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The document discusses various lossless compression techniques including entropy coding methods like Huffman coding and arithmetic coding. It also covers dictionary-based coding like LZW, as well as spatial compression techniques like run-length coding, quadtrees for images, and lossless JPEG.
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A complete slide that represent huffman coding and tree with examples explained and very easy to understantd
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The document discusses different techniques for compressing multimedia data such as text, images, audio and video. It describes how compression works by removing redundancy in digital data and exploiting properties of human perception. It then explains different compression methods including lossless compression, lossy compression, entropy encoding, and specific algorithms like Huffman encoding and arithmetic coding. The goal of compression is to reduce the size of files to reduce storage and bandwidth requirements for transmission.
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The document discusses Shannon-Fano encoding, which is an early method for data compression that constructs efficient binary codes for information sources without memory. It works by assigning shorter codes to more frequent messages and longer codes to less frequent messages. The encoding process involves recursively splitting the set of messages in half based on probability until each message has its own unique code. While Shannon-Fano codes are reasonably efficient, they are not always optimal and the codes generated can depend on how the initial splitting of messages is done.
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This document discusses arithmetic coding, a technique for data compression. It begins by explaining how real numbers can be represented in binary and how this relates to encoding strings based on their probabilities. The document then provides examples of how to generate codes for strings and explains the coding and decoding algorithms. It discusses issues like scaling, integer arithmetic coding, adaptation of probabilities, and handling zero frequency and end of file problems. In the end, it compares arithmetic coding to Huffman coding, noting their similarities and differences in terms of compression performance, context handling, and adaptation.
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Huffman coding is an algorithm that uses variable-length binary codes to compress data. It assigns shorter codes to more frequent symbols and longer codes to less frequent symbols. The algorithm constructs a binary tree from the frequency of symbols and extracts the Huffman codes from the tree. Huffman coding is widely used in applications like ZIP files, JPEG images, and MPEG videos to reduce file sizes for efficient transmission or storage.
arithmetic and adaptive arithmetic coding
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A complete slide that represent huffman coding and tree with examples explained and very easy to understantd
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Huffman codes are a technique for lossless data compression that assigns variable-length binary codes to characters, with more frequent characters having shorter codes. The algorithm builds a frequency table of characters then constructs a binary tree to determine optimal codes. Characters are assigned codes based on their path from the root, with left branches representing 0 and right 1. Both encoder and decoder use this tree to translate between binary codes and characters. The tree guarantees unique decoding and optimal compression.
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This document discusses arithmetic coding, a lossless data compression technique. It begins with an overview of source coding and commonly used compression algorithms like Huffman coding and run-length encoding. It then describes problems with Huffman coding like less efficiency and flexibility. Arithmetic coding is presented as an alternative with better compression ratios and adaptiveness. The document proceeds to explain the encoding and decoding process of arithmetic coding through assigning probabilities to symbols and mapping them to ranges within 0 to 1. It provides examples and figures to illustrate the technique. In summary, arithmetic coding achieves better compression than Huffman coding by producing a single codeword rather than multiple codewords.
Huffman Algorithm By Shuhin
Huffman coding is a lossless data compression technique that converts fixed length codes to variable length codes. It assigns shorter codes to more frequent characters and longer codes to less frequent characters. This allows for more efficient data storage and transmission. The key steps are to create a frequency table of characters, construct a binary tree based on frequencies, and extract the Huffman codes from the tree. Huffman coding can significantly reduce file sizes by achieving better compression than fixed length codes. It is used widely in file formats like ZIP, JPEG, and MPEG.
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These slides cover the fundamentals of data communication & networking. it covers Shanon fano coding which are used in communication of data over transmission medium. it is useful for engineering students & also for the candidates who want to master data communication & computer networking.
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speed of a fan can be linearly controlled
automatically, depending
on the room temperature. The circuit is
highly efficient as it uses thyristors for
power control. Alternatively, the same
circuit can be used for automatic temperature
controlled AC power control.
In this circuit, the temperature sensor
used is an NTC thermistor, i.e. one having
a negative temperature coefficient. The
value of thermistor resistance at 25°C is
about 1 kilo-ohm.
Op-amp A1 essentially works as
I to V (current-to-voltage) converter
and converts temperature variations
into voltage variations. To amplify
the change in voltage due to change in
temperature, instrumentation amplifier
formed by op-amps A2, A3 and A4
is used. Resistor R2 and zener diode
D1 combination is used for generating
reference voltage as we want to amplify
only change in voltage due to the
change in temperature.
Op-amp μA741 (IC2) works as a
comparator. One input to the comparator
is the output from the instrumentation
amplifier while the other input
is the stepped down, rectified and
suitably attenuated sample of AC voltage.
This is a negative going pulsating
DC voltage. It will be observed that
with increase in temperature, pin 2 of
IC2 goes more and more negative and
hence the width of the positive going
output pulses (at pin 6) increases linearly
with the temperature. Thus IC2
functions as a pulse width modulator
in this circuit. The output from the
comparator is coupled to an optocoupler,
which in turn controls the AC
power delivered to fan (load).
The circuit has a high sensitivity and
the output RMS voltage (across load) can
be varied from 120V to 230V (for a temp.
range of 22°C to 36°C), and hence wide
variations in speed are available. Also
note that speed varies linearly and not
in steps. Besides, since an optocoupler is
used, the control circuit is fully isolated
from power circuit, thus providing added
safety. Note that for any given temperature
the speed of fan (i.e. voltage across
load) can be adjusted to a desired value
by adjusting potmeters VR1 and VR2
appropriately.
Potmeter VR1 should he initially kept
in its mid position to realise a gain of approximately
40 from the instrumentation
amplifier. It may be subsequently trimmed
slightly to obtain linear variation of the
fan speed.
image compression ppt
This document discusses image compression techniques. It begins by defining image compression as reducing the data required to represent a digital image. It then discusses why image compression is needed for storage, transmission and other applications. The document outlines different types of redundancies that can be exploited in compression, including spatial, temporal and psychovisual redundancies. It categorizes compression techniques as lossless or lossy and describes several algorithms for each type, including Huffman coding, LZW coding, DPCM, DCT and others. Key aspects like prediction, quantization, fidelity criteria and compression models are also summarized.
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This document describes a temperature controlled fan project. It contains a block diagram showing the main components: an 8051 microcontroller, temperature sensor, ADC, motor driver, fan motor, and 7-segment displays. It also provides details on the working, which involves measuring temperature, displaying it on the 7-segment displays, and varying the fan speed based on the temperature using PWM. Simulation results and hardware implementation snapshots are included. The project aims to automatically control fan speed based on sensed temperature.
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- 19. LZW Example Code String 1 a 2 b 2 c
- 20. LZW Example
- 25. Quadtree Example
- 26. Quadtree Example
- 27. Quadtree Example