What is Video Compression?, Introduction of Video Compression. Motivation, Working Methodology of Video Compression., Example, Applications, Needs of Video Compression, Advantages & Disadvantages
Digital image processing- Compression- Different Coding techniques sudarmani rajagopal
Image Compression- Different coding Techniques such as Huffman coding, Bit plane and Arithmetic coding are discussed. By using these coding techniques compression of data or image can be achieved.
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 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.
Computer Science/ICT - Data Compression
This presentation covers all aspects of data compression you'll need to know such as definition, reasons, types of compression (lossy and lossless) and the types of compression within those sections (JPEG, MPEG, MP3, Run Length and Dictionary Based encoding)
AN EFFICIENT CODEBOOK INITIALIZATION APPROACH FOR LBG ALGORITHMIJCSEA Journal
In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm.
What is Video Compression?, Introduction of Video Compression. Motivation, Working Methodology of Video Compression., Example, Applications, Needs of Video Compression, Advantages & Disadvantages
Digital image processing- Compression- Different Coding techniques sudarmani rajagopal
Image Compression- Different coding Techniques such as Huffman coding, Bit plane and Arithmetic coding are discussed. By using these coding techniques compression of data or image can be achieved.
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 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.
Computer Science/ICT - Data Compression
This presentation covers all aspects of data compression you'll need to know such as definition, reasons, types of compression (lossy and lossless) and the types of compression within those sections (JPEG, MPEG, MP3, Run Length and Dictionary Based encoding)
AN EFFICIENT CODEBOOK INITIALIZATION APPROACH FOR LBG ALGORITHMIJCSEA Journal
In VQ based image compression technique has three major steps namely (i) Codebook Design, (ii) VQ Encoding Process and (iii) VQ Decoding Process. The performance of VQ based image compression technique depends upon the constructed codebook. A widely used technique for VQ codebook design is the Linde-Buzo-Gray (LBG) algorithm. However the performance of the standard LBG algorithm is highly dependent on the choice of the initial codebook. In this paper, we have proposed a simple and very effective approach for codebook initialization for LBG algorithm. The simulation results show that the proposed scheme is computationally efficient and gives expected performance as compared to the standard LBG algorithm.
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Generalization of linear and non-linear support vector machine in multiple fi...CSITiaesprime
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.
A Brief Introduction to Machine Learning techniques applied in data science. Definitions and applications of machine learning algorithms. Classification and Regression Techniques.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will get an introduction to Autoencoders and Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
Comparison of Learning Algorithms for Handwritten Digit RecognitionSafaa Alnabulsi
A 20 minutes seminar where I explained the performance of different classifiers in the Handwritten Digit Recognition problem.
The paper: http://yann.lecun.com/exdb/publis/pdf/lecun-95b.pdf
Unit 1 Introduction to Non-Conventional Energy ResourcesDr Piyush Charan
This unit is part of the course EC228 Renewable Energy Engineering of program B.Tech. Electronics Engg. (Solar Photovoltaic Engineering). It gives an introduction to conventional and non-conventional energy resources.
Unit 5-Operational Amplifiers and Electronic Measurement DevicesDr Piyush Charan
Lecture Notes on Operational Amplifiers and Measuring Instruments. These notes cover unit 5 of the course Basic Electronics (EC101) taught at Integral University.
This presentation deals with the lecture notes of unit 4 of the course Basic Electronics EC101 which is a common course of B.Tech Curriculum having 4 credits.
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/
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.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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.
Pile Foundation by Venkatesh Taduvai (Sub Geotechnical Engineering II)-conver...
Unit 5 Quantization
1. Lecture Notes on Quantization
for
Open Educational Resource
on
Data Compression(CA209)
by
Dr. Piyush Charan
Assistant Professor
Department of Electronics and Communication Engg.
Integral University, Lucknow
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Unit 5-Syllabus
• Quantization
– Vector Quantization,
– Advantages of Vector Quantization over Scalar
Quantization,
– The Linde-BuzoGray Algorithm,
– Tree-structured Vector Quantizers,
– Structured Vector Quantizers
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 3
3. Introduction
• Quantization is one of the efficient tool for lossy
compression.
• It can reduce the bits required to represent the source.
• In lossy compression application, we represent each source
output using one of a small number of codewords.
• The number of distinct source output values is generally
much larger than the number of codewords available to
represent them.
• The process of representing the number of distinct output
values to a much smaller set is called quantization.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 4
4. Introduction contd…
• The set of input and output of a quantizer can
be scalars or vectors.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 5
5. Types of Quantization
• Scalar Quantization: The most common types of
quantization is scalar quantization. Scalar quantization,
typically denoted as y = Q(x) is the process of using
quantization function Q(x) to map a input value x to scalar
output value y.
