Run Length Encoding (RLE) is a data compression method that simplifies long sequences of repeated values into pairs of run lengths and values. It's efficient for data with repetitive patterns, such as binary data and JPEG images, and is used in digital photography, medical imaging, and more. While RLE is excellent for certain types of data, it may not suit datasets with high variability.
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RLE compresses data by replacing long sequences of repeated values with pairs indicating the number of repetitions and the repeated value
Most efficient for data with extensive repetitive patterns
RLE is particularly efficient for data with long sequences of repeating values, but may not be the best choice for data with few repetitions
Linear time complexity of O(n)
RLE has a time complexity of O(n), making it efficient for large datasets
A specialized form of RLE designed for binary data, consisting of only two possible values: 0 and 1
Python's clear syntax and powerful standard libraries make it well-suited for implementing RLE
To encode data using RLE in Python, one typically iterates through the input, counting consecutive occurrences of each element and adding them to the result
Decompressing RLE data in Python involves reversing the encoding process and reproducing each value the specified number of times
RLE is highly effective for compressing long sequences of identical binary digits, making it beneficial for binary image compression
RLE plays a pivotal role in JPEG image compression, specifically in encoding sequences of zero coefficients
RLE is also used in bitmap graphics, medical imaging, thematic mapping, and fax transmission for its lossless and efficient compression capabilities
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RLE Basic Principle
Replaces sequences of repeated values with count and value.
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RLE Example Encoding
String 'AAABBBCC' becomes '3A3B2C'.
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RLE Inefficiency Scenario
Not suitable for data with few repetitions; may increase size.
