How to perform image compression using run-length encoding (RLE)

Run-length encoding (RLE) is a lossless compression technique where the original data can be perfectly reconstructed from the compressed data. It’s suitable for scenarios where data integrity must be preserved.

It is a data compression technique that simplifies data by replacing repeating occurrences of a unique symbol with an occurrence of that symbol along with its frequency. This method efficiently reduces the data size, especially where long runs of the same symbol are prevalent. By identifying and substituting these runs with symbol-frequency pairs, RLE transforms the data into a more compact representation, making it easier to store and transmit while retaining the essential information.

RLE is most effective when there are long series of identical pixels. Let’s look at the example below on how the shortest code is generated for the row with the same color.

For the uncompressed image, the binary values represent each pixel value, i.e., 0 for black and 1 for white. In the compressed image representation, the code format is the frequency followed by the pixel value.

Code

Let’s look at how we can perform RLE in Python.

The function rle_encode takes a binary image as input and iterates through each row of the image, calculating runs of consecutive pixels with the same value.

Pros and cons of using RLE

Let's explore some pros and cons of using RLE for image compression.

In image compression, RLE provides simplicity and efficiency for images with repetitive patterns, but it’s less effective for more complex images. The choice of compression method depends on the image’s characteristics and the trade-offs between compression ratio and computational complexity.

Test your knowledge

Answer the following questions to test your knowledge on RLE.