• Vector Quantization: A vector quantization map k-
dimensional vector in the vector space Rk into a finite set of
vectors Y=[Yi : i=1,2,..,N]. Each vector Yi is called a code
vector or a codeword and set of all the codeword is called a
codebook.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 6
6. Vector Quantization
• VQ is a lossy data compression method based on the principal of
block coding technique that quantizes blocks of data instead of
signal sample.
• VQ exploits the correlation existing between neighboring signal
sample by quantizing them together.
• VQ is one of the widely used and efficient technique for image
compression.
• Since last few decades in the field of multimedia data compression,
VQ has received a great attention because it has simple decoding
structure and can provide high compression ratio.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 7
7. Vector Quantization contd…
• VQ based image compression technique has three major steps namely:
1. Codebook Design
2. VQ Encoding Processes.
3. VQ Decoding Processes.
• In VQ based image compression first image is decomposed into non-
overlapping sub-blocks and each sub block is converted into one-
dimension vector termed as training vector.
• From training vectors, a set of representative vector are selected to
represent the entire set of training vector.
• The set of representative training vector is called a codebook and each
representative training vector is called codeword or code-vector.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 8
8. Vector Quantization contd…
• The goal of VQ code book generation is to find an optimal code book that yields the
lowest possible distortion when compared with all other code books of the same size.
• The performance of VQ based image compression technique depends upon the
constructed codebook.
• The search complexity increases with the number of vectors in the code book and to
minimize the search complexity, the tree search vector quantization schemes was
introduced.
• The number of code vectors N depends on two parameters, rate R and dimensions L.
• The number of code vector is calculated using the following formula-
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑑𝑒 𝑣𝑒𝑐𝑡𝑜𝑟𝑠 (𝑁) = 2𝑅×𝐿
where;
R → Rate in bits/pixel,
L → Dimensions
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 9
10. Difference between Vector and Scalar
Quantization
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 11
• ⇢ 1: Vector Quantization can lower the average distortion with the
number of reconstruction levels held constant, While Scalar Quantization
cannot.
• ⇢ 2: Vector Quantization can reduce the number of reconstruction levels
when distortion is held constant, While Scalar Quantization cannot.
• ⇢ 3: The most significant way Vector Quantization can improve
performance over Scalar Quantization is by exploiting the statistical
dependence among scalars in the block.
• ⇢ 4: Vector Quantization is also more effective than Scalar Quantization
When the source output values are not correlated.
11. Difference between Vector and Scalar
Quantization contd…
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 12
• ⇢ 5: In Scalar Quantization, in One Dimension, the quantization regions
are restricted to be in intervals(i.e., Output points are restricted to be
rectangular grids) and the only parameter we can manipulate is the size of
the interval. While, in Vector Quantization, When we divide the input into
vectors of some length n, the quantization regions are no longer restricted to
be rectangles or squares, we have the freedom to divide the range of the
inputs in an infinite number of ways.
• ⇢ 6: In Scalar Quantization, the Granular Error is affected by size of
quantization interval only, while in Vector Quantization, Granular Error is
affected by the both shape and size of quantization interval.
• ⇢ 7: Vector Quantization provides more flexibility towards modifications
than Scalar Quantization. The flexibility of Vector Quantization towards
modification increases with increasing dimension.
12. Difference between Vector and Scalar
Quantization contd…
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 13
• ⇢ 8: Vector Quantization have improved performance when there is
sample to sample dependence of input, While not in Scalar
Quantization.
• ⇢ 9: Vector Quantization have improved performance when there is
not the sample to sample dependence of input, While not in Scalar
Quantization.
• ⇢ 10:Describing the decision boundaries between reconstruction
levels is easier in Scalar Quantization than in Vector Quantization.
13. Advantages of Vector Quantization
over Scalar Quantization
• Vector Quantization provide flexibility in choosing
multidimensional Quantizer cell shape and in choosing a
desired code-book size.
• The advantage of VQ over SQ is the fractional value of
resolution that is achieved and very important for low-bit rate
applications where low resolution is sufficient.
• For a given rate VQ results in a lower distortion than SQ.
• VQ can utilize the memory of the source better than SQ.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 14
14. Linde-Buzo Gray Algorithm
• The need for multi-dimensional integration for the
design of a vector quantizer was a challenging problem
in the earlier days.
• The main concepts is to divide a group of vector. To
find a most representative vector from one group. Then
gather the vectors to from a codebook. The inputs are
not longer scalars in the LBG algorithm.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 15
15. LBG Algorithm
1. Divide image into block. Then we can view one block as k-dimension
vector.
2. Arbitrarily choose initial codebook. Set these initial codebook as
centroids. Other are grouped. Vector are in the same group when they
have the same nearest centroids.
