Adventures in Machine Learning

Efficient Image Compression in Python with Pillow Library

Image Compression in Python: Efficient Storage and Transfer of Images

Images play a crucial role in our lives, from personal photographs to business applications such as graphic design and medical imaging. However, high-quality images require large amounts of storage space, which can be a challenge for individuals and organizations alike.

This is where image compression comes in – a process of reducing the redundant information in an image to optimize storage space and facilitate efficient transfer. In this article, we’ll explore how to implement image compression in Python using the Pillow library and examine the advantages and disadvantages of compression.

Importance of Image Compression

The importance of image compression lies in its ability to reduce redundancy in images. This compression technique is particularly useful for large files containing vast amounts of visual information.

By removing unnecessary data, we save significant storage space, making it possible to store more images on a device or transmit them over the internet more quickly. In addition, image compression is crucial for optimizing file transfer.

Images are often shared across the web, and large image files take a long time to upload or download, leading to delays and frustration. Compression can help significantly reduce upload and download times, making it a critical tool for users who need to transfer vast amounts of image data.

Using the Pillow Library for Image Compression

Python provides an extensive collection of libraries that can be used to develop various applications, including image processing. One such library is Pillow, which offers simple and powerful image processing capabilities.

The Pillow library comes pre-installed with many Python distributions, so we do not need to install it separately. To use Pillow, we must import it into our Python program using the import statement.

After that, we can easily load and manipulate image files with various options to modify their properties. Pillow supports various image file formats, such as JPEG, PNG, BMP, and many others.

Advantages and Disadvantages of Image Compression

One of the most significant advantages of image compression is that it reduces the size of image files, making them more efficient to store and transfer. It enables users to store and transmit more images in the same amount of space, which is especially useful when dealing with large numbers of images.

Additionally, compressed image files can be easily shared across the internet, email or other digital platforms. This reduces the time and cost associated with sending and receiving large image files, making it even more useful for users who frequently send or receive image files.

However, image compression also has some disadvantages. One of the downsides is the reduction in image quality.

A compressed image file loses some details and may look noticeably different from the original image file. It is essential to consider the required image quality for a given application before selecting an image compression method.

Another disadvantage of image compression is that it can lead to compatibility issues between different software and hardware platforms. It is essential to choose a compression format that can be used across different devices and applications, to ensure seamless compatibility.

Implementation of Image Compression with Python and Pillow

Now, let’s look at how we can implement image compression in Python using the Pillow library. The following are the steps to follow:


Import the necessary modules: First, we must import the required modules, which include Pillow and os. We can do this by adding the following code to the beginning of our Python script:

from PIL import Image

import os

2. Define the image compression function: Next, we will create a function that takes the image properties such as dimensions, resolution, and image file path as input and compresses the image with the desired compression level.

Below is an example function to achieve this:

def compress_image(image_path, compressed_path, compression_level):


with as image:, “JPEG”, optimize=True, quality=compression_level)

return True

except IOError:

return False

In this function, we use the method to open the input image file, save it in JPEG format with compression level defined by the quality parameter. The “optimize” parameter is used to help optimize JPEG compression, which makes its file size smaller.

3. Create the Driver code: Finally, we can create the driver code that invokes the image compression function and reduces the file size.

The code snippet is as follows:

if __name__ == ‘__main__’:

# Sample input values

image_path = “/path/to/image.jpg”

compressed_path = “/path/to/compressed_image.jpg”

compression_level = 70

# Call the image compression function

compress_success = compress_image(image_path, compressed_path, compression_level)

# Check if compression was successful

if compress_success:

original_size = os.path.getsize(image_path)

compressed_size = os.path.getsize(compressed_path)

# Print original and compressed file sizes

print(“Original file size: “, original_size / (1024 * 1024), “MB”)

print(“Compressed file size: “, compressed_size / (1024 * 1024), “MB”)

print(“File size reduction: “,

(1 – compressed_size / original_size) * 100, “%”)

This code snippet defines a sample image file path, compressed image file path and the desired compression level. It then calls the compression function to compress this file and measure the resulting file size reduction.


