There are two categories of data compression methods: lossless and lossy. Lossless methods preserve the integrity of the data by using compression and decompression algorithms that are exact inverses, while lossy methods allow for data loss. Common lossless methods include run-length encoding and Huffman coding, while lossy methods like JPEG, MPEG, and MP3 are used to compress images, video, and audio by removing imperceptible or redundant data.
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.
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.
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.
Types of Data compression, Lossy Compression, Lossless compression and many more. How data is compressed etc. A little extensive than CIE O level Syllabus
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.
Types of Data compression, Lossy Compression, Lossless compression and many more. How data is compressed etc. A little extensive than CIE O level Syllabus
Digital video is, a sequence of images, called frames, displayed at a certain frame rate (so many frames per second, or fps) to create the illusion of animation.
These slides cover the fundamentals of data communication & networking. it covers Data compression which compression data for transmitted over communication channel. It is useful for engineering students & also for the candidates who want to master data communication & computer networking.
HIDING A MESSAGE IN MP3 USING LSB WITH 1, 2, 3 AND 4 BITSIJCNCJournal
Steganography is the art of hiding information in ways that prevent the detection of hidden messages. This
paper presentsa new method which randomly selects position in MP3 file to hide a text secret messageby
using Least Significant Bit (LSB) technique. The text secret message isused in start and ends locations a
unique signature or key.The methodology focuses to embed one bit, two bits, three bitsor four bits from
secret message into MP3 file by using LSB techniques. The evaluation and performancemethods are based
on robustness (BER and correlation), Imperceptibility (PSNR) and hiding capacity (Ratio between Sizes of
text message and MP3 Cover) indicators.The experimental results show the new method is more security.
Moreover the contribution of this paper is the provision of a robustness-based classification of LSB
steganography models depending on their occurrence in the embedding position.
The combination of steganography and
cryptography is considered as one of the best security methods
used for message protection, due to this reason, in this paper, a
data hiding system that is based on image steganography and
cryptography is proposed to secure data transfer between the
source and destination. Animated GIF image is chosen as a
carrier file format for the steganography due to a wide use in web
pages and a LSB (Least Significant Bits) algorithm is employed to
hide the message inside the colors of the pixels of an animated
GIF image frames. To increase the security of hiding, each frame
of GIF image is converted to 256 color BMP image and the
palette of them is sorted and reassign each pixels to its new index,
furthermore, the message is encrypted by LZW ( Lempel _
Ziv_Welch) compression algorithm before being hidden in the
image frames. The proposed system was evaluated for
effectiveness and the result shows that, the encryption and
decryption methods used for developing the system make the
security of the proposed system more efficient in securing data
from unauthorized users. The system is therefore, recommended
to be used by the Internet users for establishing a more secure
communication
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Data compression implies sending or storing a smaller number
of bits. Although many methods are used for this purpose, in
general these methods can be divided into two broad
categories: lossless and lossy methods.
3. In lossless data compression, the integrity of the data is
preserved.
The original data and the data after compression and
decompression are exactly the same because, in these
methods, the compression and decompression algorithms are
exact inverses of each other: no part of the data is lost in the
process.
Redundant data is removed in compression and added during
decompression.
4. Run-length encoding is probably the simplest method of
compression.
It can be used to compress data made of any combination of
symbols.
It does not need to know the frequency of occurrence of
symbols and can be very efficient if data is represented as 0s
and 1s.
5. Huffman coding assigns shorter codes to symbols that occur more
frequently and longer codes to those that occur less frequently.
For example, imagine we have a text file that uses only five
characters (A, B, C, D, E). Before we can assign bit patterns to each
character, we assign each character a weight based on its frequency
of use.
6. A character’s code is found by starting at the root and
following the branches that lead to that character. The code
itself is the bit value of each branch on the path, taken in
sequence.
7. Lempel Ziv (LZ) encoding is an example of a category of
algorithms called dictionary-based encoding.
The idea is to create a dictionary (a table) of strings used
during the communication session.
If both the sender and the receiver have a copy of the
dictionary, then previously-encountered strings can be
substituted by their index in the dictionary to reduce the
amount of information transmitted.
8.
9. Our eyes and ears cannot distinguish subtle changes. In such
cases, we can use a lossy data compression method.
These methods are cheaper—they take less time and space
when it comes to sending millions of bits per second for
images and video.
Several methods have been developed using lossy
compression techniques. JPEG (Joint Photographic Experts
Group) encoding is used to compress pictures and graphics,
MPEG (Moving Picture Experts Group) encoding is used to
compress video, and MP3 (MPEG audio layer 3) for audio
compression.
10. An image can be represented by a two-dimensional array
(table) of picture elements (pixels).
A grayscale picture of 307,200 pixels is represented by
2,457,600 bits, and a color picture is represented by 7,372,800
bits.
In JPEG, a grayscale picture is divided into blocks of 8 × 8
pixel blocks to decrease the number of calculations because, as
we will see shortly, the number of mathematical operations for
each picture is the square of the number of units.
11. The Moving Picture Experts Group (MPEG) method is used to
compress video. In principle, a motion picture is a rapid
sequence of a set of frames in which each frame is a picture.
In other words, a frame is a spatial combination of pixels, and
a video is a temporal combination of frames that are sent one
after another.
Compressing video, then, means spatially compressing each
frame and temporally compressing a set of frames.
13. Audio compression can be used for speech or music. For
speech we need to compress a 64 kHz digitized signal, while
for music we need to compress a 1.411 MHz signal.
Two categories of techniques are used for audio compression:
predictive encoding and perceptual encoding.
14. In predictive encoding,
thedifferences between
samples are encoded instead
of encoding all the sampled
values.
This type of compression is
normally used for speech.
Several standards have been
defined such as GSM (13
kbps), G.729 (8 kbps), and
G.723.3 (6.4 or 5.3 kbps).
Detailed discussions of these
techniques are beyond the
scope of this book.
The most common
compression technique used
to create CD-quality audio
is based on the perceptual
encoding technique.
This type of audio needs at
least 1.411 Mbps, which
cannot be sent over the
Internet without
compression. MP3 (MPEG
audio layer 3) uses this
technique.