If the characters are the same, the count is incremented to 1''' if (message = message):īreak '''the count and the character is concatenated to the encoded string'''Įncoded_string = encoded_string + str(count) + ch '''if the character at the current index is the same as the character at the next index.
HOW TO ENCODE A MESSAGE IN A PICTURE WITH PYTHON CODE
ImplementationĬreate a new notebook and add the following code to the first cell. The lossy compression process is non-reversible, unlike the one for lossless compression algorithms.Įxamples include Lossy predictive coding, Block transform coding, and Vector quantization. Here, we achieve high compression ratios, hence greater size reduction. We use lossy algorithms where quality could be compromised. You can read more about Huffman coding here. ( Size before compression/Size after compression). These processes are reversible, and they’ve very low compression ratios since we don’t lose any information.Įxamples include Run Length Encoding (RLE), Huffman coding, Arithmetic coding, Shannon-Fanno coding, etc.Ĭompression ratio - We get this ratio by dividing the size before compression and size after compression. We try to avoid the loss of image quality. Lossless algorithms are used when information quality is very important. There two main classification types for compression algorithms are: 1. We also notice that compression in video streaming sites, where videos are loaded in low quality during poor internet connectivity.For a few apps, we can set the quality of media to be downloaded. In social media apps like WhatsApp, we notice that the image received is of lower quality and consumes much lesser space.This enables the file to be sent faster, and it reduces overhead traffic for transmitting the data. In a situation where one wants to send a picture to his/her friend, compression will be done at the source device and decompressed at the destination device.This is a process where a file size is reduced using algorithms resulting in a file that uses fewer storage bits than the original file. This is because short runs may end up taking the same space or more as we see in the second example. ( 21 characters).įrom these examples, we see that RLE is suitable for compressing large amounts of data with a few runs e.g., image pixel information. For the text AAAAHHHEEM, HAHA., it will be encoded as 4A3H2E1M1,1 1H1A1H1A1.For the text AAAAAAAAAAAAAHHHEEM ( 19 characters), RLE will encode it to 13A3H2EM (7 characters).Normal Python files could be used as well.īefore we understand RLE, let’s have a look at few examples: Familiarity with a notebook-based interface like Google Colab.To follow along with this tutorial, the reader should have: In this article, we will learn more about Compression algorithms, dive deep into implementing RLE algorithm and understand its performance. It does so by storing the number of these runs followed by the data. It compresses data by reducing repetitive, and consecutive data called runs. Run Length Encoding is a lossless data compression algorithm.
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