This document discusses text compression algorithms LZW and Flate. It describes LZW's dictionary-based encoding approach and provides examples of encoding and decoding a string. Flate compression is explained as combining LZ77 compression, which finds repeated sequences, and Huffman coding, which assigns variable length codes based on frequency. Flate can choose between no compression, LZ77 then Huffman, or LZ77 and custom Huffman trees. The advantages of LZW include lossless compression and not needing the code table during decompression, while its disadvantage is dictionary size limits. Flate provides adaptive compression and lossless compression but has overhead from generating Huffman trees and complex implementation.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Presentation given in the Seminar of B.Tech 6th Semester during session 2009-10 By Paramjeet Singh Jamwal, Poonam Kanyal, Rittitka Mittal and Surabhi Tyagi.
Audio Compression Techniques
a type of lossy or lossless compression in which the amount of data in a recorded waveform is reduced to differing extents for transmission respectively with or without some loss of quality, used in CD and MP3 encoding, Internet radio.
Dynamic range compression, also called audio level compression, in which the dynamic range, the difference between loud and quiet, of an audio waveform is reduced
Originally presented at DesignCon 2013.
Jitter is a very important topic in signal integrity for high speed serial data links. The jitter performance of clock signals used in generating the serial data signal is critical to the overall performance of these signals.
Phase noise is the most sensitive and accurate measurement of the performance of precision clocks.
This presentation covers the theory and practice for making phase noise measurements on clock signals as well as the relationship between phase noise and total jitter, random jitter and deterministic jitter. Measurements on a typical clock signal is also included.
For more information, visit http://rohde-schwarz-scopes.com or call (888) 837-8772 to speak to a local Rohde & Schwarz expert.
The attached narrated power point presentation offers a block level and an elementary level mathematical treatment of optical communication systems employing coherent detection. The material will immensely benefit KTU final year B Tech students who prepare for the subject EC 405, Optical Communications.
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.
In digital modulation, minimum-shift keying(MSK) is a type of continuous-phase frequency-shift keying that was developed in the late 1950s and 1960s.
Similar to OQPSK(Offset quadrature phase-shift keying),
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
Presentation given in the Seminar of B.Tech 6th Semester during session 2009-10 By Paramjeet Singh Jamwal, Poonam Kanyal, Rittitka Mittal and Surabhi Tyagi.
Audio Compression Techniques
a type of lossy or lossless compression in which the amount of data in a recorded waveform is reduced to differing extents for transmission respectively with or without some loss of quality, used in CD and MP3 encoding, Internet radio.
Dynamic range compression, also called audio level compression, in which the dynamic range, the difference between loud and quiet, of an audio waveform is reduced
Originally presented at DesignCon 2013.
Jitter is a very important topic in signal integrity for high speed serial data links. The jitter performance of clock signals used in generating the serial data signal is critical to the overall performance of these signals.
Phase noise is the most sensitive and accurate measurement of the performance of precision clocks.
This presentation covers the theory and practice for making phase noise measurements on clock signals as well as the relationship between phase noise and total jitter, random jitter and deterministic jitter. Measurements on a typical clock signal is also included.
For more information, visit http://rohde-schwarz-scopes.com or call (888) 837-8772 to speak to a local Rohde & Schwarz expert.
The attached narrated power point presentation offers a block level and an elementary level mathematical treatment of optical communication systems employing coherent detection. The material will immensely benefit KTU final year B Tech students who prepare for the subject EC 405, Optical Communications.
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.
In digital modulation, minimum-shift keying(MSK) is a type of continuous-phase frequency-shift keying that was developed in the late 1950s and 1960s.
Similar to OQPSK(Offset quadrature phase-shift keying),
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
Data compression refers to reducing the amount of space needed to store data or reducing the
amount of time needed to transmit data. Many data compression techniques allow encoding the
compressed form of data with different compression ratio. In particular, in the case of LZ77
technique, it reduces the data concurrency of an input file. In the output of this technique it conveys
more information that is actually not needed in practical. Removing the extra information from the
encoded file that makes this algorithm more optimal. Our task is to identify how much extra
information it conveys and how can we minimize it so that there is no trouble at the time of
decoding. Basically the encoded output of LZ77 is the sequence of triplets (a structure of encoded
output) that is in binary and having fix size. For making the triplets of fix size, sometimes we are
creating unnecessary information. We present the method of variable triplet size as a way to improve
LZ77 compression and demonstrate it through many experiments. In our optimization algorithm we
are getting more compression ratio compare to the conventional LZ77 data compression algorithm.
Lempel–Ziv–Welch (LZW) is a universal lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch. It was published by Welch in 1984 as an improved implementation of the LZ78 algorithm published by Lempel and Ziv in 1978. The algorithm is simple to implement, and has the potential for very high throughput in hardware implementations.
It is the algorithm of the widely used Unix file compression utility compress, and is used in the GIF image format.
Comparison of various data compression techniques and it perfectly differentiates different techniques of data compression. Its likely to be precise and focused on techniques rather than the topic itself.
