Applications in Deep Learning
One deep learning AE application of high interest is image and video compression.
It’s used with deep autoencoders to achieve high-quality image compression with very small bits per pixel (bpp).
One of the recent papers that use AE is: Cheng, Zhengxue, et al. “Learning image and video compression through spatial-temporal energy compaction.” Proceed.
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Decimal Module
The AE technique encodes the entire message, regardless of its length, into a single floating-point number.
In Python, the float data type is restricted to a limited number of decimal digits.
A Python float cannot accurately save a number beyond this number of decimal digits.
The exact number of digits is returned using the dig attribute in the str.
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Decoding
The inputs for decoding are:.
1) The single value that encodes the message.
2) The frequency table.
It should be identical to the one used for encoding.
3) The number of symbols in the original message.
In our example, the value that encodes the message is 0.173.
The frequency table is [a=2, b=7, c=1].
The number of symbols in the message is.
3) The .
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Encoding
Encoding in AE works by representing the cumulative probabilities of all symbols on a line that ranges from 0.0 to 1.0.
On that line, each symbol uses a sub-range.
Given any symbol C, it starts from the value Sand ends at the value calculated using the following equation: S+(P(C))*R Where:.
1) S: The cumulative sum of all previous probabilities. 2. .
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Example #2
In this example, I won’t detail the steps like before.
There are 4 symbols available to build messages – a, b, c, and d.
The frequencies of these symbols are: 1. a=2 2. b=3 3. c=1 4. d=4 Based on the frequency table, here are the probabilities of the symbols: 1. p(a)=2/10=0.2 2. p(b)=3/10=0.3 3. p(c)=1/10=0.1 4. p(d)=4/10=0.4 The next figure shows .
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Frequency Table
To encode (= compress) a message using AE, you need these inputs:.
1) The message you want to encode.
2) The frequency table of all possible symbols in the message.
The frequency table is usually calculated by analyzing different messages and concluding by the number of times each symbol is repeated.
For example: assume that the messages to be encod.
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Is Python a good tool for working with compressed files?
Python standard library provides great modules and tools for pretty much any task you can think of and modules for working with compressed files are no exception.
Whether it’s basics like , specific tools or formats such as:
- or even more exotic formats like
- Python has it all
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Overview of The Lossless Algorithm
In data compression, lossy algorithms compress data while losing some details.
They reduce the number of bits used to represent the message, even if that reduces the quality of reconstructed data.
Lossless algorithms reconstruct original data without any loss.
Because of this, they use a higher number of bits compared to lossy algorithms.
Arithmeti.
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Probability Table
Using the frequency table, we can calculate the probability of occurrence of each symbol.
The probability is calculated as follows: Frequency of a symbol/Sum of frequencies of all symbols Based on the frequency table, here are the probabilities of our 3 symbols: 1. p(a)=2/10=0.2 2. p(b)=7/10=0.7 3. p(c)=1/10=0.1 Given the message and the probabilit.
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Python Implementation
We build the implementation of AE as a class named ArithmeticEncoding, within a module named pyae.py.
The constructor of this class accepts the frequency table, as in the next code.
The frequency table passed to this method is a dictionary where each item has a string key representing the symbol, and an integer value representing the frequency.
Her.
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What are the two types of data compression algorithms?
There are 2 categories of data compression algorithms:
- lossy and lossless
In this tutorial, we’ll discuss a lossless algorithm called arithmetic encoding (AE).
You’ll see how AE works through an example that shows the steps of encoding and decoding a message.
We’ll also build a Python implementation that can encode and decode text messages.
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What is data compression in Python programming language?
In this tutorial, we will learn about the data compression in Python programming language.
In python, the data can be archived, compressed using the modules like zlib, gzip, bz2,lzma,zipfile and tarfile.
To use the respective module, you need to import the module first.
Let us look at below example.
