The JPEG image compression standard works by first converting the image color space to Y'CbCr and subsampling the chroma channels. It then applies the discrete cosine transform to separate the image into spatial frequencies. Quantization more heavily reduces the higher frequency components, capitalizing on human visual perception being less sensitive to color and fine details. Run-length encoding groups common values, and Huffman coding further compresses the data into an efficient binary representation for storage and transmission.
A new algorithm for data compression technique using vlsiTejeswar Tej
HOW COMPRESSION IS POSSIBLE?????????
NOW A DAYS LOT OF ALGORITHMS ARE READY TO COMPRESS DATA BUT POWER IS THE MAJOR CRITERIA OF ALL.BUT MY PROJECT IS TO OVERCOME IT I..E THE NEW ALGORITHM BY
K-RLE
A new algorithm for data compression technique using vlsiTejeswar Tej
HOW COMPRESSION IS POSSIBLE?????????
NOW A DAYS LOT OF ALGORITHMS ARE READY TO COMPRESS DATA BUT POWER IS THE MAJOR CRITERIA OF ALL.BUT MY PROJECT IS TO OVERCOME IT I..E THE NEW ALGORITHM BY
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International Journal of Engineering Research and Development (IJERD)IJERD Editor
We would send hard copy of Journal by speed post to the address of correspondence author after online publication of paper.
We will dispatched hard copy to the author within 7 days of date of publication
Image compression: Techniques and ApplicationNidhi Baranwal
This presentation involves a mathematical view of image compression having a brief introduction of its theory,major techniques along with their algorithm and examples.
Image compression using Hybrid wavelet Transform and their Performance Compa...IJMER
Images may be worth a thousand words, but they generally occupy much more space in hard disk, or
bandwidth in a transmission system, than their proverbial counterpart. Compressing an image is significantly
different than compressing raw binary data. Of course, general purpose compression programs can be used to
compress images, but the result is less than optimal. This is because images have certain statistical properties
which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image
can be sacrificed for the sake of saving a little more bandwidth or storage space. Compression is the process of
representing information in a compact form. Compression is a necessary and essential method for creating
image files with manageable and transmittable sizes. The data compression schemes can be divided into
lossless and lossy compression. In lossless compression, reconstructed image is exactly same as compressed
image. In lossy image compression, high compression ratio is achieved at the cost of some error in reconstructed
image. Lossy compression generally provides much higher compression than lossless compression.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
We would send hard copy of Journal by speed post to the address of correspondence author after online publication of paper.
We will dispatched hard copy to the author within 7 days of date of publication
Image compression: Techniques and ApplicationNidhi Baranwal
This presentation involves a mathematical view of image compression having a brief introduction of its theory,major techniques along with their algorithm and examples.
Image compression using Hybrid wavelet Transform and their Performance Compa...IJMER
Images may be worth a thousand words, but they generally occupy much more space in hard disk, or
bandwidth in a transmission system, than their proverbial counterpart. Compressing an image is significantly
different than compressing raw binary data. Of course, general purpose compression programs can be used to
compress images, but the result is less than optimal. This is because images have certain statistical properties
which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image
can be sacrificed for the sake of saving a little more bandwidth or storage space. Compression is the process of
representing information in a compact form. Compression is a necessary and essential method for creating
image files with manageable and transmittable sizes. The data compression schemes can be divided into
lossless and lossy compression. In lossless compression, reconstructed image is exactly same as compressed
image. In lossy image compression, high compression ratio is achieved at the cost of some error in reconstructed
image. Lossy compression generally provides much higher compression than lossless compression.
