding in easiest method possible. The graphics and diagrammatic representations are worth looking for. Simple language is another pro.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
This presentation explains the Transform coding in easiest method possible. The graphics and diagrammatic representations are worth looking for. Simple language is another pro.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
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.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median Filters
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.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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 .
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/
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.
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.
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.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
2. Goal of Image Compression
The goal of image compression is to reduce the
amount of data required to represent a digital
image.
3. Data ≠ Information
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.
4. Data vs Information (cont’d)
The same amount of information can be represented
by various amount of data.
Ex1:
Your wife, Helen, will meet you at Logan Airport in
Boston at 5 minutes past 6:00 pm tomorrow night
Ex2:
Your wife will meet you at Logan Airport at 5 minutes
past 6:00 pm tomorrow night
Ex3:
Helen will meet you at Logan at 6:00 pm tomorrow night
7. Types of Data Redundancy
(1) Coding Redundancy
(2) Interpixel Redundancy
(3) Psychovisual Redundancy
Compression attempts to reduce one or more of these
redundancy types.
8. Coding Redundancy
Code: a list of symbols (letters, numbers, bits etc.)
Code word: a sequence of symbols used to represent a
piece of information or an event (e.g., gray levels).
Code word length: number of symbols in each code
word
9. Coding Redundancy (cont’d)
N x M image
rk: k-th gray level
P(rk): probability of rk
l(rk): # of bits for rk
Expected value:
12. Interpixel redundancy
Interpixel redundancy implies that pixel values are
correlated (i.e., a pixel value can be reasonably
predicted by its neighbors).
autocorrelation: f(x)=g(x)
13. Interpixel redundancy (cont’d)
To reduce interpixel redundancy, the data must be
transformed in another format (i.e., using a transformation)
e.g., thresholding, DFT, DWT, etc.
Example:
origin
al
thresholded
14. Psychovisual redundancy
The human eye does not respond with equal sensitivity
to all visual information.
It is more sensitive to the lower frequencies than to the
higher frequencies in the visual spectrum.
Idea: discard data that is perceptually insignificant!
15. Psychovisual redundancy (cont’d)
256 gray levels 16 gray levels 16 gray levels/random noise
C=8/4 = 2:1
i.e., add to each pixel a
small pseudo-random number
prior to quantization
Example: quantization
16. Fidelity Criteria
How close is to ?
Criteria
Subjective: based on human observers
Objective: mathematically defined criteria
20. Image Compression Models
The image compression system is composed of 2
distinct structural blocks: an encoder & a decoder.
Encoder performs Compression
Decoder performs Decompression.
21. The encoder is made up of a source encoder which removes
input redundancies, and a channel encoder, which increases the
noise immunity of the source encoder's output.
The de-coder includes a channel decoder followed by a source
decoder.
If the channel between the encoder and decoder is noise free (no
prone or error) the channel encoder and decoder are omitted.
Input image f(x,y) is fed into the encoder, which creates a
compressed representation of input.
It is stored for future for later use or transmitted for storage and
use at a remote location.
When the compressed image is given to decoder, a reconstructed
output image f’(x,…..) is generated.
The encoded input and decoder output are f(x, y) & f’(x, y) resp.
In video applications, they are f(x, y, t) & f’(x, y, t) where t is time.
22. 1-The Source Encoder and Decoder::
The source encoder is responsible for reducing or eliminating any
coding, interpixel and psychovisual redundancies in the input
image.
Encoder is used to remove the redundancies through a series of 3
independent operations.
Mapper:It transforms f(x,y) into a format designed to reduce
interpixel redundancies.
It is reversible
It may / may not reduce the amount of data to represent image.
Ex. Run Length coding
Quantizer: reduces the accuracy of the mapper's output in
accordance with some pre-established fidelity criterion. This stage
reduces the psychovisual redundancies of the input image.
This operation is irreversible.