In computer science and information theory, data compression, source coding,[1] or bit-rate reduction involves encoding information using fewer bits than the original representation.[2] Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2. Index
1. Introduction to Data Compression
2. Introduction to Text Compression
3. LZW
3.1 LZW Encoding Algorithm
3.2 Encoding a String Example
3.2 LZW Decoding Algorithm
3.3 Decoding a String Example.
4. Flate Compression
4.1 Decomposition
4.1.1 Huffman Coding
4.1.2 LZ77 Compression
4.1.3 Putting both together
5. Advantages and Disadvantages
5.1 LZW
5.2 Flate
6. Conclusion
3. 1. Introduction to Data
Compression
Encoding information using fewer bits than the
original representation.
Data Compression is achieved when redundancies are
reduced or eliminated
Lossless where no information is lost.
Lossy where some information is lost.
Compression reduces the data storage space.
4. Introduction to Data
Compression…. Contd.
Reduces transmission time needed over the network.
Data must be decompressed or decoded to be reused.
Symmetrical or Asymmetrical
Software or Hardware
5. 2. Introduction to Text
Compression
The compression of Text based data.
Major difference between Text and Image compression.
Databases, binary programs, text on one side and sound,
image, video signals on the other.
Text compression needs Losseless Compression.
Needed in literary works, product catalogues, genomic
databases, raw text databases.
6. 3. LZW (Lempel-Ziv-Welch)
Starts with a dictionary of all the single characters and gradually
builds the dictionary as the information is sent through.
Lossless compression hence works good for text compression.
A dictionary or code table based encoding algorithm.
Uses a code table with 4096 as a common choice for number of
entries.
It tries to identify repeated sequences of data and adds them to
the code table.
7. LZW (Lempel-Ziv-Welch)….contd.
A general compression algorithm capable of working
on almost any type of data.
Large size Text files in English language can be
typically be compressed to half it’s size.
Used in GIF (Graphics Interchange Format) to reduce
the size without degrading the visual quality.
8. 3.1 LZW Encoding Algorithm
1. STRING = get input character
2. WHILE not end of input stream DO
3. CHARACTER = get input character
4. IF STRING+CHARACTER is in the string table then
5. STRING = STRING+CHARACTER
6. ELSE
7. Output the code for STRING
8. add STRING+CHARACTER to the STRING table
9. STRING = CHARACTER
10. END of IF
11. END of WHILE
12. Output the code for STRING
10. 3.2 Encoding a String example
To encode a string of characters
1. First Generate a initial dictionary of single characters
Symbol Binary Decimal
# 00000 0
A 00001 1
B 00010 2
C 00011 3
D 00100 4
E 00101 5
Contd……..
upto Z
11. Encoding a String Example …..contd
2. Example TOBEORNOTTOBEORTOBEORNOT
Current Output
Next Char Extended Dictionary Comments
Sequence Code Bits
NULL T
T O 20 10100 27: TO 27 = first available code after 0 through 26
O B 15 01111 28: OB
B E 2 00010 29: BE
E O 5 00101 30: EO
O R 15 01111 31: OR
32 requires 6 bits, so for next output use 6
R N 18 10010 32: RN
bits
N O 14 001110 33: NO
O T 15 001111 34: OT
T T 20 010100 35: TT
TO B 27 011011 36: TOB
BE O 29 011101 37: BEO
12. Encoding a String Example …..contd
TO B 27 011011 36: TOB
BE O 29 011101 37: BEO
OR T 31 011111 38: ORT
TOB E 36 100100 39: TOBE
EO R 30 011110 40: EOR
RN O 32 100000 41: RNO
# stops the algorithm;
OT # 34 100010
send the cur seq
0 000000 and the stop code
13. 3.3 LZW Decoding Algorithm
1. Read OLD_CODE
2. output OLD_CODE
3. CHARACTER = OLD_CODE
4. WHILE there are still input characters DO
5. Read NEW_CODE
6. IF NEW_CODE is not in the translation table THEN
7. STRING = get translation of OLD_CODE
8. STRING = STRING+CHARACTER
9. ELSE
10. STRING = get translation of NEW_CODE
11. END of IF
12. output STRING
13. CHARACTER = first character in STRING
14. add OLD_CODE + CHARACTER to the translation table
15. OLD_CODE = NEW_CODE
16. END of WHILE
15. 3.4 Decoding a String Example
To decode an LZW-compressed archive, one needs to know
in advance the initial dictionary used, but additional
entries can be reconstructed as they are always simply
concatenations of previous entries.
Input New Dictionary Entry
Output
Comments
Bits Code Sequence Full Conjecture
10100 20 T 27: T?
01111 15 O 27: TO 28: O?
00010 2 B 28: OB 29: B?
00101 5 E 29: BE 30: E?
01111 15 O 30: EO 31: O?
created code 31 (last to fit
10010 18 R 31: OR 32: R?
in 5 bits)
so start reading input at 6
001110 14 N 32: RN 33: N?
bits
16. 4. Flate Compression
A lossless data compression.