PIPELINED ARCHITECTURE OF 2D-DCT, QUANTIZATION AND ZIGZAG PROCESS FOR JPEG IM...VLSICS Design
This paper presents the architecture and VHDL design of a Two Dimensional Discrete Cosine Transform (2D-DCT) with Quantization and zigzag arrangement. This architecture is used as the core and path in JPEG image compression hardware. The 2D- DCT calculation is made using the 2D- DCT Separability property, such that the whole architecture is divided into two 1D-DCT calculations by using a transpose buffer. Architecture for Quantization and zigzag process is also described in this paper. The quantization process is done using division operation. This design aimed to be implemented in Spartan-3E XC3S500 FPGA. The 2D- DCT architecture uses 1891 Slices, 51I/O pins, and 8 multipliers of one Xilinx Spartan-3E XC3S500E FPGA reaches an operating frequency of 101.35 MHz One input block with 8 x 8 elements of 8 bits each is processed in 6604 ns and pipeline latency is 140 clock cycles .
Pipelined Architecture of 2D-DCT, Quantization and ZigZag Process for JPEG Im...VLSICS Design
This paper presents the architecture and VHDL design of a Two Dimensional Discrete Cosine Transform (2D-DCT) with Quantization and zigzag arrangement. This architecture is used as the core and path in JPEG image compression hardware. The 2D- DCT calculation is made using the 2D- DCT Separability property, such that the whole architecture is divided into two 1D-DCT calculations by using a transpose buffer. Architecture for Quantization and zigzag process is also described in this paper. The quantization process is done using division operation. This design aimed to be implemented in Spartan-3E XC3S500 FPGA. The 2D- DCT architecture uses 1891 Slices, 51I/O pins, and 8 multipliers of one Xilinx Spartan-3E XC3S500E FPGA reaches an operating frequency of 101.35 MHz One input block with 8 x 8 elements of 8 bits each is processed in 6604 ns and pipeline latency is 140 clock cycles.
Comparison of different Fingerprint Compression Techniquessipij
The important features of wavelet transform and different methods in compression of fingerprint images have been implemented. Image quality is measured objectively using peak signal to noise ratio (PSNR) and mean square error (MSE).A comparative study using discrete cosine transform based Joint Photographic Experts Group(JPEG) standard , wavelet based basic Set Partitioning in Hierarchical trees(SPIHT) and Modified SPIHT is done. The comparison shows that Modified SPIHT offers better compression than basic SPIHT and JPEG. The results will help application developers to choose a good wavelet compression system for their applications.
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.
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.
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.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
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.
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.
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2. JPEG
JPEG is a commonly used method of lossy compression for digital images,
particularly for those images produced by digital photography. The degree of
compression can be adjusted, allowing a selectable tradeoff between storage
size and image quality. JPEG typically achieves 10:1 compression with little
perceptible loss in image quality.
Wikipedia, JPEG
4. 1. Color Transform & Downsampling
Ref. https://www.fileformat.info/mirror/egff/ch09_06.htm
5. 1. Color Transform & Downsampling
The representation of the colors in the image is converted from RGB toY′CBCR,
consisting of one luma component (Y'), representing brightness, and two
chroma components, (CB and CR), representing color. This step is sometimes
skipped.
The resolution of the chroma data is reduced, usually by a factor of 2 or 3.
This reflects the fact that the eye is less sensitive to fine color details than to
fine brightness details.
Wikipedia, JPEG
6. YUV
YUV (…) encodes a color image or video taking human
perception into account, allowing reduced bandwidth for
chrominance components, thereby typically enabling
transmission errors or compression artifacts to be more
efficiently masked by the human perception than using a "direct"
RGB-representation.
Wikipedia,YUV
7. YUV
Y′UV was invented when engineers wanted color television
in a black-and-white infrastructure.
The luma component already existed as the black and white signal; they added
the UV signal to this as a solution.
The U and V signals tell the television to shift the color of a certain pixel
without altering its brightness. Or the U and V signals tell the monitor to make
one color brighter at the cost of the other and by how much it should be
shifted.
Wikipedia,YUV
8. Y’CbCr
Y′CbCr is often confused with theYUV color space,
and typically the termsYCbCr andYUV are used
interchangeably, leading to some confusion.
The main difference is that YUV is analog andYCbCr is digital.