23. Encoder
Symbol Encoder: Generates a fixed or variable length
code to represent the quantizer output and maps the
output in accordance with the code.
• In most cases, a variable-length code is used to represent
the mapped and quantized data set. It assigns the shortest
code words to the most frequently occurring output values
and thus reduces coding redundancy.
• It is reversible.
• Upon its completion, the input image has been processed
for the removal of all 3 redundancies.
25. Encoder
Quantizer: reduces the accuracy of the
mapper’s output in accordance with some pre-established
fidelity criteria.
26. Encoder
Symbol encoder: assigns the shortest code
to the most frequently occurring output
values.
27. Decoder
• Inverse operations are performed.
• But … quantization is irreversible in
general.
• Quantization results in irreversible loss,
an inverse quantizer block is not included
in the decoder block.
28.
29. Channel
2-The Channel Encoder and Decoder::
In the overall encoding-decoding process when the channel is noisy or
prone to error They are designed to reduce the impact of channel noise by
inserting a controlled form of redundancy into the source encoded data.
As the output of the source encoder contains little redundancy, it would
be highly sensitive to transmission noise without the addition of this
"controlled redundancy."
One of the most useful channel encoding techniques was devised by R.
W.Hamming (Hamming [1950]).
It is based on appending enough bits to the data being encoded to ensure
that some minimum number of bits must change between valid code
words.
Hamming(7,4) is a linear error-correcting code that encodes 4 bits of data
into 7 bits by adding 3 parity bits.
Hamming's (7,4) algorithm can correct any single-bit error, or detect all
single-bit and two-bit errors.
30. Elements of Information Theory
Measuring Information
The generation of information is modeled as a probabilistic
process. Random event E occurs with probability P(E)
I(E)=log= 1 =_ log (p(E))
P(E)
The base of the logarithm determines the units used to measure
the information. If the base 2 is selected the resulting
information unit is called bit. If P(E)=0.5 (two possible equally
likely events) the information is one bit
I(E) is called the self-information of E.
31. The Information Channel
Information channel is the physical medium that
connectsthe information source to the user of information.
Self-information is transferred between an information
source and a user of the information, through the
information channel.
Information source
Generates a random sequence of symbols from a finite or
countably infinite set of possible symbols.
Output of the source is a discrete random variable
32. The source :
Modeled as a discrete random variable
Source alphabet A={aj}
Symbols (letters) aj with probabilities P(aj)
33. A simple information system
Output of the channel is also a discrete random variable which
takes on values from a finite or countably infinite set of symbols
{b1, b2,…, b K} called the channel alphabet B
38. Estimating Entropy
The first-order estimate provides only a lower-bound on
the compression that can be achieved.
Differences between higher-order estimates of entropy
and the first-order estimate indicate the presence of
interpixel redundancy!
40. Estimating Entropy
Entropy of difference image:
Better than before (i.e., H=1.81 for original image)
However, a better transformation could be found
since:
42. Error-Free Compression
Some applications require no error in compression
(medical, business documents, etc..)
CR=2 to 10 can be expected.
Make use of coding redundancy and inter-pixel
redundancy.
Ex: Huffman codes, LZW, Arithmetic coding, 1D and 2D
run-length encoding, Loss-less Predictive Coding, and
Bit-Plane Coding.
43. Run-length encoding (RLE)
is a very simple form of data compression
stored as a single data value and count.
Ex: AAAABBCCCAA
Sol: 3A2B3C2A
44. Huffman Coding
Huffman Coding
The most popular technique for removing coding
redundancy is due to Huffman (1952)
A variable-length coding technique.
Optimal code (i.e., minimizes the number of code
symbols per source symbol).
Huffman Coding yields the smallest number of code
symbols per source symbol
Assumption: symbols are encoded one at a time!
49. The final message symbol narrows to [0.06752,
0.0688).
Any number between this interval can be used to
represent the message.