Can discover and exploit many patterns in the input
data.
An improvement over LZW compression, Flate
encoded data is usually much more compact than
LZW encoded output.
It was originally defined by Phil Katz for version 2 of
his PKZIP archiving tool and was later specified in RFC
1951.
Used in PDF compression, Adobe uses a Flate
compression tool for PDF files.
17. 4.1 Decomposition
Flate specifications defines a lossless data format that
compresses data using a combination of LZ77 algorithm
and Huffman coding.
Hence the format can be implemented readily in a manner
not covered by patents.
The manner in which these two algorithms work are
explained below and then the combination of the two
which work to produce Flate compression.
18. 4.1.1 Huffman Coding
A type of entropy encoding algorithm.
Used for lossless data compression.
Can be used to generate variable-length codes.
The variable length codes are generated based on the
frequency of the occurrence of the characters.
The idea of assigning shortest code to the character
with the highest probability of occurrence.
19. Huffman Coding…. contd.
The algorithm starts by assigning each element a
‘weight’ a number that represents the relative
frequency within the data to be compressed.
Taking an example for the set of weights {1,2,3,3,4}
1. They are assigned to be the nodes or leaves of the
Huffman tree to be formed
20. Huffman Coding…. contd.
2. During the first step, the two nodes with weights
(highest priority OR lowest probability) 1 and 2 are
merged, to create a new tree with a root of weight 3.
21. Huffman Coding…. contd.
3. Now we have three nodes with weights 3 at their
roots, so choosing one of the 3 weighted node.
22. Huffman Coding…. contd.
4. Now our two minimum trees are the two singleton
nodes of weights 3 and 4. We will combine these to
form a new tree of weight 7.
24. Huffman Coding…. contd.
When all nodes have been recombined into a single
``Huffman tree,'' then by starting at the root and
selecting 0 or 1 at each step, you can reach any element
in the tree.
Each element now has a Huffman code, which is the
sequence of 0's and 1's that represents that path
through the tree.
25. 4.1.2 LZ77 Compression
Works by finding the sequence of data that are
repeated.
A lossless data compression algorithm.
Maintains a ‘sliding window during compression’
which means that the compressor have a record of
what last characters were.
Goes through the text in a sliding window consisting
of a search buffer and a look ahead buffer.
The search buffer is used as dictionary.
26. LZ77 Compression…. contd.
1. Suppose the input text is
AABABBBABAABABBBABBABB
2. The first block found is simply A, encoded as (0,A).
The next is AB, encoded as (1,B) where 1 is a reference
to A:
A|AB|ABBBABAABABBBABBABB
3. The next block is ABB, which is encoded as (2,B)
where 2 is a reference to AB, entered in the
dictionary one iteration ago. Going this way, the
string parses into
A|AB|ABB|B|ABA|ABAB|BB|ABBA|BB
27. LZ77 Compression…. Contd.
At the end of the algorithm, the dictionary is:
Reference Phrase Encoding
1 A (0,A)
2 AB (1,B)
3 ABB (2,B)
4 B (0,B)
5 ABA (2,A)
6 ABAB (5,B)
7 BB (4,B)
8 ABBA (3,A)
9 BB (7,0)
28. 4.1.3 Putting Both Together
The Flate is a smart algorithm that adapts the way it
compresses data to the actual data themselves. There are
three modes of compression that the compressor has
available:
1. Not compressed at all an intelligent choice when the
data has already been compressed.
2. Compression, first with LZ77 and then with a slightly
modified version of Huffman coding. The trees that
are used are defined by the Flate specification itself.
29. Putting Both Together….contd.
3. Compression first with LZ77 and then with Huffman
coding with trees that compressor creates and stores
along with the data.
The data is broken up into blocks each block uses a
single mode of compression.
30. 5. Advantages & Disadvantages
5.1 LZW
Advantage
Is a lossless compression algo. Hence no information is lost.
One need not pass the code table between the two
compression and the decompression.
Simple, fast and good compression.
Disadvantage
What happens when the dictionary becomes too large.
One approach is to throw the dictionary away when it reaches
a certain size.
Useful only for a large amount of text data where redundancy
is high.
31. Advantages & Disadvantages
5.1 Flate Compression
Advantage
Huffman is easy to implement.
Flate is a lossless compression technique hence no loss of text.
Simple, fast and good compression.
Freedom to chose the type of compression based on the need of the
content.
Disadvantage
Overhead is generated due to Huffman tree generation.
The actual resulting compression code becomes too complex as it
combines LZ77 and Huffman.
It’s quiet tricky to understand and correctly apply the correct
combination of LZ77 and Huffman.
32. 6. Conclusion
LZW has various advantages when being used to
compress large text data, in English language which
has high redundancy.
Both LZW and Flate are software based, Dictionary
and lossless methods of compression.
The text compression needs lossless technique of
compression.
Flate which is readily used in PDF files, is an adaptive,
changeable and complex way to compress text.