Y′CbCr is used to separate out a luma signal (Y′) that can be stored with high
resolution or transmitted at high bandwidth, and two chroma components (CB
and CR) that can be bandwidth-reduced, subsampled, compressed, or
otherwise treated separately for improved system efficiency.
Wikipedia,YCbCr
9. Chroma Subsampling
Chroma subsampling is the practice of encoding images by implementing less
resolution for chroma information than for luma information, taking
advantage of the human visual system's lower acuity for color differences
than for luminance.
The subsampling scheme is commonly expressed as a three part ratio J:a:b, that
describe the number of luminance and chrominance samples in a conceptual
region that is J pixels wide, and 2 pixels high.
● J: horizontal sampling reference (width of the conceptual region). Usually, 4.
● a: number of chrominance samples (Cr, Cb) in the first row of J pixels.
● b: number of changes of chrominance samples (Cr, Cb) between first and second row of J pixels.
Wikipedia, Chroma Subsampling
12. 2. Discrete Cosine Transform
After subsampling, each channel must be split into 8×8 blocks. Depending on
chroma subsampling, this yields Minimum Coded Unit (MCU) blocks of size 8×8
(4:4:4 – no subsampling), 16×8 (4:2:2), or most commonly 16×16 (4:2:0).
If the data for a channel does not represent an integer number of blocks then the
encoder must fill the remaining area of the incomplete blocks with some form
of dummy data. Filling the edges with a fixed color can create ringing
artifacts along the visible part of the border; repeating the edge pixels is a
common technique that reduces (but does not necessarily completely eliminate)
such artifacts, and more sophisticated border filling techniques can also be
applied.
Wikipedia, JPEG
13. Fourier Transform
The Fourier transform (FT) decomposes
a function of time (a signal) into its constituent
frequencies. This is similar to the way a musical
chord can be expressed in terms of the volumes and frequencies of its
constituent notes.
The Fourier transform of a function of time is itself a complex-valued function of
frequency, whose magnitude (modulus) represents the amount of that
frequency present in the original function, and whose argument is the phase
offset of the basic sinusoid in that frequency.
Wikipedia, Fourier Transform
14. 2. Discrete Cosine Transform
A discrete cosine transform (DCT) expresses a finite sequence of data points in
terms of a sum of cosine functions oscillating at different frequencies. DCTs
are important to numerous applications in science and engineering, from lossy
compression of audio (e.g. MP3), images (e.g. JPEG) (where small
high-frequency components can be discarded), and video (e.g. MPEG)
Wikipedia, DCT
The DCT transforms an 8×8 block of input values to a linear combination of
these 64 patterns. The patterns are referred to as the two-dimensional DCT
basis functions, and the output values are referred to as transform
coefficients.
Wikipedia, JPEG
15. Discrete Cosine Transform Example
= Σ
u (horizontal spatial frequency) = 0 → 7
v(verticalspatialfrequency)=0→7
Wikipedia, Discrete Cosine Transform
17. Discrete Cosine Transform
Note the top-left corner entry with the rather large
magnitude. This is the DC coefficient, which defines
the basic hue for the entire block. The remaining 63
coefficients are the AC coefficients.
The advantage of the DCT is its tendency to aggregate
most of the signal in one corner of the result. The
quantization step to follow accentuates this effect while
simultaneously reducing the overall size of the DCT
coefficients, resulting in a signal that is easy to
compress efficiently in the entropy stage.
Wikipedia, JPEG
18. 3. Quantization
Note the top-left corner entry with the rather large
magnitude. This is the DC coefficient, which defines
the basic hue for the entire block. The remaining 63
coefficients are the AC coefficients.
The human eye is good at seeing small differences in
brightness over a relatively large area,
but not so good at distinguishing the exact strength of
a high frequency brightness variation.
Wikipedia, JPEG
19. 3. Quantization
The human eye is good at seeing small differences in brightness over a relatively
large area (DC Coefficients, basic hue), but not so good at distinguishing the
exact strength of a high frequency brightness variation (AC Coefficients).