In conclusion, image compression is an essential technique that can optimize storage space and file transfer efficiency for various types of image files. Python is a versatile programming language that provides ample resources for image processing, and we can use the Pillow library to implement image compression and other related tasks.

However, it’s crucial to consider the consequences of image compression, including loss of image quality and compatibility issues when selecting a compression method. With these caveats in mind, we can use Python and Pillow to develop robust image compression solutions that enhance our digital experience.

When it comes to image manipulation with Python, one of the most popular and versatile modules out there is PIL, or the Python Imaging Library. PIL provides a range of features and functions that make it easy to modify and process all kinds of image files, from photographs to graphics and much more.

Importance of PIL in Image Manipulation with Python

One of the primary benefits of PIL is the wide range of image processing and manipulation tools it offers. This module provides access to a range of image file formats, including JPEG, PNG, BMP, TIFF, and many others.

It also offers extensive functionality for manipulating images, such as resizing, cropping, rotating, filtering, and much more. Furthermore, PIL is an open-source module, meaning it is constantly being updated and improved by the active developer community.

This provides users with easy access to new and improved tools and functions, ensuring that PIL remains a relevant and powerful tool in image manipulation with Python.

Common Uses of PIL

There are several common, and useful, applications for PIL in image manipulation within Python. Three of these applications are image size reduction, image format conversion, and image compression.

1. Image Size Reduction

One of the most common tasks in image manipulation is reducing the size of images.

Large image files can take up significant amounts of storage space, both on your devices and when being transferred online. Resizing images to a more manageable size can help save storage space and reduce the time it takes to transfer images online.

Luckily, PIL provides an easy method for resizing images with its “resize” function. This function can reduce an image’s size by specifying a new height and width, or by providing a scaling factor for the original image’s size.

Below is an example of how to use the resize function to reduce an image’s size by half:


from PIL import Image

# Load the image

img =“sample_image.jpg”)

# Resize the image to half its original size

width, height = img.size

new_width, new_height = int(width / 2), int(height / 2)

resized_img = img.resize((new_width, new_height))

# Save the resized image“resized_image.jpg”)


2. Image Format Conversion

Another critical application of PIL in image manipulation is image format conversion.

Different image file formats serve different purposes, and converting an image from one file format to another is often necessary, especially when transferring images between different platforms or editing software. PIL makes it easy to convert image files between different formats using its “convert” function.

This function can convert an input image file into another format, which is specified by the output file’s extension. Below is an example of how to use the convert function to convert an image file from PNG to JPEG:


from PIL import Image

# Load the PNG image

png_image =“sample_image.png”)

# Convert to JPEG format

png_image.convert(“RGB”).save(“converted_image.jpg”, “JPEG”)


3. Image Compression

Finally, image compression is a core application of PIL in image manipulation.

As discussed earlier, image compression helps reduce file size for images, making it easier to store and transfer them quickly. Fortunately, PIL provides several compression options that developers can choose from based on the desired image quality and file size reduction.

One such option is the “save” function, which compresses images using the JPEG format. This function has compression level parameters, which determines the quality of the compressed image.

A lower quality means a smaller file size, but it may also result in a more blurred image. Below is an example of how to compress an image file using PIL in python:


from PIL import Image

# Load the image

img =“sample_image.jpg”)

# Compress the image to reduce file size“compressed_image.jpg”, format=”JPEG”, quality=50)


In conclusion, PIL provides an essential set of image processing and manipulation tools that make complex image editing and processing tasks more manageable with Python. The module’s range of functions and features make it an ideal choice for a wide range of applications, from resizing images to converting formats or compressing files.

PILs open-source nature ensures that it remains a preferred choice for developers who need a robust and reliable image manipulation tool. In conclusion, the article highlights the significance of image compression in reducing redundancy, optimizing storage space, and facilitating efficient file transfer.

Python, through modules such as the Python Imaging Library (PIL), is a powerful tool that provides image manipulation functions such as image size reduction, format conversion, and image compression. PIL’s versatility as an open-source module allows developers to access the functionalities it offers continually.

The article emphasizes the importance of assessing the desired image quality and compatibility when choosing a compression method. Overall, taking advantage of Python and PIL features can enhance image processing while significantly reducing the storage space and increasing the efficiency of transferring image files.

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