# Introduction to AI-driven Image Compression System AI-driven image compression systems are a revolutionary technology in the world of digital imaging. These systems are capable of automatically reducing the file size of digital photos and videos, without sacrificing visual quality. This can result in significant savings in storage and bandwidth costs, as well as improved user experience. At the heart of AI-driven image compression systems is a deep learning algorithm. By training a deep learning algorithm on a large dataset of images, the system is able to learn how to identify important features in an image and compress it accordingly. This allows the system to optimize the file size while preserving quality. The AI-driven image compression system also has the potential to improve the overall quality of images. By leveraging the power of deep learning, the system can understand the context of an image and apply appropriate image processing techniques to enhance the quality of the image. Overall, AI-driven image compression systems offer a powerful tool for digital imaging professionals and users alike. With the ability to reduce file sizes and improve quality with minimal effort, these systems are sure to have a positive impact on the digital imaging industry.

Algorithm: AI-driven Image Compression System 1. Load the input image: -Read the input image and store it in a variable. 2. Pre-process the image: -Apply denoising techniques to reduce any noise or artifacts in the image. -Apply sharpening techniques to improve the details in the image. 3. Apply AI techniques: -Use an AI-based algorithm (such as an autoencoder) to compress the image. -Train the AI-based algorithm on a dataset of similar images. 4. Output the compressed image: -Save the compressed image in the desired file format. 5. Evaluate the results: -Evaluate the quality of the compressed image by comparing it to the original image. Sample Code: # Load the input image input_image = cv2.imread('input_image.jpg') # Pre-process the image denoised_image = cv2.fastNlMeansDenoisingColored(input_image,None,10,10,7,21) sharpened_image = cv2.filter2D(denoised_image,-1,(1,1)) # Apply AI techniques autoencoder = Autoencoder() autoencoder.fit(sharpened_image) # Output the compressed image compressed_image = autoencoder.predict(sharpened_image) cv2.imwrite('compressed_image.jpg', compressed_image) # Evaluate the results # Compare the compressed image to the original image mse = mse(input_image, compressed_image) psnr = psnr(input_image, compressed_image) print('MSE: ', mse) print('PSNR: ', psnr)