This allows one to greatly reduce the amount of information in the high frequency
components. This is done by simply dividing each component in the frequency
domain by a constant for that component, and then rounding to the nearest
integer.
Wikipedia, JPEG
20. Quantization Matrix
A typical quantization matrix
(for a quality of 50% as specified in the original JPEG Standard)
Wikipedia, JPEG
21. Quantization Matrix
The quantization matrix is designed to provide more resolution to more
perceivable frequency components over less perceivable components
(usually lower frequencies over high frequencies) in addition to transforming as
many components to 0, which can be encoded with greatest efficiency.
This rounding operation is the only lossy operation in the whole process (other
than chroma subsampling) if the DCT computation is performed with sufficiently
high precision. As a result of this, it is typically the case that many of the higher
frequency components are rounded to zero, and many of the rest become
small positive or negative numbers, which take many fewer bits to represent.
Wikipedia, JPEG
24. Run-length Encoding (RLE)
Run-length encoding (RLE) is a very simple form of lossless data compression in
which runs of data (that is, sequences in which the same data value occurs in
many consecutive data elements) are stored as a single data value and count,
rather than as the original run. This is most useful on data that contains many
such runs.
Example. WWWWWWWWWWWWBWWWWWWWWWWWWBBBWWWWWWWWWWWWWWWWWWWWWWWW
→ 12W1B12W3B24W
Wikipedia, Run-length Encoding
25. Shannon Entropy
Information entropy is the average rate at which
information is produced by a stochastic source
of data. The measure of information entropy
associated with each possible data value is:
When the data source produces a low-probability
value (when a low-probability event occurs), the
event carries more "information" ("surprisal")
than a high-probability event.
Wikipedia, Shannon Entropy
Entropy H(X) of a coin flip
26. Huffman Encoding
In computer science and information theory, a Huffman code is a particular type
of optimal prefix code that is commonly used for lossless data compression.
The output from Huffman's algorithm can be viewed as a variable-length code
table for encoding a source symbol (such as a character in a file). The
algorithm derives this table from the estimated probability or frequency of
occurrence (weight) for each possible value of the source symbol. As in other
entropy encoding methods, more common symbols are generally represented
using fewer bits than less common symbols.
Wikipedia, Huffman Encoding
29. 4. Encoding
It involves arranging the image components in a "zigzag" order employing
run-length encoding (RLE) algorithm that groups similar frequencies together,
inserting length coding zeros, and then using Huffman coding on what is left.
The JPEG standard provides general-purpose Huffman tables; encoders may
also choose to generate Huffman tables optimized for the actual frequency
distributions in images being encoded.
Wikipedia, JPEG
30. Why RLE?
It involves arranging the image components in a
"zigzag" order employing run-length encoding
(RLE) algorithm that groups similar frequencies
together, inserting length coding zeros, and then
using Huffman coding on what is left.
It is typically the case that many of the higher
frequency components are rounded to zero.
Wikipedia, JPEG
31. Why not use Huffman Encoding directly?
A traditional Huffman code would be obliged to use at least one bit per
character. … The entropy of English, given a good model, is about one bit per
character (Shannon, 1948), so a Huffman code is likely to be highly inefficient.
A traditional patch-up of Huffman codes uses then to compress blocks of
symbols, … but only at the expense of losing the elegant instantaneous
decodeability, … and having to compute the probabilities of all relevant strings
… end up explicitly computing the probabilities and codes for a huge number of
strings, most of which will never actually occur. … They are optimal symbol
codes, but for practical purposes we don’t want a symbol code.
Information Theory, Inference, and Learning Algorithms (MacKay, 2005)
32. 5. Summary
Y’CbCr and
Chroma Subsampling
Discrete Cosine Transform
on Spatial Frequency
Effective Quantization of
AC Coefficients
Run-length Encoding
Huffman Encoding
Ref. https://www.fileformat.info/mirror/egff/ch09_06.htm