3. Again to find new centroids for every group. Get new codebooks.
Repeat 2,3 steps until the centroids of every groups converge.
• Thus at every iteration the codebook become progressively better. This
processed is continued till there is no change in the overall distortion.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 16
16. Initializing the LBG Algorithm
• The important thing we need to consider is the good set of initial
quantization points that will guarantee the convergence the LBG
algorithm guarantee that the distortion from one iteration to the next will
not increase.
• The performance of the LBG algorithm depends heavily on the initial
codebook.
• We will use splitting technique to design the initial codebook.
1. Random selection of Hilbert technique
2. Pair wise Nearest Neighbor (PNN) method.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 17
17. Empty Cell Problem
• What we will do if one of the reconstruction or quantization region in
some iteration is empty?
• There might be no points which are closer to a given reconstruction
point than any other reconstruction points.
• This is problem because in order to update an output points, we need to
take the average of the input vectors assigned to that output.
• But in this case we will end up with an output that is never used.
• A common solution to a empty cell problem is to remove an output
point that has no inputs associated with it and replace it with a point
from the quantization region with most training points.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 18
18. Tree Structure Vector Quantization
• Another fast codebook design technique-structured VQ and
was presented by Buzo.
• The number of operation can be reduced by enforcing a certain
structure on the codebook.
• One such possibility is using a tree structure, while turns into a
tree codebook and the method is called the binary search
clustering.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 19
19. Tree Structure Vector Quantization
• The disadvantage of tree-search is that we might not end up with the reconstruction
point that is closest the distortion will be a little higher compared to a full search
Quantizer.
• The storage requirement will also be larger, since we have to store all test vector too.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 20
20. How to design TSVQ
1. Obtain the average of all the training vectors, unsettled it to obtain a
second vector, and use these vector to from a two level VQ.
2. Call the vector v0and v1 and the group of training set vector that would
be quantized to each as g0 and g1.
3. Unsettled v0 and v1 to get the initial vectors for a four-level VQ.
4. Use g0 to design a two-level VQ and g1 and to design the another two-
level VQ.
5. Label the vectors v00,v01,v10,v11.
6. Split g0 using v00 and v01 into two groups g00,g01
7. Split g1 using v10 and v11 into two groups g10 and g11.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 21
21. Pruned Tree- structured Vector Quantizer
• Now we have develop tree-structured codebook and we can
improve its rate distortion performance by pruning removing
carefully selected subgroups that will reduce the size of the
codebook and thus the rate.
• But it may increase the distortion so the main objective of
pruning is to remove those groups that will result in the best
trade-off rate and distortion.
• Prune trees by finding sub tree T that minimizes ‘𝜆𝑇’
• 𝜆𝑇 =
𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑑𝑖𝑠𝑡𝑜𝑟𝑡𝑖𝑜𝑛 𝑖𝑓 𝑝𝑟𝑢𝑛𝑒 𝑠𝑢𝑏 𝑡𝑟𝑒𝑒 𝑇
𝑐ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑟𝑎𝑡𝑒 𝑖𝑓 𝑝𝑟𝑢𝑛𝑒 𝑠𝑢𝑏𝑡𝑟𝑒𝑒 𝑇
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 22
22. Structured Vector Quantization
• Several structured code impose a structure that allows for reduces implementation
complexity and also constrain codewords or codeword search.
• Let L be the digestion of the VQ. If R is the bit rate, then 𝐿2𝑅𝐿 scalar need to be stored.
Also, 𝐿2𝑅𝐿 scalar distortion calculation are required.
• So solution is to introduce some from of structure in the codebook and also in quantization
process.
• Disadvantage of structure VQ is inventible loss in rate-distortion performance.
• Different types of structure vector Quantizer are:
1. Lattice quantization
2. Tree-structure code
3. Multistage code
4. Product code: gain/shape code
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 23
23. Lattice Vector Quantizer
• VQ codebook designed using LBG
algorithm complicated the
quantization process and have no
visible structure.
• So alternative is a Lattice point
quantization sine we can use it as
fast encoding algorithm.
• For a bit rate of n bit/sample and
spatial dimension v, the number of
codebook vectors, or equivalently of
lattice points used in 2nv.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 24
24. How are tree structured vector quantizers
better?
• Tree-structured vector quantization (TSVQ) reduces the complexity
by imposing a hierarchical structure on the partitioning. We study the
design of optimal tree-structured vector quantizers that minimize the
expected distortion subject to cost functions related to storage cost,
encoding rate, or quantization time.
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 25
25. Thanks!!
02 February 2021 Dr. Piyush Charan, Dept. of ECE, Integral University, Lucknow 26
Dr. Piyush Charan
Assistant Professor,
Department of ECE,
Integral University, Lucknow
Email: er.piyush.charan@gmail.com, piyush@iul.ac.in