E.g. 0.068
3 decimal digits are used to represent the 5 symbol
message.
50. Fixed Length: LZW Coding
Error Free Compression Technique
Remove Inter-pixel redundancy
Requires no priori knowledge of probability
distribution of pixels
Assigns fixed length code words to variable length
sequences
Patented Algorithm US 4,558,302
Included in GIF and TIFF and PDF file formats
51. Coding Technique
A codebook or a dictionary has to be constructed
For an 8-bit monochrome image, the first 256
entries are assigned to the gray levels 0,1,2,..,255.
As the encoder examines image pixels, gray level
sequences that are not in the dictionary are
assigned to a new entry.
For instance sequence 255-255 can be assigned to
entry 256, the address following the locations
reserved for gray levels 0 to 255.
52. Example
Consider the following 4 x 4 8 bit image
39 39 126 126
39 39 126 126
39 39 126 126
39 39 126 126
Dictionary Location Entry
0 0
1 1
. .
255 255
256 -
511 -
53. 39 39 126 126
39 39 126 126
39 39 126 126
39 39 126 126
• Is 39 in the
dictionary……..Yes
• What about 39-
39………….No
• Then add 39-39 in entry 256
• And output the last
recognized symbol…39
Dictionary Location Entry
0 0
1 1
. .
255 255
256 39-39
511 -
54. Bit-Plane Coding
An effective technique to reduce inter pixel
redundancy is to process each bit plane
individually
The image is decomposed into a series of binary
images.
Each binary image is compressed using one of
well known binary compression techniques.
59. Decoding LZW
Use the dictionary for decoding the “encoded output”
sequence.
The dictionary need not be sent with the encoded
output.
Can be built on the “fly” by the decoder as it reads the
received code words.
60. Run-length coding (RLC)
(interpixel redundancy)
Encodes repeating string of symbols (i.e., runs) using
a few bytes: (symbol, count)
1 1 1 1 1 0 0 0 0 0 0 1 (1,5) (0, 6) (1, 1)
a a a b b b b b b c c (a,3) (b, 6) (c, 2)
Can compress any type of data but cannot achieve
high compression ratios compared to other
compression methods.
61. Bit-plane coding (interpixel
redundancy)
Process each bit plane individually.
(1) Decompose an image into a series of binary images.
(2) Compress each binary image (e.g., using run-length
coding)
62. Combining Huffman Coding
with Run-length Coding
Assuming that a message has been encoded using
Huffman coding, additional compression can be
achieved using run-length coding.
e.g., (0,1)(1,1)(0,1)(1,0)(0,2)(1,4)(0,2)
64. Lossy Compression
Transform the image into a domain where
compression can be performed more efficiently (i.e.,
reduce interpixel redundancies).
~ (N/n)2 subimages
65. Transform Selection
T(u,v) can be computed using various
transformations, for example:
DFT
DCT (Discrete Cosine Transform)
KLT (Karhunen-Loeve Transformation)
76. JPEG 2000
wavelet and sub-band technologies
Embedded Block Coding with Optimized
Truncation (EBCOT)
77. Features of JPEG2000
Lossless and lossy compression.
Protective image security.
Region-of-interest coding.
Robustness to bit errors.
78. JPEG-LS
latest ISO/ITU-T standard for lossless coding of still
images.
provides for “near-lossless” compression.
Part-I:
the baseline system, is based on:
adaptive prediction, context modeling and Golomb
coding.
Part-II (still under preparation).
Designed for low-complexity.
79. Video Compression Standards
MPEG-1
The driving focus of the standard was storage of multimedia
content on a standard CDROM, which supported data
transfer rates of 1.4 Mb/s and a total storage capability of
about 600 MB.
MPEG-2
Designed to provide the capability for compressing, coding,
and transmitting high quality, multi-channel, multimedia
signals over broadband networks.
ex: ATM.
MPEG-4
Digital television, interactive graphics and the World Wide
Web.